Search results for: papyri images

Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Thomas Louis, Irina Primac, Florent Morfoisse, Tania Durre, Silvia Blacher, Agnes Noel

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In vitro and in vivo lymphangiogenesis assays are essential for the identification of potential lymphangiogenic agents and the screening of pharmacological inhibitors. In the present study, we analyse three biological models: in vitro lymphatic endothelial cell spheroids, in vivo ear sponge assay, and in vivo lymph node colonisation by tumour cells. These assays provide suitable 3D models to test pro- and anti-lymphangiogenic factors or drugs. 3D images were acquired by confocal laser scanning and light sheet fluorescence microscopy. Virtual scan microscopy followed by 3D reconstruction by image aligning methods was also used to obtain 3D images of whole large sponge and ganglion samples. 3D reconstruction, image segmentation, skeletonisation, and other image processing algorithms are described. Fixed and time-lapse imaging techniques are used to analyse lymphatic endothelial cell spheroids behaviour. The study of cell spatial distribution in spheroid models enables to detect interactions between cells and to identify invasion hierarchy and guidance patterns. Global measurements such as volume, length, and density of lymphatic vessels are measured in both in vivo models. Branching density and tortuosity evaluation are also proposed to determine structure complexity. Those properties combined with vessel spatial distribution are evaluated in order to determine lymphangiogenesis extent. Lymphatic endothelial cell invasion and lymphangiogenesis were evaluated under various experimental conditions. The comparison of these conditions enables to identify lymphangiogenic agents and to better comprehend their roles in the lymphangiogenesis process. The proposed methodology is validated by its application on the three presented models.

Keywords: 3D image segmentation, 3D image skeletonisation, cell invasion, confocal microscopy, ear sponges, light sheet microscopy, lymph nodes, lymphangiogenesis, spheroids

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: S. Jayalakshmi, Gayathri S., Subiksa V., Nithyasri P., Agasthiya

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Paleochannels are the buried part of an active river system which was separated from the active river channel by the process of cutoff or abandonment during the dynamic evolution of the active river. Over time, they are filled by young unconsolidated or semi-consolidated sediments. Additionally, it is impacted by geo morphological influences, lineament alterations, and other factors. The primary goal of this study is to identify the paleochannels in Periyar river basin for the year 2023. Those channels has a high probability in the presence of natural resources, including gold, platinum,tin,an duranium. Numerous techniques are used to map the paleochannel. Using the optical data, Satellite images were collected from various sources, which comprises multispectral satellite images from which indices such as Normalized Difference Vegetation Index (NDVI),Normalized Difference Water Index (NDWI), Soil Adjusted Vegetative Index (SAVI) and thematic layers such as Lithology, Stream Network, Lineament were prepared. Weights are assigned to each layer based on its importance, and overlay analysis has done, which concluded that the northwest region of the area has shown some paleochannel patterns. The results were cross-verified using the results obtained using microwave data. Using Sentinel data, Synthetic Aperture Radar (SAR) Image was extracted from European Space Agency (ESA) portal, pre-processed it using SNAP 6.0. In addition to that, Polarimetric decomposition technique has incorporated to detect the paleochannels based on its scattering property. Further, Principal component analysis has done for enhanced output imagery. Results obtained from optical and microwave radar data were compared and the location of paleochannels were detected. It resulted six paleochannels in the study area out of which three paleochannels were validated with the existing data published by Department of Geology and Environmental Science, Kerala. The other three paleochannels were newly detected with the help of SAR image.

Keywords: paleochannels, optical data, SAR image, SNAP

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Authors: Carolina Morales, Ricardo Castro-Huerta, Enrique A. Mundaca

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The edaphic fauna is the main factor involved in the transformation of nutrients and soil decomposition processes. Edaphic organisms are highly sensitive to soil disturbances, which normally causes changes in the composition and abundance of such organisms. Fire is known to be a disturbing factor since it affects the physical, chemical and biological properties of the soil and the whole ecosystem. During the summer (December-March) of 2017, Chile suffered the major fire events recorded in its modern history, which affected a vast area and a number of ecosystem types. The objective of this study was first to use remote sensing satellite images and GIS (Geographic Information Systems) to assess and identify levels of fire severity in disturbed areas and to compare the responses of the soil mesofauna diversity among such areas. We identified four areas (treatments) with an ascending level of severity, namely: mild, medium, high severity, and free of fire. A non-affected patch of forest was established as a control. Three samples from each treatment were collected in the form of a soil cube (10x10x10 cm). Edaphic mesofauna was obtained from each sample through the Berlese-Tullgren funnel method. Collected specimens were quantified and identified, using the RTU (Recognisable Taxonomic Unit) criterion. Diversity was analysed using inferential statistics to compare Simpson and Shannon-Wiener indexes across treatments. As predicted, the unburned forest patch (control) exhibited higher diversity values than the treatments. Significantly higher diversity values were recorded in those treatments subjected to lower fire severity. We conclude that remote sensing zoning is an adequate tool to identify different levels of fire severity and that an edaphic mesofauna is a group of organisms that qualify as good bioindicators for monitoring soil recovery after fire events.

Keywords: bioindicator, Chile, fire severity level, soil

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Authors: Kuo-Cheng Liu

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In the perceptual image coding, the objective is to shape the coding distortion such that the amplitude of distortion does not exceed the error visibility threshold, or to remove perceptually redundant signals from the image. While most researches focus on color image coding, the perceptual-based quantizer developed for luminance signals are always directly applied to chrominance signals such that the color image compression methods are inefficient. In this paper, the internal generative mechanism is integrated into the design of a color image compression method. The internal generative mechanism working model based on the structure-based spatial masking is used to assess the subjective distortion visibility thresholds that are visually consistent to human eyes better. The estimation method of structure-based distortion visibility thresholds for color components is further presented in a locally adaptive way to design quantization process in the wavelet color image compression scheme. Since the lowest subband coefficient matrix of images in the wavelet domain preserves the local property of images in the spatial domain, the error visibility threshold inherent in each coefficient of the lowest subband for each color component is estimated by using the proposed spatial error visibility threshold assessment. The threshold inherent in each coefficient of other subbands for each color component is then estimated in a local adaptive fashion based on the distortion energy allocation. By considering that the error visibility thresholds are estimated using predicting and reconstructed signals of the color image, the coding scheme incorporated with locally adaptive perceptual color quantizer does not require side information. Experimental results show that the entropies of three color components obtained by using proposed IGM-based color image compression scheme are lower than that obtained by using the existing color image compression method at perceptually lossless visual quality.

Keywords: internal generative mechanism, structure-based spatial masking, visibility threshold, wavelet domain

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Authors: Faizan Tariq, Moayid Ali Zaidi

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Facial emotion recognition algorithms are expanding rapidly now a day. People are using different algorithms with different combinations to generate best results. There are six basic emotions which are being studied in this area. Author tried to recognize the facial expressions using object detector algorithms instead of traditional algorithms. Two object detection algorithms were chosen which are Faster R-CNN and YOLO. For pre-processing we used image rotation and batch normalization. The dataset I have chosen for the experiments is Static Facial Expression in Wild (SFEW). Our approach worked well but there is still a lot of room to improve it, which will be a future direction.

Keywords: face recognition, emotion recognition, deep learning, CNN

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Authors: Sh. Sohrabnezhad, A. Jafarzadeh

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In this paper, MCM-41 mesoporous material nanofibers were synthesized by an electrospinning technique. The nanofibers were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and nitrogen adsorption–desorption measurement. Tetraethyl orthosilicate (TEOS) and polyvinyl alcohol (PVA) were used as a silica source and fiber forming source, respectively. TEM and SEM images showed synthesis of MCM-41 nanofibers with a diameter of 200 nm. The pore diameter and surface area of calcined MCM-41 nanofibers was 2.2 nm and 970 m²/g, respectively. The morphology of the MCM-41 nanofibers depended on spinning voltages.

Keywords: electrospinning, electron microscopy, fiber technology, porous materials, X-ray techniques

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Authors: Panatda Intanin, Sangutid Thongsawad, Chirapha Tannanonta, Todsaporn Fuangrod

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Stereotactic radiosurgery (SRS) is a highly precision delivery technique that requires comprehensive quality assurance (QA) tests prior to treatment delivery. An isocenter of delivery beam plays a critical role that affect the treatment accuracy. The uncertainty of isocenter is traditionally accessed using circular cone equipment, Winston-Lutz (WL) phantom and film. This technique is considered time consuming and highly dependent on the observer. In this work, the development of multileaf collimator (MLC)-based isocenter verification tool using electronic portal imaging device (EPID) was proposed and evaluated. A mechanical isocenter alignment with ball bearing diameter 5 mm and circular cone diameter 10 mm fixed to gantry head defines the radiation field was set as the conventional WL test method. The conventional setup was to compare to the proposed setup; using MLC (10 x 10 mm) to define the radiation filed instead of cone. This represents more realistic delivery field than using circular cone equipment. The acquisition from electronic portal imaging device (EPID) and radiographic film were performed in both experiments. The gantry angles were set as following: 0°, 90°, 180° and 270°. A software tool was in-house developed using MATLAB/SIMULINK programming to determine the centroid of radiation field and shadow of WL phantom automatically. This presents higher accuracy than manual measurement. The deviation between centroid of both cone-based and MLC-based WL tests were quantified. To compare between film and EPID image, the deviation for all gantry angle was 0.26±0.19mm and 0.43±0.30 for cone-based and MLC-based WL tests. For the absolute deviation calculation on EPID images between cone and MLC-based WL test was 0.59±0.28 mm and the absolute deviation on film images was 0.14±0.13 mm. Therefore, the MLC-based isocenter verification using EPID present high sensitivity tool for SRS QA.

Keywords: isocenter verification, quality assurance, EPID, SRS

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Authors: Wanderson Goncalves e Goncalves, José Alberto Silva de Sá

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Human actions are a threat to the balance and conservation of the Amazon forest. Therefore the environmental monitoring services play an important role as the preservation and maintenance of this environment. This study classified forest types using data from a forest inventory provided by the 'Florestal e da Biodiversidade do Estado do Pará' (IDEFLOR-BIO), located between the municipalities of Santarém, Juruti and Aveiro, in the state of Pará, Brazil, covering an area approximately of 600,000 hectares, Bands 3, 4 and 5 of the TM-Landsat satellite image, and Self - Organizing Maps. The information from the satellite images was extracted using QGIS software 2.8.1 Wien and was used as a database for training the neural network. The midpoints of each sample of forest inventory have been linked to images. Later the Digital Numbers of the pixels have been extracted, composing the database that fed the training process and testing of the classifier. The neural network was trained to classify two forest types: Rain Forest of Lowland Emerging Canopy (Dbe) and Rain Forest of Lowland Emerging Canopy plus Open with palm trees (Dbe + Abp) in the Mamuru Arapiuns glebes of Pará State, and the number of examples in the training data set was 400, 200 examples for each class (Dbe and Dbe + Abp), and the size of the test data set was 100, with 50 examples for each class (Dbe and Dbe + Abp). Therefore, total mass of data consisted of 500 examples. The classifier was compiled in Orange Data Mining 2.7 Software and was evaluated in terms of the confusion matrix indicators. The results of the classifier were considered satisfactory, and being obtained values of the global accuracy equal to 89% and Kappa coefficient equal to 78% and F1 score equal to 0,88. It evaluated also the efficiency of the classifier by the ROC plot (receiver operating characteristics), obtaining results close to ideal ratings, showing it to be a very good classifier, and demonstrating the potential of this methodology to provide ecosystem services, particularly in anthropogenic areas in the Amazon.

Keywords: artificial neural network, computational intelligence, pattern recognition, unsupervised learning

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Authors: Ng Wai Ming

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The Yijing (Classic of Changes) is one of the most influential Chinese classics, and its text, images and divination have been widely studied and used by different people in the world from past to present. Its impact in Asia has been particularly strong due to cultural and geographical proximity. Based on many years of textual study of the history of the Yijing in the Sinosphere, the author attempts to identify various levels of acceptance and localization of the Yijing in different Asian regions, including Japan, Korea, the Ryukyu Kingdom, Vietnam, Mongolia and Tibet. It will create a new concept of “Yijing cultural sphere” to explain the popularization and indigenization of the Yijing in Asia.

Keywords: classic of changes, asia, sinosphere, localization

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

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Authors: Elmira Solati, Atousa Mehrani, Davoud Dorranian

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Generation of ZnO-Au nanocomposite under laser irradiation of a mixture of the ZnO and Au colloidal suspensions are experimentally investigated. In this work, firstly ZnO and Au nanoparticles are prepared by pulsed laser ablation of the corresponding metals in water using the 1064 nm wavelength of Nd:YAG laser. In a second step, the produced ZnO and Au colloidal suspensions were mixed in different volumetric ratio and irradiated using the second harmonic of a Nd:YAG laser operating at 532 nm wavelength. The changes in the size of the nanostructure and optical properties of the ZnO-Au nanocomposite are studied as a function of the volumetric ratio of ZnO and Au colloidal suspensions. The crystalline structure of the ZnO-Au nanocomposites was analyzed by X-ray diffraction (XRD). The optical properties of the samples were examined at room temperature by a UV-Vis-NIR absorption spectrophotometer. Transmission electron microscopy (TEM) was done by placing a drop of the concentrated suspension on a carbon-coated copper grid. To further confirm the morphology of ZnO-Au nanocomposites, we performed Scanning electron microscopy (SEM) analysis. Room temperature photoluminescence (PL) of the ZnO-Au nanocomposites was measured to characterize the luminescence properties of the ZnO-Au nanocomposites. The ZnO-Au nanocomposites were characterized by Fourier transform infrared (FTIR) spectroscopy. The X-ray diffraction pattern shows that the ZnO-Au nanocomposites had the polycrystalline structure of Au. The behavior observed by images of transmission electron microscope reveals that soldering of Au and ZnO nanoparticles include their adhesion. The plasmon peak in ZnO-Au nanocomposites was red-shifted and broadened in comparison with pure Au nanoparticles. By using the Tauc’s equation, the band gap energy for ZnO-Au nanocomposites is calculated to be 3.15–3.27 eV. In this work, the formation of ZnO-Au nanocomposites shifts the FTIR peak of metal oxide bands to higher wavenumbers. PL spectra of the ZnO-Au nanocomposites show that several weak peaks in the ultraviolet region and several relatively strong peaks in the visible region. SEM image indicates that the morphology of ZnO-Au nanocomposites produced in water was spherical. The TEM images of ZnO-Au nanocomposites demonstrate that with increasing the volumetric ratio of Au colloidal suspension the adhesion increased. According to the size distribution graphs of ZnO-Au nanocomposites with increasing the volumetric ratio of Au colloidal suspension the amount of ZnO-Au nanocomposites with the smaller size is further.

Keywords: Au nanoparticles, pulsed laser ablation, ZnO-Au nanocomposites, ZnO nanoparticles

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Authors: Jing Zhang, Yvonne Reinwald, Nick Poulson, Alicia El Haj, Chung See, Mike Somekh, Melissa Mather

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Biomedical imaging of native tissue using light offers the potential to obtain excellent structural and functional information in a non-invasive manner with good temporal resolution. Image contrast can be derived from intrinsic absorption, fluorescence, or scatter, or through the use of extrinsic contrast. A major challenge in applying optical microscopy to in vivo tissue imaging is the effects of light attenuation which limits light penetration depth and achievable imaging resolution. Recently Selective Plane Illumination Microscopy (SPIM) has been used to map the 3D distribution of fluorophores dispersed in biological structures. In this approach, a focused sheet of light is used to illuminate the sample from the side to excite fluorophores within the sample of interest. Images are formed based on detection of fluorescence emission orthogonal to the illumination axis. By scanning the sample along the detection axis and acquiring a stack of images, 3D volumes can be obtained. The combination of rapid image acquisition speeds with the low photon dose to samples optical sectioning provides SPIM is an attractive approach for imaging biological samples in 3D. To date all implementations of SPIM rely on the use of fluorescence reporters be that endogenous or exogenous. This approach has the disadvantage that in the case of exogenous probes the specimens are altered from their native stage rendering them unsuitable for in vivo studies and in general fluorescence emission is weak and transient. Here we present for the first time to our knowledge a label-free implementation of SPIM that has downstream applications in the clinical setting. The experimental set up used in this work incorporates both label-free and fluorescent illumination arms in addition to a high specification camera that can be partitioned for simultaneous imaging of both fluorescent emission and scattered light from intrinsic sources of optical contrast in the sample being studied. This work first involved calibration of the imaging system and validation of the label-free method with well characterised fluorescent microbeads embedded in agarose gel. 3D constructs of mammalian cells cultured in agarose gel with varying cell concentrations were then imaged. A time course study to track cell proliferation in the 3D construct was also carried out and finally a native tissue sample was imaged. For each sample multiple images were obtained by scanning the sample along the axis of detection and 3D maps reconstructed. The results obtained validated label-free SPIM as a viable approach for imaging cells in a 3D gel construct and native tissue. This technique has the potential use in a near-patient environment that can provide results quickly and be implemented in an easy to use manner to provide more information with improved spatial resolution and depth penetration than current approaches.

Keywords: bioimaging, optics, selective plane illumination microscopy, tissue imaging

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Authors: Zhao Gao, Eran Edirisinghe

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The production of an accurate sketch of a suspect based on a verbal description obtained from a witness is an essential task for most criminal investigations. The criminal investigation system employs specifically trained professional artists to manually draw a facial image of the suspect according to the descriptions of an eyewitness for subsequent identification. Within the advancement of Deep Learning, Recurrent Neural Networks (RNN) have shown great promise in Natural Language Processing (NLP) tasks. Additionally, Generative Adversarial Networks (GAN) have also proven to be very effective in image generation. In this study, a trained GAN conditioned on textual features such as keywords automatically encoded from a verbal description of a human face using an RNN is used to generate photo-realistic facial images for criminal investigations. The intention of the proposed system is to map corresponding features into text generated from verbal descriptions. With this, it becomes possible to generate many reasonably accurate alternatives to which the witness can use to hopefully identify a suspect from. This reduces subjectivity in decision making both by the eyewitness and the artist while giving an opportunity for the witness to evaluate and reconsider decisions. Furthermore, the proposed approach benefits law enforcement agencies by reducing the time taken to physically draw each potential sketch, thus increasing response times and mitigating potentially malicious human intervention. With publically available 'CelebFaces Attributes Dataset' (CelebA) and additionally providing verbal description as training data, the proposed architecture is able to effectively produce facial structures from given text. Word Embeddings are learnt by applying the RNN architecture in order to perform semantic parsing, the output of which is fed into the GAN for synthesizing photo-realistic images. Rather than the grid search method, a metaheuristic search based on genetic algorithms is applied to evolve the network with the intent of achieving optimal hyperparameters in a fraction the time of a typical brute force approach. With the exception of the ‘CelebA’ training database, further novel test cases are supplied to the network for evaluation. Witness reports detailing criminals from Interpol or other law enforcement agencies are sampled on the network. Using the descriptions provided, samples are generated and compared with the ground truth images of a criminal in order to calculate the similarities. Two factors are used for performance evaluation: The Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). A high percentile output from this performance matrix should attribute to demonstrating the accuracy, in hope of proving that the proposed approach can be an effective tool for law enforcement agencies. The proposed approach to criminal facial image generation has potential to increase the ratio of criminal cases that can be ultimately resolved using eyewitness information gathering.

Keywords: RNN, GAN, NLP, facial composition, criminal investigation

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Authors: Yu Zhang, Kohei Inoue, Kiichi Urahama

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We propose a simplified bilateral filter with binarized coefficients for accelerating it. Its computational cost is further decreased by sampling pixels. This computationally low cost filter is useful for smoothing or denoising images by using mobile devices with limited computational power.

Keywords: bilateral filter, binarized-weight bilateral filter, image smoothing, image denoising, pixel sampling

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Authors: Huailei Wen

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The Kramers vs. Kramers (1979) is a film that comprehensively examines the role and status of women under the traditional secular vision, where women have become subordinate to the patriarchal society and family. Through the construction of the protagonist Joanna's dissatisfaction with the social and ethical status quo, her struggle to subvert the existing status of women, and her return to her own self, the story comprehensively reflects the difficult journey of women, represented by Joanna, to subvert the stereotypes and return to their own selves in the specific historical context of the time, revealing the self-value of Joanna's phenomenon to modern women.

Keywords: star image, feminism, Kramers vs. Kramers, Hollywood

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Authors: Shende Suraj Balu, A. B. Deoghare, K. M. Pandey

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The human middle ear (ME) is a delicate and vital organ. It has a complex structure that performs various functions such as receiving sound pressure and producing vibrations of eardrum and propagating it to inner ear. It consists of Tympanic Membrane (TM), three auditory ossicles, various ligament structures and muscles. Incidents such as traumata, infections, ossification of ossicular structures and other pathologies may damage the ME organs. The conditions can be surgically treated by employing prosthesis. However, the suitability of the prosthesis needs to be examined in advance prior to the surgery. Few decades ago, this issue was addressed and analyzed by developing an equivalent representation either in the form of spring mass system, electrical system using R-L-C circuit or developing an approximated CAD model. But, nowadays a three-dimensional ME model can be constructed using micro X-Ray Computed Tomography (μCT) scan data. Moreover, the concern about patient specific integrity pertaining to the disease can be examined well in advance. The current research work emphasizes to develop the ME model from the stacks of μCT images which are used as input file to MIMICS Research 19.0 (Materialise Interactive Medical Image Control System) software. A stack of CT images is converted into geometrical surface model to build accurate morphology of ME. The work is further extended to understand the dynamic behaviour of Harmonic response of the stapes footplate and umbo for different sound pressure levels applied at lateral side of eardrum using finite element approach. The pathological condition Cholesteatoma of ME is investigated to obtain peak to peak displacement of stapes footplate and umbo. Apart from this condition, other pathologies, mainly, changes in the stiffness of stapedial ligament, TM thickness and ossicular chain separation and fixation are also explored. The developed model of ME for pathologies is validated by comparing the results available in the literatures and also with the results of a normal ME to calculate the percentage loss in hearing capability.

Keywords: computed tomography (μCT), human middle ear (ME), harmonic response, pathologies, tympanic membrane (TM)

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Authors: Maria A. Castillo H., Andrés R. Leandro C.

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During the last decades, agricultural productivity in Latin America has increased with precision agriculture and more efficient agricultural technologies. The use of automated systems, satellite images, geographic information systems, and tools for data analysis, and artificial intelligence have contributed to making more effective strategic decisions. Twenty years ago, the state of Mérida, located in the Venezuelan Andes, reported the largest area covered by greenhouses in the country, where certified seeds of potatoes, vegetables, ornamentals, and flowers were produced for export and consumption in the central region of the country. In recent years, it is estimated that production under greenhouses has changed, and the area covered has decreased due to different factors, but there are few historical statistical data in sufficient quantity and quality to support this estimate or to be used for analysis and decision making. The objective of this study is to compare data collected about geoposition, use, and covered areas of the greenhouses in 2007 to data available in 2021, as support for the analysis of the current situation of horticultural production in the main municipalities of the state of Mérida. The document presents the development of the work in the diagnosis and integration of geographic coordinates in GIS and data analysis phases. As a result, an evaluation of the process is made, a dashboard is presented with the most relevant data along with the geographical coordinates integrated into GIS, and an analysis of the obtained information is made. Finally, some recommendations for actions are added, and works that expand the information obtained and its geographical traceability over time are proposed. This study contributes to granting greater certainty in the supporting data for the evaluation of social, environmental, and economic sustainability indicators and to make better decisions according to the sustainable development goals in the area under review. At the same time, the methodology provides improvements to the agricultural data collection process that can be extended to other study areas and crops.

Keywords: greenhouses, geographic information system, protected agriculture, data analysis, Venezuela

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Authors: Tridipa Biswas, Kamal Pandey

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A Digital Elevation Model (DEM) is a simple representation of a surface in 3 dimensional space with elevation as the third dimension along with X (horizontal coordinates) and Y (vertical coordinates) in rectangular coordinates. DEM has wide applications in various fields like disaster management, hydrology and watershed management, geomorphology, urban development, map creation and resource management etc. Cartosat-1 or IRS P5 (Indian Remote Sensing Satellite) is a state-of-the-art remote sensing satellite built by ISRO (May 5, 2005) which is mainly intended for cartographic applications.Cartosat-1 is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution. One camera is looking at +26 degrees forward while another looks at –5 degrees backward to acquire stereoscopic imagery with base to height ratio of 0.62. The time difference between acquiring of the stereopair images is approximately 52 seconds. The high resolution stereo data have great potential to produce high-quality DEM. The high-resolution Cartosat-1 stereo image data is expected to have significant impact in topographic mapping and watershed applications. The objective of the present study is to generate high-resolution DEM, quality evaluation in different elevation strata, generation of ortho-rectified image and associated accuracy assessment from CARTOSAT-1 data based Ground Control Points (GCPs) for Aglar watershed (Tehri-Garhwal and Dehradun district, Uttarakhand, India). The present study reveals that generated DEMs (10m and 30m) derived from the CARTOSAT-1 stereo pair is much better and accurate when compared with existing DEMs (ASTER and CARTO DEM) also for different terrain parameters like slope, aspect, drainage, watershed boundaries etc., which are derived from the generated DEMs, have better accuracy and results when compared with the other two (ASTER and CARTO) DEMs derived terrain parameters.

Keywords: ASTER-DEM, CARTO-DEM, CARTOSAT-1, digital elevation model (DEM), ortho-rectified image, photogrammetry, RPC, stereo pair, terrain parameters

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: Kaustav Mukherjee

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In this paper we propose a Neural Style Transfer solution whereby we have created a Lightweight Separable Convolution Kernel Based GAN Architecture (SC-GAN) which will very useful for designing filter for Mobile Phone Cameras and also Edge Devices which will convert any image to its 2D ANIMATED COMIC STYLE Movies like HEMAN, SUPERMAN, JUNGLE-BOOK. This will help the 2D animation artist by relieving to create new characters from real life person's images without having to go for endless hours of manual labour drawing each and every pose of a cartoon. It can even be used to create scenes from real life images.This will reduce a huge amount of turn around time to make 2D animated movies and decrease cost in terms of manpower and time. In addition to that being extreme light-weight it can be used as camera filters capable of taking Comic Style Shots using mobile phone camera or edge device cameras like Raspberry Pi 4,NVIDIA Jetson NANO etc. Existing Methods like CartoonGAN with the model size close to 170 MB is too heavy weight for mobile phones and edge devices due to their scarcity in resources. Compared to the current state of the art our proposed method which has a total model size of 31 MB which clearly makes it ideal and ultra-efficient for designing of camera filters on low resource devices like mobile phones, tablets and edge devices running OS or RTOS. .Owing to use of high resolution input and usage of bigger convolution kernel size it produces richer resolution Comic-Style Pictures implementation with 6 times lesser number of parameters and with just 25 extra epoch trained on a dataset of less than 1000 which breaks the myth that all GAN need mammoth amount of data. Our network reduces the density of the Gan architecture by using Depthwise Separable Convolution which does the convolution operation on each of the RGB channels separately then we use a Point-Wise Convolution to bring back the network into required channel number using 1 by 1 kernel.This reduces the number of parameters substantially and makes it extreme light-weight and suitable for mobile phones and edge devices. The architecture mentioned in the present paper make use of Parameterised Batch Normalization Goodfellow etc al. (Deep Learning OPTIMIZATION FOR TRAINING DEEP MODELS page 320) which makes the network to use the advantage of Batch Norm for easier training while maintaining the non-linear feature capture by inducing the learnable parameters

Keywords: comic stylisation from camera image using GAN, creating 2D animated movie style custom stickers from images, depth-wise separable convolutional neural network for light-weight GAN architecture for EDGE devices, GAN architecture for 2D animated cartoonizing neural style, neural style transfer for edge, model distilation, perceptual loss

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Authors: Mahdieh Khanmohammadi, Sune Darkner, Nicoletta Nava, Jens Randel Nyengaard, Jon Sporring

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We study the effect of stress on nervous system and we use two experimental groups of rats: sham rats and rats subjected to acute foot-shock stress. We investigate the synaptic vesicles density as a function of distance to the active zone in serial section transmission electron microscope images in 2 and 3 dimensions. By estimating the density in 2D and 3D we compare two groups of rats.

Keywords: stress, 3-dimensional synaptic vesicle density, image registration, bioinformatics

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Authors: Luca Indovina, Carmela Coppola, Carlo Altucci, Riccardo Barberi, Rocco Romano

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In this paper a real court case, held in Italy at the Court of Nola, in which a correct physical description, conducted with both a Monte Carlo and biophysical analysis, would have been sufficient to arrive at conclusions confirmed by documentary evidence, is considered. This will be an example of how forensic physics can be useful in confirming documentary evidence in order to reach hardly questionable conclusions. This was a libel trial in which the defendant, Mr. DS (Defendant for Slander), had falsely accused one of his neighbors, Mr. OP (Offended Person), of having caused him some damages. The damages would have been caused by an external plaster piece that would have detached from the neighbor’s property and would have hit Mr DS while he was in his garden, much more than a meter far away from the facade of the building from which the plaster piece would have detached. In the trial, Mr. DS claimed to have suffered a scratch on his forehead, but he never showed the plaster that had hit him, nor was able to tell from where the plaster would have arrived. Furthermore, Mr. DS presented a medical certificate with a diagnosis of contusion of the cerebral cortex. On the contrary, the images of Mr. OP’s security cameras do not show any movement in the garden of Mr. DS in a long interval of time (about 2 hours) around the time of the alleged accident, nor do they show any people entering or coming out from the house of Mr. DS in the same interval of time. Biophysical analysis shows that both the diagnosis of the medical certificate and the wound declared by the defendant, already in conflict with each other, are not compatible with the fall of external plaster pieces too small to be found. The wind was at a level 1 of the Beaufort scale, that is, unable to raise even dust (level 4 of the Beaufort scale). Therefore, the motion of the plaster pieces can be described as a projectile motion, whereas collisions with the building cornice can be treated using Newtons law of coefficients of restitution. Numerous numerical Monte Carlo simulations show that the pieces of plaster would not have been able to reach even the garden of Mr. DS, let alone a distance over 1.30 meters. Results agree with the documentary evidence (images of Mr. OP’s security cameras) that Mr. DS could not have been hit by plaster pieces coming from Mr. OP’s property.

Keywords: biophysics analysis, Monte Carlo simulations, Newton’s law of restitution, projectile motion

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Authors: Lucas M. Furlan, Cesar A. Moreira, Jepherson F. Sales, Guilherme T. Bueno, Manuel E. Ferreira, Carla V. S. Coelho, Vania Rosolen

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Located in the western part of the State of Minas Gerais, Brazil, the study area consists of a savanna environment, represented by sedimentary plateau and a soil cover composed by lateritic and hydromorphic soils - in the latter, occurring the deferruginization and concentration of high-alumina clays, exploited as refractory material. In the hydromorphic topographic depressions (wetlands) the hydropedogical relationships are little known, but it is observed that in times of rainfall, the depressed region behaves like a natural seasonal reservoir - which suggests that the wetlands on the surface of the plateau are places of recharge of the aquifer. The aquifer recharge areas are extremely important for the sustainable social, economic and environmental development of societies. The understanding of hydrodynamics in relation to the functioning of the ferruginous and hydromorphic lateritic soils system in the savanna environment is a subject rarely explored in the literature, especially its understanding through the joint application of geoprocessing by UAV (Unmanned Aerial Vehicle) and electrical tomography. The objective of this work is to understand the hydrogeological dynamics in a wetland (with an area of 426.064 m²), in the Brazilian savanna,as well as the understanding of the subsurface architecture of hydromorphic depressions in relation to the recharge of aquifers. The wetland was compartmentalized in three different regions, according to the geoprocessing. Hydraulic conductivity studies were performed in each of these three portions. Electrical tomography was performed on 9 lines of 80 meters in length and spaced 10 meters apart (direction N45), and a line with 80 meters perpendicular to all others. With the data, it was possible to generate a 3D cube. The integrated analysis showed that the area behaves like a natural seasonal reservoir in the months of greater precipitation (December – 289mm; January – 277,9mm; February – 213,2mm), because the hydraulic conductivity is very low in all areas. In the aerial images, geotag correction of the images was performed, that is, the correction of the coordinates of the images by means of the corrected coordinates of the Positioning by Precision Point of the Brazilian Institute of Geography and Statistics (IBGE-PPP). Later, the orthomosaic and the digital surface model (DSM) were generated, which with specific geoprocessing generated the volume of water that the wetland can contain - 780,922m³ in total, 265,205m³ in the region with intermediate flooding and 49,140m³ in the central region, where a greater accumulation of water was observed. Through the electrical tomography it was possible to identify that up to the depth of 6 meters the water infiltrates vertically in the central region. From the 8 meters depth, the water encounters a more resistive layer and the infiltration begins to occur horizontally - tending to concentrate the recharge of the aquifer to the northeast and southwest of the wetland. The hydrodynamics of the area is complex and has many challenges in its understanding. The next step is to relate hydrodynamics to the evolution of the landscape, with the enrichment of high-alumina clays, and to propose a management model for the seasonal reservoir.

Keywords: electrical tomography, hydropedology, unmanned aerial vehicle, water resources management

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Authors: Sirajdin Olagoke Adeshina, Haidi Ibrahim, Akeem Salawu

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large lecture theatres cannot be covered by a single camera but rather by a multicamera setup because of their size, shape, and seating arrangements. Although, classroom capture is achievable through a single camera. Therefore, a design and implementation of a multicamera setup for a large lecture hall were considered. Researchers have shown emphasis on the impact of class attendance taken on the academic performance of students. However, the traditional method of carrying out this exercise is below standard, especially for large lecture theatres, because of the student population, the time required, sophistication, exhaustiveness, and manipulative influence. An automated large classroom attendance system is, therefore, imperative. The common approach in this system is face detection and recognition, where known student faces are captured and stored for recognition purposes. This approach will require constant face database updates due to constant changes in the facial features. Alternatively, face counting can be performed by cropping the localized faces on the video or image into a folder and then count them. This research aims to develop a face localization-based approach to detect student faces in classroom images captured using a multicamera setup. A selected Haar-like feature cascade face detector trained with an asymmetric goal to minimize the False Rejection Rate (FRR) relative to the False Acceptance Rate (FAR) was applied on Raspberry Pi 4B. A relationship between the two factors (FRR and FAR) was established using a constant (λ) as a trade-off between the two factors for automatic adjustment during training. An evaluation of the proposed approach and the conventional AdaBoost on classroom datasets shows an improvement of 8% TPR (output result of low FRR) and 7% minimization of the FRR. The average learning speed of the proposed approach was improved with 1.19s execution time per image compared to 2.38s of the improved AdaBoost. Consequently, the proposed approach achieved 97% TPR with an overhead constraint time of 22.9s compared to 46.7s of the improved Adaboost when evaluated on images obtained from a large lecture hall (DK5) USM.

Keywords: automatic attendance, face detection, haar-like cascade, manual attendance

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Authors: Erzsebet Gulyas

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Our inner world, our imagination, and our way of thinking are invisible and inaudible to others, but they influence our behavior. To investigate the relationship between thinking and language use, we created a test in Hungarian using ideas from the literature. The test prompts participants to make decisions based on visual images derived from the written information presented. There is a correlation (r=0.5) between the test result and the self-assessment of the visual imagery vividness and the visual and verbal components of internal representations measured by self-report questionnaires, as well as with responses to language-use inquiries in the background questionnaire. 56 university students completed the tests, and SPSS was used to analyze the data.

Keywords: imagination, internal representations, verbalization, visualization

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Authors: Filip Rusak, Yulia Arzhaeva, Dadong Wang

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Machine learning based Computer-Aided Diagnosis (CAD) is gaining much popularity in medical imaging and diagnostic radiology. However, it requires a large amount of high quality and labeled training image datasets. The training images may come from different sources and be acquired from different radiography machines produced by different manufacturers, digital or digitized copies of film radiographs, with various sizes as well as different pixel intensity distributions. In this paper, a content-aware image augmentation method is presented to deal with these variations. The results of the proposed method have been validated graphically by plotting the removed and added seams of pixels on original images. Two different chest X-ray (CXR) datasets are used in the experiments. The CXRs in the datasets defer in size, some are digital CXRs while the others are digitized from analog CXR films. With the proposed content-aware augmentation method, the Seam Carving algorithm is employed to resize CXRs and the corresponding labels in the form of image masks, followed by histogram matching used to normalize the pixel intensities of digital radiography, based on the pixel intensity values of digitized radiographs. We implemented the algorithms, resized the well-known Montgomery dataset, to the size of the most frequently used Japanese Society of Radiological Technology (JSRT) dataset and normalized our digital CXRs for testing. This work resulted in the unified off-the-shelf CXR dataset composed of radiographs included in both, Montgomery and JSRT datasets. The experimental results show that even though the amount of augmentation is large, our algorithm can preserve the important information in lung fields, local structures, and global visual effect adequately. The proposed method can be used to augment training and testing image data sets so that the trained machine learning model can be used to process CXRs from various sources, and it can be potentially used broadly in any medical imaging applications.

Keywords: computer-aided diagnosis, image augmentation, lung segmentation, medical imaging, seam carving

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Authors: Lakshay Kharbanda, Aabhas Chauhan

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In this paper, an application of Normalized Compression Distance (NCD) to detect notable scene alterations occurring in videos is presented. Several research groups have been developing methods to perform image classification using NCD, a computable approximation to Normalized Information Distance (NID) by studying the degree of similarity in images. The timeframes where significant aberrations between the frames of a video have occurred have been identified by obtaining a threshold NCD value, using two compressors: LZMA and BZIP2 and defining scene alterations using Pixel Difference Percentage metrics.

Keywords: image compression, Kolmogorov complexity, normalized compression distance, root mean square error

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Authors: Ezil Sam Leni, Shalen S.

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Transport service monitoring and upkeep are essential components of smart city initiatives. The main risks to the relevant departments and authorities are the ever-increasing vehicular traffic and the conditions of the roads. In India, the economy is greatly impacted by the road transport sector. In 2021, the Ministry of Road Transport and Highways Transport, Government of India, produced a report with statistical data on traffic accidents. The data included the number of fatalities, injuries, and other pertinent criteria. This study proposes a distributed infrastructure for the monitoring, detection, and reporting of potholes to the appropriate authorities. In a distributed environment, the nodes are the edge devices, and local edge servers, and global servers. The edge devices receive the initial model to be employed from the global server. The YOLOv8 model for pothole detection is used in the edge devices. The edge devices run the pothole detection model, gather the pothole images on their path, and send the updates to the nearby edge server. The local edge server selects the clients for its aggregation process, aggregates the model updates and sends the updates to the global server. The global server collects the updates from the local edge servers, performs aggregation and derives the updated model. The updated model has the information about the potholes received from the local edge servers and notifies the updates to the local edge servers and concerned authorities for monitoring and maintenance of road conditions. The entire process is implemented in FedCV distributed environment with the implementation using the client-server model and aggregation entities. After choosing the clients for its aggregation process, the local edge server gathers the model updates and transmits them to the global server. After gathering the updates from the regional edge servers, the global server aggregates them and creates the updated model. Performance indicators and the experimentation environment are assessed, discussed, and presented. Accelerometer data may be taken into consideration for improved performance in the future development of this study, in addition to the images captured from the transportation routes.

Keywords: federated Learning, pothole detection, distributed framework, federated averaging

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