Search results for: retinal fundus images

Authors: Nawras Kurzom, Avi Mendelsohn

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The effects of background music on learning and memory are inconsistent, partly due to the intrinsic complexity and variety of music and partly to individual differences in music perception and preference. A prominent musical feature that is known to elicit strong emotional responses is musical tension. Musical tension can be brought about by building anticipation of rhythm, harmony, melody, and dynamics. Delaying the resolution of dominant-to-tonic chord progressions, as well as using dissonant harmonics, can elicit feelings of tension, which can, in turn, affect memory formation of concomitant information. The aim of the presented studies was to explore how forming declarative memory is influenced by musical tension, brought about within continuous music as well as in the form of isolated chords with varying degrees of dissonance/consonance. The effects of musical tension on long-term memory of declarative information were studied in two ways: 1) by evoking tension within continuous music pieces by delaying the release of harmonic progressions from dominant to tonic chords, and 2) by using isolated single complex chords with various degrees of dissonance/roughness. Musical tension was validated through subjective reports of tension, as well as physiological measurements of skin conductance response (SCR) and pupil dilation responses to the chords. In addition, music information retrieval (MIR) was used to quantify musical properties associated with tension and its release. Each experiment included an encoding phase, wherein individuals studied stimuli (words or images) with different musical conditions. Memory for the studied stimuli was tested 24 hours later via recognition tasks. In three separate experiments, we found positive relationships between tension perception and physiological measurements of SCR and pupil dilation. As for memory performance, we found that background music, in general, led to superior memory performance as compared to silence. We detected a trade-off effect between tension perception and memory, such that individuals who perceived musical tension as such displayed reduced memory performance for images encoded during musical tension, whereas tense music benefited memory for those who were less sensitive to the perception of musical tension. Musical tension exerts complex interactions with perception, emotional responses, and cognitive performance on individuals with and without musical training. Delineating the conditions and mechanisms that underlie the interactions between musical tension and memory can benefit our understanding of musical perception at large and the diverse effects that music has on ongoing processing of declarative information.

Keywords: musical tension, declarative memory, learning and memory, musical perception

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Authors: Michal Spilacek, Martin Lisy, Marek Balas, Zdenek Skala

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This article is trying to determine the status of flue gas that is entering the KWH heat exchanger from combustion chamber in order to calculate the heat transfer ratio of the heat exchanger. Combination of measurement, calculation, and computer simulation was used to create a useful way to approximate the heat transfer rate. The measurements were taken by a number of sensors that are mounted on the experimental device and by a thermal imaging camera. The results of the numerical calculation are in a good correspondence with the real power output of the experimental device. Results show that the research has a good direction and can be used to propose changes in the construction of the heat exchanger, but still needs enhancements.

Keywords: heat exchanger, heat transfer rate, numerical calculation, thermal images

Procedia PDF Downloads 616

Authors: D. F. Carvalho, A. O. Uscamayta, J. C. Guerrero, H. F. Oliveira, P. M. Azevedo-Marques

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The creation of vector contours slices (ROIs) on body silhouettes in oncologic patients is an important step during the radiotherapy planning in clinic and hospitals to ensure the accuracy of oncologic treatment. The radiotherapy planning of patients is performed by complex softwares focused on analysis of tumor regions, protection of organs at risk (OARs) and calculation of radiation doses for anomalies (tumors). These softwares are supplied for a few manufacturers and run over sophisticated workstations with vector processing presenting a cost of approximately twenty thousand dollars. The Brazilian project SIPRAD (Radiotherapy Planning System) presents a proposal adapted to the emerging countries reality that generally does not have the monetary conditions to acquire some radiotherapy planning workstations, resulting in waiting queues for new patients treatment. The SIPRAD project is composed by a set of integrated and interoperabilities softwares that are able to execute all stages of radiotherapy planning on simple personal computers (PCs) in replace to the workstations. The goal of this work is to present an image processing technique, computationally feasible, that is able to perform an automatic contour delineation in patient body silhouettes (SIPRAD-Body). The SIPRAD-Body technique is performed in tomography slices under grayscale images, extending their use with a greedy algorithm in three dimensions. SIPRAD-Body creates an irregular polyhedron with the Canny Edge adapted algorithm without the use of preprocessing filters, as contrast and brightness. In addition, comparing the technique SIPRAD-Body with existing current solutions is reached a contours similarity at least 78%. For this comparison is used four criteria: contour area, contour length, difference between the mass centers and Jaccard index technique. SIPRAD-Body was tested in a set of oncologic exams provided by the Clinical Hospital of the University of Sao Paulo (HCRP-USP). The exams were applied in patients with different conditions of ethnology, ages, tumor severities and body regions. Even in case of services that have already workstations, it is possible to have SIPRAD working together PCs because of the interoperability of communication between both systems through the DICOM protocol that provides an increase of workflow. Therefore, the conclusion is that SIPRAD-Body technique is feasible because of its degree of similarity in both new radiotherapy planning services and existing services.

Keywords: radiotherapy, image processing, DICOM RT, Treatment Planning System (TPS)

Procedia PDF Downloads 296

Authors: Tala Toonsi, Marah Alagha, Lina Alnowaiser, Hala Rajab

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This report is about the Photomath app, which is an AI application that uses image recognition technology, specifically optical character recognition (OCR) algorithms. The (OCR) algorithm translates the images into a mathematical equation, and the app automatically provides a step-by-step solution. The application supports decimals, basic arithmetic, fractions, linear equations, and multiple functions such as logarithms. Testing was conducted to examine the usage of this app, and results were collected by surveying ten participants. Later, the results were analyzed. This paper seeks to answer the question: To what level the artificial intelligence features are accurate and the speed of process in this app. It is hoped this study will inform about the efficiency of AI in Photomath to the users.

Keywords: photomath, image recognition, app, OCR, artificial intelligence, mathematical equations.

Procedia PDF Downloads 171

Authors: Alireza Akbari, Gelareh Babaee Khou

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Ground Penetrating Radar (GPR) is an important nondestructive remote sensing tool that has been used in both military and civilian fields. Recently, GPR imaging has attracted lots of attention in detection of subsurface shallow small targets such as landmines and unexploded ordnance and also imaging behind the wall for security applications. For the monostatic arrangement in the space-time GPR image, a single point target appears as a hyperbolic curve because of the different trip times of the EM wave when the radar moves along a synthetic aperture and collects reflectivity of the subsurface targets. With this hyperbolic curve, the resolution along the synthetic aperture direction shows undesired low resolution features owing to the tails of hyperbola. However, highly accurate information about the size, electromagnetic (EM) reflectivity, and depth of the buried objects is essential in most GPR applications. Therefore hyperbolic curve behavior in the space-time GPR image is often willing to be transformed to a focused pattern showing the object's true location and size together with its EM scattering. The common goal in a typical GPR image is to display the information of the spatial location and the reflectivity of an underground object. Therefore, the main challenge of GPR imaging technique is to devise an image reconstruction algorithm that provides high resolution and good suppression of strong artifacts and noise. In this paper, at first, the standard back-projection (BP) algorithm that was adapted to GPR imaging applications used for the image reconstruction. The standard BP algorithm was limited with against strong noise and a lot of artifacts, which have adverse effects on the following work like detection targets. Thus, an improved BP is based on cross-correlation between the receiving signals proposed for decreasing noises and suppression artifacts. To improve the quality of the results of proposed BP imaging algorithm, a weight factor was designed for each point in region imaging. Compared to a standard BP algorithm scheme, the improved algorithm produces images of higher quality and resolution. This proposed improved BP algorithm was applied on the simulation and the real GPR data and the results showed that the proposed improved BP imaging algorithm has a superior suppression artifacts and produces images with high quality and resolution. In order to quantitatively describe the imaging results on the effect of artifact suppression, focusing parameter was evaluated.

Keywords: algorithm, back-projection, GPR, remote sensing

Procedia PDF Downloads 452

Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Maysoon Nasser Samhan, Somaya Alkiswani, Abdullah Alzibdeh

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Background: Routine lumbar MRIs for back pain may yield normal results despite persistent symptoms, which means the possibility of other causes for this pain, which was not shown on the routine images. Research suggests including coronal STIR imaging to detect additional pathologies like sacroiliitis. Objectives: This study aims to enhance diagnostic accuracy and aid in determining treatment processes for patients with persistent back pain who have normal routine lumbar MRI (T1 and T2 images) by incorporating coronal STIR into the examination. Methods: A prospectively conducted study involving 274 patients, 115 males and 159 females, with an age range of 6–92 years, reviewed their medical records and imaging data following a lumbar spine MRI. This study included patients with back pain and sciatica as their primary complaints, all of whom underwent lumbar spine MRIs at our hospital to identify potential pathologies. Using a GE Signa HD 1.5T MRI System, each patient received a standard MRI protocol that included T1 and T2 sagittal and axial sequences, as well as a coronal STIR sequence. We collected relevant MRI findings, including abnormalities and structural variations, from radiology reports. We classified these findings into tables and documented them as counts and percentages, using Fisher’s exact test to assess differences between categorical variables. We conducted a statistical analysis using Prism GraphPad software version 10.1.2. The study adhered to ethical guidelines, institutional review board approvals, and patient confidentiality regulations. Results: Exclusion of the coronal STIR sequence led to 83 subjects (30.29%) being classified as within normal limits on MRI examination. 36 patients without abnormalities on T1 and T2 sequences showed abnormalities on the coronal STIR sequence, with 26 cases attributed to spinal pathologies and 10 to non-spinal pathologies. In addition to that, Fisher's exact test demonstrated a significant association between sacroiliitis diagnosis and abnormalities identified solely through the coronal STIR sequence (P < 0.0001). Conclusion: Implementing coronal STIR imaging as part of routine lumbar MRI protocols has the potential to improve patient care by facilitating a more comprehensive evaluation and management of persistent back pain.

Keywords: magnetic resonance imaging, lumber MRI, radiology, neurology

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Authors: Bingquan Shen

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Convolutional Neural Networks (CNN) have demonstrated their effectiveness in synthesizing 3D views of object instances at various viewpoints. Given the problem where one have limited viewpoints of a particular object for classification, we present a pose normalization architecture to transform the object to existing viewpoints in the training dataset before classification to yield better classification performance. We have demonstrated that this Pose Normalization Network (PNN) can capture the style of the target object and is able to re-render it to a desired viewpoint. Moreover, we have shown that the PNN improves the classification result for the 3D chairs dataset and ShapeNet airplanes dataset when given only images at limited viewpoint, as compared to a CNN baseline.

Keywords: convolutional neural networks, object classification, pose normalization, viewpoint invariant

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Authors: Katsue Edo, Yu Hiroi

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T Increasing attention has been paid to using pictures and photographs in research since the beginning of the 21th century in social sciences. Meanwhile we have been studying the usefulness of Picture mining, which is one of the new ways for a these picture using researches. Picture Mining is an explorative research analysis method that takes useful information from pictures, photographs and static or moving images. It is often compared with the methods of text mining. The Picture Mining concept includes observational research in the broad sense, because it also aims to analyze moving images (Ochihara and Edo 2013). In the recent literature, studies and reports using pictures are increasing due to the environmental changes. These are identified as technological and social changes (Edo et.al. 2013). Low price digital cameras and i-phones, high information transmission speed, low costs for information transferring and high performance and resolution of the cameras of mobile phones have changed the photographing behavior of people. Consequently, there is less resistance in taking and processing photographs for most of the people in the developing countries. In these studies, this method of collecting data from respondents is often called as ‘participant-generated photography’ or ‘respondent-generated visual imagery’, which focuses on the collection of data and its analysis (Pauwels 2011, Snyder 2012). But there are few systematical and conceptual studies that supports it significance of these methods. We have discussed in the recent years to conceptualize these picture using research methods and formalize theoretical findings (Edo et. al. 2014). We have identified the most efficient fields of Picture mining in the following areas inductively and in case studies; 1) Research in Consumer and Customer Lifestyles. 2) New Product Development. 3) Research in Fashion and Design. Though we have found that it will be useful in these fields and areas, we must verify these assumptions. In this study we will focus on the field of fashion and design, to determine whether picture mining methods are really reliable in this area. In order to do so we have conducted an empirical research of the respondents’ attitudes and behavior concerning pictures and photographs. We compared the attitudes and behavior of pictures toward fashion to meals, and found out that taking pictures of fashion is not as easy as taking meals and food. Respondents do not often take pictures of fashion and upload their pictures online, such as Facebook and Instagram, compared to meals and food because of the difficulty of taking them. We concluded that we should be more careful in analyzing pictures in the fashion area for there still might be some kind of bias existing even if the environment of pictures have drastically changed in these years.

Keywords: empirical research, fashion and design, Picture Mining, qualitative research

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Authors: J. Tomilla Ajayi, Werner E. Van Zyl

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This work presents an overview of the reaction of 2, 4-diferrocenyl-1, 3-dithiadiphosphetane-2, 4-disulfide (Ferrocenyl Lawesson’s reagent) with water to produce the non-symmetric, ferocenyl dithiophosphonic acid respectively in high yields. These acids were readily deprotonated by anhydrous Ammonia to yield the corresponding ammonium salt NH4S2PFcOH. These were complex to Ni (II) in molar ratio 1:1 and 1:2. The resulting complex from the reaction formed same compound with different isomers (Cis and Trans) and also compound with multimetallic coordination. Quality X-ray crystals were formed from THF/Ether. The compounds were characterized by 1H, 31P NMR, and FTIR. Bulk purity were confirmed by either ESI-MS or elemental analysis and The XRD images were obtained using single crystal X-ray crystallographic studies. The electrochemical investigation of the Compounds were carried out using cyclic voltammetry.

Keywords: ferrocenyl, dithiophosphonate, isomer, coordination

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Mehmet Bulent Ozdemir, Sultan Çagirici, Sahika Pinar Akyer, Fikri Turk

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Neuroanatomical appearance can be correlated with clinical or other characteristics of illness. With the introduction of diagnostic imaging machines, producing 3D images of anatomic structures, calculating the correlation between subjects and pattern of the structures have become possible. The aim of this study is to examine the 3D structure of hippocampus in cases with Alzheimer disease in different dementia severity. For this purpose, 62 female and 38 male- 68 patients’s (age range between 52 and 88) MR scanning were imported to the computer. 3D model of each right and left hippocampus were developed by a computer aided propramme-Surf Driver 3.5. Every reconstruction was taken by the same investigator. There were different apperance of hippocampus from normal to abnormal. In conclusion, These results might improve the understanding of the correlation between the morphological changes in hippocampus and clinical staging in Alzheimer disease.

Keywords: Alzheimer disease, hippocampus, computer-assisted anatomy, 3D

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Authors: Somkait Udomhunsakul

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In this paper, a multi-focus image fusion method using Spatial Frequency Measurements (SFM) and Wavelet Packet was proposed. The proposed fusion approach, firstly, the two fused images were transformed and decomposed into sixteen subbands using Wavelet packet. Next, each subband was partitioned into sub-blocks and each block was identified the clearer regions by using the Spatial Frequency Measurement (SFM). Finally, the recovered fused image was reconstructed by performing the Inverse Wavelet Transform. From the experimental results, it was found that the proposed method outperformed the traditional SFM based methods in terms of objective and subjective assessments.

Keywords: multi-focus image fusion, wavelet packet, spatial frequency measurement

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Authors: Jui-Ting Hsu, Heng-Li Huang, Ming-Tzu Tsai, Kuo-Chih Su, Lih-Jyh Fuh

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The objectives of this study were to measure the initial stability of the dental implant (ISQ and PTV) in the artificial foam bone block with three different quality levels. In addition, the 3D bone to implant contact percentage (BIC%) was measured based on the micro-computed tomography images. Furthermore, the relation between the initial stability of dental implant (ISQ and PTV) and BIC% were calculated. The experimental results indicated that enhanced the material property of the artificial foam bone increased the initial stability of the dental implant. The Pearson’s correlation coefficient between the BIC% and the two approaches (ISQ and PTV) were 0.652 and 0.745.

Keywords: dental implant, implant stability quotient, peak insertion torque, bone-implant contact, micro-computed tomography

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Authors: Kunal Bhardwaj, Christine Browne

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With the increase in global awareness of the environmental impacts of plastic-based products, there has been a massive drive to reduce our use of these products. Use of plastic-based substrates in electronic circuits has been a matter of concern recently. Plastics provide a very smooth and cheap surface for printing high-performance electronics due to their non-permeability to ink and easy mouldability. In this research, we explore the use of nano cellulose (NC) films in electronics as they provide an advantage of being 100% recyclable and eco-friendly. The main hindrance in the mass adoption of NC film as a substitute for plastic is its higher surface roughness which leads to ink penetration, and dispersion in the channels on the film. This research was conducted to tune the RMS roughness of NC films to a range where they can replace plastics in electronics(310-470nm). We studied the dependence of the surface roughness of the NC film on the following tunable aspects: 1) composition by weight of the NC suspension that is sprayed on a silicon wafer 2) the width and the depth of the channels on the silicon wafer used as a base. Various silicon wafers with channel depths ranging from 6 to 18 um and channel widths ranging from 5 to 500um were used as a base. Spray coating method for NC film production was used and two solutions namely, 1.5wt% NC and a 50-50 NC-CNC (cellulose nanocrystal) mixture in distilled water, were sprayed through a Wagner sprayer system model 117 at an angle of 90 degrees. The silicon wafer was kept on a conveyor moving at a velocity of 1.3+-0.1 cm/sec. Once the suspension was uniformly sprayed, the mould was left to dry in an oven at 50°C overnight. The images of the films were taken with the help of an optical profilometer, Olympus OLS 5000. These images were converted into a ‘.lext’ format and analyzed using Gwyddion, a data and image analysis software. Lowest measured RMS roughness of 291nm was with a 50-50 CNC-NC mixture, sprayed on a silicon wafer with a channel width of 5 µm and a channel depth of 12 µm. Surface roughness values of 320+-17nm were achieved at lower (5 to 10 µm) channel widths on a silicon wafer. This research opened the possibility of the usage of 100% recyclable NC films with an additive (50% CNC) in high-performance electronics. Possibility of using additives like Carboxymethyl Cellulose (CMC) is also being explored due to the hypothesis that CMC would reduce friction amongst fibers, which in turn would lead to better conformations amongst the NC fibers. CMC addition would thus be able to help tune the surface roughness of the NC film to an even greater extent in future.

Keywords: nano cellulose films, electronic circuits, nanocrystals and surface roughness

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Authors: O. Boussoufi, K. Lamrini Uahabi, M. Atounti

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The main purpose of this paper is to calculate the fractal dimension of some Julia Sets and Mandelbrot Set in the Misiurewicz Points. Using Matlab to generate the Julia Sets images that match the Misiurewicz points and using a Fractal software, we were able to find different measures that characterize those fractals in textures and other features. We are actually focusing on fractal dimension and the error calculated by the software. When executing the given equation of regression or the log-log slope of image a Box Counting method is applied to the entire image, and chosen settings are available in a FracLAc Program. Finally, a comparison is done for each image corresponding to the area (boundary) where Misiurewicz Point is located.

Keywords: box counting, FracLac, fractal dimension, Julia Sets, Mandelbrot Set, Misiurewicz Points

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Authors: Sasidhar B., Bhaskar Rao N., Ramesh Babu D. R., Ravi Shankar M.

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Segmentation of lung pleura is a preprocessing step in Computer-Aided Diagnosis (CAD) which helps in reducing false positives in detection of lung cancer. The existing methods fail in extraction of lung regions with the nodules at the pleura of the lungs. In this paper, a new method is proposed which segments lung regions with nodules at the pleura of the lungs based on curvature analysis and morphological operators. The proposed algorithm is tested on 06 patient’s dataset which consists of 60 images of Lung Image Database Consortium (LIDC) and the results are found to be satisfactory with 98.3% average overlap measure (AΩ).

Keywords: curvature analysis, image segmentation, morphological operators, thresholding

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Authors: Attallah Bilal, Hendel Fatiha

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This paper proposes the bimodal biometrics system for identity verification iris and fingerprint, at matching score level architecture using weighted sum of score technique. The features are extracted from the pre processed images of iris and fingerprint. These features of a query image are compared with those of a database image to obtain matching scores. The individual scores generated after matching are passed to the fusion module. This module consists of three major steps i.e., normalization, generation of similarity score and fusion of weighted scores. The final score is then used to declare the person as genuine or an impostor. The system is tested on CASIA database and gives an overall accuracy of 91.04% with FAR of 2.58% and FRR of 8.34%.

Keywords: iris, fingerprint, sum rule, fusion

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Authors: Maria Sciammarella, Uttam M. Shrestha, Youngho Seo, Grant T. Gullberg, Elias H. Botvinick

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Background: Myocardial perfusion imaging (MPI) is typically performed with static imaging protocols and visually assessed for perfusion defects based on the relative intensity distribution. Dynamic cardiac SPECT, on the other hand, is a new imaging technique that is based on time varying information of radiotracer distribution, which permits quantification of myocardial blood flow (MBF). In this abstract, we report a progress and current status of dynamic cardiac SPECT using conventional gamma camera (Infinia Hawkeye 4, GE Healthcare) for estimation of myocardial blood flow and coronary flow reserve. Methods: A group of patients who had high risk of coronary artery disease was enrolled to evaluate our methodology. A low-dose/high-dose rest/pharmacologic-induced-stress protocol was implemented. A standard rest and a standard stress radionuclide dose of ⁹⁹ᵐTc-tetrofosmin (140 keV) was administered. The dynamic SPECT data for each patient were reconstructed using the standard 4-dimensional maximum likelihood expectation maximization (ML-EM) algorithm. Acquired data were used to estimate the myocardial blood flow (MBF). The correspondence between flow values in the main coronary vasculature with myocardial segments defined by the standardized myocardial segmentation and nomenclature were derived. The coronary flow reserve, CFR, was defined as the ratio of stress to rest MBF values. CFR values estimated with SPECT were also validated with dynamic PET. Results: The range of territorial MBF in LAD, RCA, and LCX was 0.44 ml/min/g to 3.81 ml/min/g. The MBF between estimated with PET and SPECT in the group of independent cohort of 7 patients showed statistically significant correlation, r = 0.71 (p < 0.001). But the corresponding CFR correlation was moderate r = 0.39 yet statistically significant (p = 0.037). The mean stress MBF value was significantly lower for angiographically abnormal than that for the normal (Normal Mean MBF = 2.49 ± 0.61, Abnormal Mean MBF = 1.43 ± 0. 0.62, P < .001). Conclusions: The visually assessed image findings in clinical SPECT are subjective, and may not reflect direct physiologic measures of coronary lesion. The MBF and CFR measured with dynamic SPECT are fully objective and available only with the data generated from the dynamic SPECT method. A quantitative approach such as measuring CFR using dynamic SPECT imaging is a better mode of diagnosing CAD than visual assessment of stress and rest images from static SPECT images Coronary Flow Reserve.

Keywords: dynamic SPECT, clinical SPECT/CT, selective coronary angiograph, ⁹⁹ᵐTc-Tetrofosmin

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Authors: Daria N. Podvigina, Tatiana V. Chernigovskaya

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We have studied the temporal characteristics of bistable perception of the stimuli of two types: one involves alterations in a perceived depth and another one has an ambiguous content. We used the Necker lattice and lines of shadowed circles ambiguously perceived either as spheres or holes as stimuli of the first type. The Winson figure (the Eskimo/Indian picture) was a stimulus of the second type. We have analyzed how often the reversals occurred (reversal rate) and for how long each of the two interpretations, or percepts, was observed during one presentation (stability durations). For all three ambiguous images the reversal rate and the stability durations had similar values, which provide another evidence for a significant role of top-down processes in multistable perception.

Keywords: multistable perception, perceived depth, reversal rate, top-down processes

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Authors: X. Wang

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Timely and accurate information on urban building damage caused by earthquake is important basis for disaster assessment and emergency relief. Very high resolution (VHR) remotely sensed imagery containing abundant fine-scale information offers a large quantity of data for detecting and assessing urban building damage in the aftermath of earthquake disasters. However, the accuracy obtained using spectral features alone is comparatively low, since building damage, intact buildings and pavements are spectrally similar. Therefore, it is of great significance to detect urban building damage effectively using multi-source data. Considering that in general height or geometric structure of buildings change dramatically in the devastated areas, a novel multi-stage urban building damage detection method, using bi-temporal spectral, height and corner information, was proposed in this study. The pre-event height information was generated using stereo VHR images acquired from two different satellites, while the post-event height information was produced from airborne LiDAR data. The corner information was extracted from pre- and post-event panchromatic images. The proposed method can be summarized as follows. To reduce the classification errors caused by spectral similarity and errors in extracting height information, ground surface, shadows, and vegetation were first extracted using the post-event VHR image and height data and were masked out. Two different types of building damage were then extracted from the remaining areas: the height difference between pre- and post-event was used for detecting building damage showing significant height change; the difference in the density of corners between pre- and post-event was used for extracting building damage showing drastic change in geometric structure. The initial building damage result was generated by combining above two building damage results. Finally, a post-processing procedure was adopted to refine the obtained initial result. The proposed method was quantitatively evaluated and compared to two existing methods in Port au Prince, Haiti, which was heavily hit by an earthquake in January 2010, using pre-event GeoEye-1 image, pre-event WorldView-2 image, post-event QuickBird image and post-event LiDAR data. The results showed that the method proposed in this study significantly outperformed the two comparative methods in terms of urban building damage extraction accuracy. The proposed method provides a fast and reliable method to detect urban building collapse, which is also applicable to relevant applications.

Keywords: building damage, corner, earthquake, height, very high resolution (VHR)

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Authors: Sudeepti Dayal, Neelesh Gupta

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In terms of transmission, bigger the size of any image, longer the time the channel takes for transmission. It is understood that the bandwidth of the channel is fixed. Therefore, if the size of an image is reduced, a larger number of data or images can be transmitted over the channel. Compression is the technique used to reduce the size of an image. In terms of storage, compression reduces the file size which it occupies on the disk. Any image is based on two parameters, region of interest and non-region of interest. There are several algorithms of compression that compress the data more economically. In this paper we have reviewed region of interest and non-region of interest based compression techniques and the algorithms which compress the image most efficiently.

Keywords: compression ratio, region of interest, DCT, DWT

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Authors: Hee-Choul Kwon, Hyungjin Cho, Heeyong Kwon

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Image rotation is one of main pre-processing step in image processing or image pattern recognition. It is implemented with rotation matrix multiplication. However it requires lots of floating point arithmetic operations and trigonometric function calculations, so it takes long execution time. We propose a new high speed image rotation algorithm without two major time-consuming operations. We compare the proposed algorithm with the conventional rotation one with various size images. Experimental results show that the proposed algorithm is superior to the conventional rotation ones.

Keywords: high speed rotation operation, image processing, image rotation, pattern recognition, transformation matrix

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Authors: Retno Supriyanti, Teguh Budiono, Yogi Ramadhani, Haris B. Widodo, Arwita Mulyawati

Abstract:

Cervical cancer is the leading cause of mortality in cancer-related diseases. In this diagnosis doctors usually perform several tests to determine the presence of cervical cancer in a patient. However, these checks require support equipment to get the results in more detail. One is by using ultrasonography. However, for the developing countries most of the existing ultrasonography has a low resolution. The goal of this research is to obtain abnormalities on low-resolution ultrasound images especially for cervical cancer case. In this paper, we emphasize our work to use Image Enhancement for pre-processing image quality improvement. The result shows that pre-processing stage is promising to support further analysis.

Keywords: cervical cancer, mortality, low-resolution, image enhancement.

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Authors: S. Tehsin, S. Kausar

Abstract:

The ultimate aim of machine intelligence is to explore and materialize the human capabilities, one of which is the ability to detect various text objects within one or more images displayed on any canvas including prints, videos or electronic displays. Multimedia data has increased rapidly in past years. Textual information present in multimedia contains important information about the image/video content. However, it needs to technologically testify the commonly used human intelligence of detecting and differentiating the text within an image, for computers. Hence in this paper feature set based on anatomical study of human text detection system is proposed. Subsequent examination bears testimony to the fact that the features extracted proved instrumental to text detection.

Keywords: biologically inspired vision, content based retrieval, document analysis, text extraction

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Authors: Kemal Polat

Abstract:

In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Huayuan Lu, Chunfang Yang, Ma Zhu, Baojun Qi, Yaqiong Qiao, Jiangqian Xu

Abstract:

Image geolocalization has great application prospects in fields such as autonomous driving and virtual/augmented reality. In practical application scenarios, the size of the image to be located is not fixed; it is impractical to train different networks for all possible sizes. When its size does not match the size of the input of the descriptor extraction model, existing image geolocalization methods usually directly scale or crop the image in some common ways. This will result in the loss of some information important to the geolocalization task, thus affecting the performance of the image geolocalization method. For example, excessive down-sampling can lead to blurred building contour, and inappropriate cropping can lead to the loss of key semantic elements, resulting in incorrect geolocation results. To address this problem, this paper designs a learnable image resizer and proposes an arbitrary-sized image geolocation method. (1) The designed learnable image resizer employs the self-attention mechanism to enhance the geometric features of the resized image. Firstly, it applies bilinear interpolation to the input image and its feature maps to obtain the initial resized image and the resized feature maps. Then, SKNet (selective kernel net) is used to approximate the best receptive field, thus keeping the geometric shapes as the original image. And SENet (squeeze and extraction net) is used to automatically select the feature maps with strong contour information, enhancing the geometric features. Finally, the enhanced geometric features are fused with the initial resized image, to obtain the final resized images. (2) The proposed image geolocalization method embeds the above image resizer as a fronting layer of the descriptor extraction network. It not only enables the network to be compatible with arbitrary-sized input images but also enhances the geometric features that are crucial to the image geolocalization task. Moreover, the triplet attention mechanism is added after the first convolutional layer of the backbone network to optimize the utilization of geometric elements extracted by the first convolutional layer. Finally, the local features extracted by the backbone network are aggregated to form image descriptors for image geolocalization. The proposed method was evaluated on several mainstream datasets, such as Pittsburgh30K, Tokyo24/7, and Places365. The results show that the proposed method has excellent size compatibility and compares favorably to recently mainstream geolocalization methods.

Keywords: image geolocalization, self-attention mechanism, image resizer, geometric feature

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Authors: Golnoush Rostami, Farzam Kharvari

Abstract:

In recent years, developments in the field of architectural education have resulted in a shift from conventional teaching methods to alternative state-of-the-art approaches in teaching methods and strategies. Criticism in architecture has been a key player both in the profession and education, but it has been mostly offered by renowned individuals. Hence, not only students or other professionals but also critics themselves may not have the option to experience buildings and rely on available 2D materials, such as images and plans, that may not result in a holistic understanding and evaluation of buildings. On the other hand, immersive environments provide students and professionals the opportunity to experience buildings virtually and reflect their evaluation by experiencing rather than judging based on 2D materials. Therefore, the aim of this study is to compare the effect of experiencing buildings in immersive environments and 2D drawings, including images and plans, on architecture criticism and architectural education. As a result, three buildings that have parametric brick facades were studied through 2D materials and in Unreal Engine v. 24 as an immersive environment among 22 architecture students that were selected using convenient sampling and were divided into two equal groups using simple random sampling. This study used mixed methods, including quantitative and qualitative methods; the quantitative section was carried out by a questionnaire, and deep interviews were used for the qualitative section. A questionnaire was developed for measuring three constructs, including privacy regulation based on Altman’s theory, the sufficiency of illuminance levels in the building, and the visual status of the view (visually appealing views based on obstructions that may have been caused by facades). Furthermore, participants had the opportunity to reflect their understanding and evaluation of the buildings in individual interviews. Accordingly, the collected data from the questionnaires were analyzed using independent t-test and descriptive analyses in IBM SPSS Statistics v. 26, and interviews were analyzed using the content analysis method. The results of the interviews showed that the participants who experienced the buildings in the immersive environment were able to have a thorough and more precise evaluation of the buildings in comparison to those who studied them through 2D materials. Moreover, the analyses of the respondents’ questionnaires revealed that there were statistically significant differences between measured constructs among the two groups. The outcome of this study suggests that integrating immersive environments into the profession and architectural education as an effective and efficient tool for architecture criticism is vital since these environments allow users to have a holistic evaluation of buildings for vigorous and sound criticism.

Keywords: immersive environments, architecture criticism, architectural education, occupant-centric evaluation, pre-occupancy evaluation

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Authors: András Rövid, Takeshi Hashimoto

Abstract:

The paper focuses on the problem of the point correspondence matching in stereo images. The proposed matching algorithm is based on the combination of simpler methods such as normalized sum of squared differences (NSSD) and a more complex phase correlation based approach, by considering the noise and other factors, as well. The speed of NSSD and the preciseness of the phase correlation together yield an efficient approach to find the best candidate point with sub-pixel accuracy in stereo image pairs. The task of the NSSD in this case is to approach the candidate pixel roughly. Afterwards the location of the candidate is refined by an enhanced phase correlation based method which in contrast to the NSSD has to run only once for each selected pixel.

Keywords: stereo matching, sub-pixel accuracy, phase correlation, SVD, NSSD

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Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

Abstract:

Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

Procedia PDF Downloads 224