Search results for: cut redundant information in image

Authors: Katalin Szöllősi, László Szabó

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Introduction and purpose: Even though female sexual dysfunctions are common among women in the postpartum period, the profile of these disturbances has not been well investigated in Hungary yet. The aim of the study was to evaluate the postpartum female sexual functions in Hungary. This research sought to investigate the possible predictor factors which can influence postpartum female sexual functions. Method and sample: This was a cross-sectional study, including patients from two maternity clinics in Budapest. 113 women were recruited into our study 3 months after their childbirth. 53 had vaginal birth, 60 had a caesarian section. Data were collected from medical reports in addition by using self-developed questions and validated questionnaires in order to measure important predictors which may be responsible for postpartum sexual dysfunctions such as mode of delivery, parity, urinary incontinence and body image. Sexual functions were evaluated by the Hungarian version of the Female Sexual Function Index (FSFI). The Hungarian version of Body Image Questionnaire-Short Form14 (BSQ-SF14) was applied for assessing body image. Results: 82,3% of the participants began to have sexual intercourse within three months postpartum. 53,98% of the participants reported sexual dysfunctions (cut-off FSFI score 26,55). According to our results mode of delivery, parity, hemorrhoids, time of intercourse, resumption was not associated with female sexual dysfunctions. We found correlation at a tendential level between urinary incontinence and sexual dysfunctions (p=0,003, R=0,26). We found a negative correlation at a tendential level between the total score of BSQ-SF14 and FSFI (p=0,03, R=-0,269). Only 32,74% of women reported discussing sexual life with health care professionals. However, 67,25% of them would have had the need to be asked about their postpartum health issues. Conclusions and recommendations: The prevalence of female sexual dysfunctions were relatively high after childbirth. We found that incontinence and body image was associated with sexual dysfunctions; other risk factors remained unknown. Despite regular contact with health care professionals, women rarely get any information about postpartum sexual health issues. The high prevalence of dysfunctions indicates the need for further investigation to address other risk factors and proper counselling of women after childbirth.

Keywords: body image, postpartum, sexual dysfunction, urinary incontinence

Procedia PDF Downloads 107

Authors: Devi Srinivas, K. Sindhuja

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This paper presents architecture to improve surveillance applications based on the usage of the service oriented paradigm, with smart phones as user terminals, allowing application dynamic composition and increasing the flexibility of the system. According to the result of moving object detection research on video sequences, the movement of the people is tracked using video surveillance. The moving object is identified using the image subtraction method. The background image is subtracted from the foreground image, from that the moving object is derived. So the Background subtraction algorithm and the threshold value is calculated to find the moving image by using background subtraction algorithm the moving frame is identified. Then, by the threshold value the movement of the frame is identified and tracked. Hence, the movement of the object is identified accurately. This paper deals with low-cost intelligent mobile phone-based wireless video surveillance solution using moving object recognition technology. The proposed solution can be useful in various security systems and environmental surveillance. The fundamental rule of moving object detecting is given in the paper, then, a self-adaptive background representation that can update automatically and timely to adapt to the slow and slight changes of normal surroundings is detailed. While the subtraction of the present captured image and the background reaches a certain threshold, a moving object is measured to be in the current view, and the mobile phone will automatically notify the central control unit or the user through SMS (Short Message System). The main advantage of this system is when an unknown image is captured by the system it will alert the user automatically by sending an SMS to user’s mobile.

Keywords: security, graphical password, persuasive cued click points

Procedia PDF Downloads 529

Authors: Nouf Alharbi

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The electrospray deposition technique became an important method that enables fragile, nonvolatile molecules to be deposited in situ in high vacuum environments. Furthermore, it is considered one of the ways to close the gap between basic surface science and molecular engineering, which represents a gradual change in the range of scientist research. Also, this paper talked about one of the most important techniques that have been developed and aimed for helping to further develop and characterize the electrospray by providing data collected using an image charge detection instrument. Image charge detection mass spectrometry (CDMS) is used to measure speed and charge distributions of the molecular ions. As well as, some data has been included using SIMION simulation to simulate the energies and masses of the molecular ions through the system in order to refine the mass-selection process.

Keywords: charge, deposition, electrospray, image, ions, molecules, SIMION

Procedia PDF Downloads 129

Authors: Terungwa Simon Yange, Agana Moses A.

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This paper studied steganography and designed a simplistic approach to a steganographic tool for hiding information in image files with the view of addressing the security challenges with data by hiding data from unauthorized users to improve its security. The Structured Systems Analysis and Design Method (SSADM) was used in this work. The system was developed using Java Development Kit (JDK) 1.7.0_10 and MySQL Server as its backend. The system was tested with some hypothetical health records which proved the possibility of protecting data from unauthorized users by making it secret so that its existence cannot be easily recognized by fraudulent users. It further strengthens the confidentiality of patient records kept by medical practitioners in the health setting. In conclusion, this work was able to produce a user friendly steganography software that is very fast to install and easy to operate to ensure privacy and secrecy of sensitive data. It also produced an exact copy of the original image and the one carrying the secret message when compared with each.

Keywords: steganography, cryptography, encryption, decryption, secrecy

Procedia PDF Downloads 256

Authors: Dahi Ghareab Abdelsalam Ibrahim, R. H. Bakr

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Ionizing radiation, such as gamma radiation and X-rays, has many applications in medical diagnoses and cancer treatment. In this paper, we used the windowed Fourier transform to extract the complex image of the deformed red blood cells. The real values of the complex image are used to extract the best fitting of the deformed cell boundary. Male albino rats are irradiated by γ-rays from ⁶⁰Co. The male albino rats are anesthetized with ether, and then blood samples are collected from the eye vein by heparinized capillary tubes for studying the radiation-damaging effect in-vivo by the proposed windowed Fourier transform. The peripheral blood films are prepared according to the Brown method. The peripheral blood film is photographed by using an Automatic Image Contour Analysis system (SAMICA) from ELBEK-Bildanalyse GmbH, Siegen, Germany. The SAMICA system is provided with an electronic camera connected to a computer through a built-in interface card, and the image can be magnified up to 1200 times and displayed by the computer. The images of the peripheral blood films are then analyzed by the windowed Fourier transform method to extract the precise deformation from the best fitting. Based on accurate deformation evaluation of the red blood cells, diseases can be diagnosed in their primary stages.

Keywords: windowed Fourier transform, red blood cells, phase wrapping, Image processing

Procedia PDF Downloads 76

Authors: Sourour Khouaja, Hejer Jlassi, Nadia Feddaoui, Kamel Hamrouni

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Most medical images, and especially mammographies, are now stored in large databases. Retrieving a desired image is considered of great importance in order to find previous similar cases diagnosis. Our method is implemented to assist radiologists in retrieving mammographic images containing breast with similar density aspect as seen on the mammogram. This is becoming a challenge seeing the importance of density criteria in cancer provision and its effect on segmentation issues. We used the BEMD (Bidimensional Empirical Mode Decomposition) to characterize the content of images and Euclidean distance measure similarity between images. Through the experiments on the MIAS mammography image database, we confirm that the results are promising. The performance was evaluated using precision and recall curves comparing query and retrieved images. Computing recall-precision proved the effectiveness of applying the CBIR in the large mammographic image databases. We found a precision of 91.2% for mammography with a recall of 86.8%.

Keywords: BEMD, breast density, contend-based, image retrieval, mammography

Procedia PDF Downloads 227

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 187

Authors: Usha Dinakaran, Richa Ganguly

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Gastronomic tourism has evolved into a prominent niche within the travel industry, with tourists increasingly seeking unique culinary experiences as a primary motivation for their journeys. This research explores the intricate relationship between gastronomic tourism and its profound influence on the overall image of travel destinations. It delves into the multifaceted aspects of culinary experiences, tourists' perceptions, and the preservation of cultural identity, all of which play pivotal roles in shaping a destination's image. The primary aim of this study is to comprehensively examine the interplay between gastronomy and tourism, specifically focusing on its impact on destination image. The research seeks to achieve the following objectives: (1) Investigate how tourists perceive and engage with gastronomic tourism experiences. (2) Understand the significance of food in shaping the tourism image. (3.) Explore the connection between gastronomy and the destination's cultural identity Quantify the relationship between tourists' engagement in co-creation activities related to gastronomic tourism and their overall satisfaction with the quality of their culinary experiences. To achieve these objectives, a mixed-method research approach will be employed, including surveys, interviews, and content analysis. Data will be collected from tourists visiting diverse destinations known for their culinary offerings. This research anticipates uncovering valuable insights into the nexus between gastronomic tourism and destination image. It is expected to shed light on how tourists' perceptions of culinary experiences impact their overall perception of a destination. Additionally, the study aims to identify factors influencing tourist satisfaction and how cultural identity is preserved and promoted through gastronomic tourism. The findings of this research hold practical implications for destination marketers and stakeholders. Understanding the symbiotic relationship between gastronomy and tourism can guide the development of more targeted marketing strategies. Furthermore, promoting co-creation activities can enhance tourists' culinary experiences and contribute to the positive image of destinations.This study contributes to the growing body of knowledge regarding gastronomic tourism by consolidating insights from various studies and offering a comprehensive perspective on its impact on destination image. It offers a platform for future research in this domain and underscores the importance of culinary experiences in contemporary travel. In conclusion, this research endeavors to illuminate the dynamic interplay between gastronomic tourism and destination image, providing valuable insights for both academia and industry stakeholders in the field of tourism and hospitality.

Keywords: gastronomy, tourism, destination image, culinary

Procedia PDF Downloads 72

Authors: Mina Dabirinezhad, Mohsen Bayat Pour, Amin Dabirinejad

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This paper is intended to introduce the technology to solve some of the deficiencies of the direct digital radiology. Nowadays, digital radiology is the latest progression in dental imaging, which has become an essential part of dentistry. There are two main parts of the direct digital radiology comprised of an intraoral X-ray machine and a sensor (digital image receptor). The dentists and the dental nurses experience afflictions during the taking image process by the direct digital X-ray machine. For instance, sometimes they need to readjust the sensor in the mouth of the patient to take the X-ray image again due to the low quality of that. Another problem is, the position of the sensor may move in the mouth of the patient and it triggers off an inappropriate image for the dentists. It means that it is a time-consuming process for dentists or dental nurses. On the other hand, taking several the X-ray images brings some problems for the patient such as being harmful to their health and feeling pain in their mouth due to the pressure of the sensor to the jaw. The author provides a technology to solve the above-mentioned issues that is called “Self-Recognition Direct Digital Radiology” (SDDR). This technology is based on the principle that the intraoral X-ray machine is capable to diagnose the location of the sensor in the mouth of the patient automatically. In addition, to solve the aforementioned problems, SDDR technology brings out fewer environmental impacts in comparison to the previous version.

Keywords: Dental direct digital imaging, digital image receptor, digital x-ray machine, and environmental impacts

Procedia PDF Downloads 135

Authors: Simona Lucakova, Irena Branyikova

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Eutrophication, a condition when water becomes over-enriched with nutrients (P, N), can lead to undesirable excessive growth of phytoplankton, so-called algal bloom. This process results in the accumulation of toxin-producing cyanobacteria and oxygen depletion, both possibly leading to the collapse of the whole ecosystem. In real conditions, the limiting nutrient, which determines the possible growth of harmful algal bloom, is usually phosphorus. Algicides or flocculants have been applied in the eutrophicated waterbody in order to reduce the phytoplankton growth, which leads to the introduction of toxic chemicals into the water. In our laboratory, the idea of the prevention of harmful phytoplankton growth by the intentional cultivation of non-toxic cyanobacteria Tolypothrix tenuis in semi-open floating photobioreactors directly on the surface of phosphorus-rich waterbody is examined. During the process of cultivation, redundant phosphorus is incorporated into cyanobacterial biomass, which can be subsequently used for the production of biofuels, cosmetics, pharmaceuticals, or biostimulants for agricultural use. To determine the ability of phosphorus incorporation, batch-cultivation of Tolypothrix biomass in media simulating eutrophic water (10% BG medium) and in effluent from municipal wastewater treatment plant, both with the initial phosphorus concentration in the range 0.5-1.0 mgP/L was performed in laboratory-scale models of floating photobioreactors. After few hours of cultivation, the phosphorus content was decreased below the target limit of 0.035 mgP/L, which was given as a borderline for the algal bloom formation. Under laboratory conditions, the effect of several parameters on the rate of phosphorus decrease was tested (illumination, temperature, stirring speed/aeration gas flow, biomass to medium ratio). Based on the obtained results, a bench-scale floating photobioreactor was designed and will be tested for Tolypothrix growth in real conditions. It was proved that intentional cultivation of cyanobacteria Tolypothrix could be a suitable approach for extracting redundant phosphorus from eutrophic waters as prevention of algal bloom formation.

Keywords: cyanobacteria, eutrophication, floating photobioreactor, Tolypothrix

Procedia PDF Downloads 156

Authors: Parveen Kumar

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This article discusses institute information resources. Information, in its most restricted technical sense, is a sequence of symbols that can be interpreted as message information can be recorded as signs, or transmitted as signals. Information is any kind of event that affects the state of a dynamic system. Conceptually, information is the message being conveyed. This concept has numerous other meanings in different contexts. Moreover, the concept of information is closely related to notions of constraint, communication, control, data, form, instruction, knowledge, meaning, mental stimulus, pattern, perception, representation, and especially entropy.

Keywords: institutions, information institutions, information services for mission-oriented institute, pattern

Procedia PDF Downloads 372

Authors: Jigg Pelayo, Ricardo Villar

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Leaning on the development of aerial laser scanning in the Philippine geospatial industry, researches about remote sensing and machine vision technology became a trend. Object detection via template matching is one of its application which characterized to be fast and in real time. The paper purposely attempts to provide application for robust pattern matching algorithm based on the normalized cross correlation (NCC) criterion function subjected in Object-based image analysis (OBIA) utilizing high-resolution aerial imagery and low density LiDAR data. The height information from laser scanning provides effective partitioning order, thus improving the hierarchal class feature pattern which allows to skip unnecessary calculation. Since detection is executed in the object-oriented platform, mathematical morphology and multi-level filter algorithms were established to effectively avoid the influence of noise, small distortion and fluctuating image saturation that affect the rate of recognition of features. Furthermore, the scheme is evaluated to recognized the performance in different situations and inspect the computational complexities of the algorithms. Its effectiveness is demonstrated in areas of Misamis Oriental province, achieving an overall accuracy of 91% above. Also, the garnered results portray the potential and efficiency of the implemented algorithm under different lighting conditions.

Keywords: algorithm, LiDAR, object recognition, OBIA

Procedia PDF Downloads 237

Authors: Jie Huo, Jonathan Wu

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Segmentation of anatomical structures in medical images is essential for scientific inquiry into the complex relationships between biological structure and clinical diagnosis, treatment and assessment. As a method of incorporating the prior knowledge and the anatomical structure similarity between a target image and atlases, multi-atlas segmentation has been successfully applied in segmenting a variety of medical images, including the brain, cardiac, and abdominal images. The basic idea of multi-atlas segmentation is to transfer the labels in atlases to the coordinate of the target image by matching the target patch to the atlas patch in the neighborhood. However, this technique is limited by the pairwise registration between target image and atlases. In this paper, a novel multi-atlas segmentation approach is proposed by introducing a dynamic energy model. First, the target is mapped to each atlas image by minimizing the dynamic energy function, then the segmentation of target image is generated by weighted fusion based on the energy. The method is tested on MICCAI 2012 Multi-Atlas Labeling Challenge dataset which includes 20 target images and 15 atlases images. The paper also analyzes the influence of different parameters of the dynamic energy model on the segmentation accuracy and measures the dice coefficient by using different feature terms with the energy model. The highest mean dice coefficient obtained with the proposed method is 0.861, which is competitive compared with the recently published method.

Keywords: brain MRI segmentation, dynamic energy model, multi-atlas segmentation, energy minimization

Procedia PDF Downloads 327

Authors: Jie Zhao, Meng Su

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Image recognition, as one of the most critical technologies in computer vision, works to help machine-like robotics understand a scene, that is, if deployed appropriately, will trigger the revolution in remote sensing and industry automation. With the developments of AI technologies, there are many prevailing and sophisticated neural networks as technologies developed for image recognition. However, computer vision platforms as hardware, supporting neural networks for image recognition, as crucial as the neural network technologies, need to be more congruently addressed as the research subjects. In contrast, different computer vision platforms are deterministic to leverage the performance of different neural networks for recognition. In this paper, three different computer vision platforms – Jetson Nano(with 4GB), a standalone laptop(with RTX 3000s, using CUDA), and Google Colab (web-based, using GPU) are explored and four prominent neural network architectures (including AlexNet, VGG(16/19), GoogleNet, and ResNet(18/34/50)), are investigated. In the context of pairwise usage between different computer vision platforms and distinctive neural networks, with the merits of recognition accuracy and time efficiency, the performances are evaluated. In the case study using public imageNets, our findings provide a nuanced perspective on optimizing image recognition tasks across Edge-AI platforms, offering guidance on selecting appropriate neural network structures to maximize performance under hardware constraints.

Keywords: alexNet, VGG, googleNet, resNet, Jetson nano, CUDA, COCO-NET, cifar10, imageNet large scale visual recognition challenge (ILSVRC), google colab

Procedia PDF Downloads 79

Authors: Louis Nahimana

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Most maps are in a raster or pdf format and it is not easy to get vector layers of published maps. Faced to the production of geological simplified map of the northern Lake Tanganyika countries without geological information in vector format, I tried a method of obtaining vector layers from raster layers created from geological maps of Rwanda and DR Congo in pdf and jpg format. The procedure was as follows: The original raster maps were georeferenced using ArcGIS10.2. Under Adobe Photoshop, map areas with the same color corresponding to a lithostratigraphic unit were selected all over the map and saved in a specific raster layer. Using the same image processing software Adobe Photoshop, each RGB raster layer was converted in grayscale type and improved before importation in ArcGIS10. After georeferencing, each lithostratigraphic raster layer was transformed into a multitude of polygons with the tool "Raster to Polygon (Conversion)". Thereafter, tool "Aggregate Polygons (Cartography)" allowed obtaining a single polygon layer. Repeating the same steps for each color corresponding to a homogeneous rock unit, it was possible to reconstruct the simplified geological constitution of Rwanda and the Democratic Republic of Congo in vector format. By using the tool «Append (Management)», vector layers obtained were combined with those from Burundi to achieve vector layers of the geology of the « Northern Lake Tanganyika countries ».

Keywords: creating raster layer under image processing software, raster to polygon, aggregate polygons, adobe photoshop

Procedia PDF Downloads 437

Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

Procedia PDF Downloads 491

Authors: Mais Nijim, Rama Devi Chennuboyina, Waseem Al Aqqad

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Advances in spatial and spectral resolution of satellite images have led to tremendous growth in large image databases. The data we acquire through satellites, radars and sensors consists of important geographical information that can be used for remote sensing applications such as region planning, disaster management. Spatial data classification and object recognition are important tasks for many applications. However, classifying objects and identifying them manually from images is a difficult task. Object recognition is often considered as a classification problem, this task can be performed using machine-learning techniques. Despite of many machine-learning algorithms, the classification is done using supervised classifiers such as Support Vector Machines (SVM) as the area of interest is known. We proposed a classification method, which considers neighboring pixels in a region for feature extraction and it evaluates classifications precisely according to neighboring classes for semantic interpretation of region of interest (ROI). A dataset has been created for training and testing purpose; we generated the attributes by considering pixel intensity values and mean values of reflectance. We demonstrated the benefits of using knowledge discovery and data-mining techniques, which can be on image data for accurate information extraction and classification from high spatial resolution remote sensing imagery.

Keywords: remote sensing, object recognition, classification, data mining, waterbody identification, feature extraction

Procedia PDF Downloads 333

Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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Interpretation of medical images benefits from anatomical and physiological priors to optimize computer-aided diagnosis applications. Segmentation of liver, spleen and kidneys is regarded as a major primary step in the computer-aided diagnosis of abdominal organ diseases. In this paper, a semi-automated method for medical image data is presented for the abdominal organ segmentation data using mathematical morphology. Our proposed method is based on hierarchical segmentation and watershed algorithm. In our approach, a powerful technique has been designed to suppress over-segmentation based on mosaic image and on the computation of the watershed transform. Our algorithm is currency in two parts. In the first, we seek to improve the quality of the gradient-mosaic image. In this step, we propose a method for improving the gradient-mosaic image by applying the anisotropic diffusion filter followed by the morphological filters. Thereafter, we proceed to the hierarchical segmentation of the liver, spleen and kidney. To validate the segmentation technique proposed, we have tested it on several images. Our segmentation approach is evaluated by comparing our results with the manual segmentation performed by an expert. The experimental results are described in the last part of this work.

Keywords: anisotropic diffusion filter, CT images, morphological filter, mosaic image, simultaneous organ segmentation, the watershed algorithm

Procedia PDF Downloads 430

Authors: Vaibhav Barve

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Reversible information hiding has drawn a lot of interest as of late. Being reversible, we can restore unique computerized data totally. It is a plan where mystery data is put away in digital media like image, video, audio to maintain a strategic distance from unapproved access and security reason. By and large JPEG bit stream is utilized to store this key data, first JPEG bit stream is encrypted into all around sorted out structure and then this secret information or key data is implanted into this encrypted region by marginally changing the JPEG bit stream. Valuable pixels suitable for information implanting are computed and as indicated by this key subtle elements are implanted. In our proposed framework we are utilizing RC4 algorithm for encrypting JPEG bit stream. Encryption key is acknowledged by framework user which, likewise, will be used at the time of decryption. We are executing enhanced least significant bit supplanting steganography by utilizing genetic algorithm. At first, the quantity of bits that must be installed in a guaranteed coefficient is versatile. By utilizing proper parameters, we can get high capacity while ensuring high security. We are utilizing logistic map for shuffling of bits and utilization GA (Genetic Algorithm) to find right parameters for the logistic map. Information embedding key is utilized at the time of information embedding. By utilizing precise picture encryption and information embedding key, the beneficiary can, without much of a stretch, concentrate the incorporated secure data and totally recoup the first picture and also the original secret information. At the point when the embedding key is truant, the first picture can be recouped pretty nearly with sufficient quality without getting the embedding key of interest.

Keywords: data embedding, decryption, encryption, reversible data hiding, steganography

Procedia PDF Downloads 283

Authors: Mariela Gonzalez, Zoraide Lugli, Eleonora Vivas, Rosana Guzmán

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The objective was to determine the predictive capacity of self-efficacy perceived for weight control, locus of weight control and skills of weight self-management in the dissatisfaction of the body image in obese people who attend treatment. Sectional study conducted in the city of Maracay, Venezuela, with 243 obese who attend to treatment, 173 of the feminine gender and 70 of the male, with ages ranging between 18 and 57 years old. The sample body mass index ranged between 29.39 and 44.14. The following instruments were used: The Body Shape Questionnaire (BSQ), the inventory of body weight self-regulation, The Inventory of self-efficacy in the regulation of body weight and the Inventory of the Locus of weight control. Calculating the descriptive statistics and of central tendency, coefficients of correlation and multiple regression; it was found that a low ‘perceived Self-efficacy in the weight control’ and a high ‘Locus of external control’, predict the dissatisfaction with body image in obese who attend treatment. The findings are a first approximation to give an account of the importance of the personal control variables in the study of the psychological grief on the overweight individual.

Keywords: dissatisfaction with body image, obese people, personal control, psychological variables

Procedia PDF Downloads 426

Authors: Khairani Binti Supyan, Fatimah Khalid, Mas Rina Mustaffa, Azreen Bin Azman, Amirul Azuani Romle

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Perennial plant classification plays a significant role in various agricultural and environmental applications, assisting in plant identification, disease detection, and biodiversity monitoring. Nevertheless, attaining high accuracy in perennial plant image classification remains challenging due to the complex variations in plant appearance, the diverse range of environmental conditions under which images are captured, and the inherent variability in image quality stemming from various factors such as lighting conditions, camera settings, and focus. This paper proposes an adaptation approach to optimize perennial plant image classification by fine-tuning the pre-trained DNNs model. This paper explores the efficacy of fine-tuning prevalent architectures, namely VGG16, ResNet50, and InceptionV3, leveraging transfer learning to tailor the models to the specific characteristics of perennial plant datasets. A subset of the MYLPHerbs dataset consisted of 6 perennial plant species of 13481 images under various environmental conditions that were used in the experiments. Different strategies for fine-tuning, including adjusting learning rates, training set sizes, data augmentation, and architectural modifications, were investigated. The experimental outcomes underscore the effectiveness of fine-tuning deep neural networks for perennial plant image classification, with ResNet50 showcasing the highest accuracy of 99.78%. Despite ResNet50's superior performance, both VGG16 and InceptionV3 achieved commendable accuracy of 99.67% and 99.37%, respectively. The overall outcomes reaffirm the robustness of the fine-tuning approach across different deep neural network architectures, offering insights into strategies for optimizing model performance in the domain of perennial plant image classification.

Keywords: perennial plants, image classification, deep neural networks, fine-tuning, transfer learning, VGG16, ResNet50, InceptionV3

Procedia PDF Downloads 56

Authors: Han-Wei Shih, Yao-Nan Wang, Ko-Tung Chang, Lung-Ming Fu

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A multi-channel cell growth real-time monitor and evaluation system using charge-coupled device (CCD) sensors with 40X lens integrating a NI LabVIEW image processing program is proposed and demonstrated. The LED light source control of monitor system is utilizing 8051 microprocessor integrated with NI LabVIEW software. In this study, the same concentration RAW264.7 cells growth rate and morphology in four different culture conditions (DMEM, LPS, G1, G2) were demonstrated. The real-time cells growth image was captured and analyzed by NI Vision Assistant every 10 minutes in the incubator. The image binarization technique was applied for calculating cell doubling time and cell division index. The cells doubling time and cells division index of four group with DMEM, LPS, LPS+G1, LPS+G2 are 12.3 hr,10.8 hr,14.0 hr,15.2 hr and 74.20%, 78.63%, 69.53%, 66.49%. The image magnification of multi-channel CCDs cell real-time monitoring system is about 100X~200X which compares with the traditional microscope.

Keywords: charge-coupled device (CCD), RAW264.7, doubling time, division index

Procedia PDF Downloads 354

Authors: Saman Ghaffarian, Ilgin Gökaşar

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This paper presents a new automatic vehicle detection method from very high resolution aerial images to measure traffic density. The proposed method starts by extracting road regions from image using road vector data. Then, the road image is divided into equal sections considering resolution of the images. Gradient vectors of the road image are computed from edge map of the corresponding image. Gradient vectors on the each boundary of the sections are divided where the gradient vectors significantly change their directions. Finally, number of vehicles in each section is carried out by calculating the standard deviation of the gradient vectors in each group and accepting the group as vehicle that has standard deviation above predefined threshold value. The proposed method was tested in four very high resolution aerial images acquired from Istanbul, Turkey which illustrate roads and vehicles with diverse characteristics. The results show the reliability of the proposed method in detecting vehicles by producing 86% overall F1 accuracy value.

Keywords: aerial images, intelligent transportation systems, traffic density measurement, vehicle detection

Procedia PDF Downloads 373

Authors: El Asnaoui Khalid, Chawki Youness, Aksasse Brahim, Ouanan Mohammed

Abstract:

In these recent decades, the important and fast growth in the development and demand of multimedia products is contributing to an insufficient in the bandwidth of device and network storage memory. Consequently, the theory of data compression becomes more significant for reducing the data redundancy in order to save more transfer and storage of data. In this context, this paper addresses the problem of the lossless and the near-lossless compression of images. This proposed method is based on Block SVD Power Method that overcomes the disadvantages of Matlab's SVD function. The experimental results show that the proposed algorithm has a better compression performance compared with the existing compression algorithms that use the Matlab's SVD function. In addition, the proposed approach is simple and can provide different degrees of error resilience, which gives, in a short execution time, a better image compression.

Keywords: image compression, SVD, block SVD power method, lossless compression, near lossless

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Authors: R. R. Hordijk, O. J. G. Somsen

Abstract:

Traditionally, radar systems are the eyes and ears of a ship. However, these systems have their drawbacks and nowadays they are extended with systems that work with video and photos. Processing of data from these videos and photos is however very labour-intensive and efforts are being made to automate this process. A major problem when trying to recognize objects in water is that the 'background' is not homogeneous so that traditional image recognition technics do not work well. Main question is, can a method be developed which automate this recognition process. There are a large number of parameters involved to facilitate the identification of objects on such images. One is varying the resolution. In this research, the resolution of some images has been reduced to the extreme value of 1% of the original to reduce clutter before the polynomial fit (pre-processing). It turned out that the searched object was clearly recognizable as its grey value was well above the average. Another approach is to take two images of the same scene shortly after each other and compare the result. Because the water (waves) fluctuates much faster than an object floating in the water one can expect that the object is the only stable item in the two images. Both these methods (pre-processing and comparing two images of the same scene) delivered useful results. Though it is too early to conclude that with these methods all image problems can be solved they are certainly worthwhile for further research.

Keywords: image processing, image recognition, polynomial fit, water

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Authors: Khalak Bin Khair, Saqib Jahir, Mohammed Ibrahim, Fahad Bin, Debajyoti Karmaker

Abstract:

Object detection is a computer technology that deals with searching through digital images and videos for occurrences of semantic elements of a particular class. It is associated with image processing and computer vision. On top of object detection, we detect camouflage objects within an image using Deep Learning techniques. Deep learning may be a subset of machine learning that's essentially a three-layer neural network Over 6500 images that possess camouflage properties are gathered from various internet sources and divided into 4 categories to compare the result. Those images are labeled and then trained and tested using vgg16 architecture on the jupyter notebook using the TensorFlow platform. The architecture is further customized using Transfer Learning. Methods for transferring information from one or more of these source tasks to increase learning in a related target task are created through transfer learning. The purpose of this transfer of learning methodologies is to aid in the evolution of machine learning to the point where it is as efficient as human learning.

Keywords: deep learning, transfer learning, TensorFlow, camouflage, object detection, architecture, accuracy, model, VGG16

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Authors: Yongseok Kwon, Woowon Jeong, Eunjin Cho, Sangkug Chung, Kyehan Rhee

Abstract:

Steady streaming flow fields induced by a 500 um bubble oscillating at 12 kHz were measured using microscopic particle image velocimetry (PIV). The accuracy of velocity measurement using a micro PIV system was checked by comparing the measured velocity fields with the theoretical velocity profiles in fully developed laminar flow. The steady streaming flow velocities were measured in the saggital plane of the bubble attached on the wall. Measured velocity fields showed upward jet flow with two symmetric counter-rotating vortices, and the maximum streaming velocity was about 12 mm/s, which was within the velocity ranges measured by other researchers. The measured streamlines were compared with the analytic solution, and they also showed a reasonable agreement.

Keywords: oscillating bubble, particle image velocimetry, microstreaming, vortices,

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Authors: Chao Wang

Abstract:

Under rapid innovation of information technology, the media plays a very important role in the dissemination of information, and it has a totally different analogy generations face. However, the involvement of narrative images provides more possibilities of narrative text. "Images" through the process of aperture, a camera shutter and developable photosensitive processes are manufactured, recorded and stamped on paper, displayed on a computer screen-concretely saved. They exist in different forms of files, data, or evidence as the ultimate looks of events. By the interface of media and network platforms and special visual field of the viewer, class body space exists and extends out as thin as sebum layer, extremely soft and delicate with real full tension. The physical space of sebum layer of confuses the fact that physical objects exist, needs to be established under a perceived consensus. As at the scene, the existing concepts and boundaries of physical perceptions are blurred. Sebum layer physical simulation shapes the “Topical-Similarity" immersing, leading the contemporary social practice communities, groups, network users with a kind of illusion without the presence, i.e. a non-real illusion. From the investigation and discussion of literatures, digital movies editing manufacture and produce the variability characteristics of time (for example, slices, rupture, set, and reset) are analyzed. Interactive eBook has an unique interaction in "Waiting-Greeting" and "Expectation-Response" that makes the operation of image narrative structure more interpretations functionally. The works of digital editing and interactive technology are combined and further analyze concept and results. After digitization of Interventional Imaging and interactive technology, real events exist linked and the media handing cannot be cut relationship through movies, interactive art, practical case discussion and analysis. Audience needs more rational thinking about images carried by the authenticity of the text.

Keywords: sebum layer, topical and similarity, interactive technology, image narrative

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Authors: Nadia Ben Youssef, Aicha Bouzid

Abstract:

Gradient estimation is one of the most fundamental tasks in the field of image processing in general, and more particularly for color images since that the research in color image gradient remains limited. The widely used gradient method is Di Zenzo’s gradient operator, which is based on the measure of squared local contrast of color images. The proposed gradient mechanism, presented in this paper, is based on the principle of the Di Zenzo’s approach using quaternion representation. This edge detector is compared to a marginal approach based on multiscale product of wavelet transform and another vector approach based on quaternion convolution and vector gradient approach. The experimental results indicate that the proposed color gradient operator outperforms marginal approach, however, it is less efficient then the second vector approach.

Keywords: gradient, edge detection, color image, quaternion

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Authors: Okhee Woo

Abstract:

Purpose: With increasing concern of individual radiation exposure doses, studies analyzing radiation dosage in breast imaging modalities are required. Aim of this study is to compare radiation dosage and image quality between digital breast tomosynthesis (DBT) and full-field digital mammography (FFDM). Methods and Materials: 303 patients (mean age 52.1 years) who studied DBT and FFDM were retrospectively reviewed. Radiation dosage data were obtained by radiation dosage scoring and monitoring program: Radimetrics (Bayer HealthCare, Whippany, NJ). Entrance dose and mean glandular doses in each breast were obtained in both imaging modalities. To compare the image quality of DBT with two-dimensional synthesized mammogram (2DSM) and FFDM, 5-point scoring of lesion clarity was assessed and the better modality between the two was selected. Interobserver performance was compared with kappa values and diagnostic accuracy was compared using McNemar test. The parameters of radiation dosages (entrance dose, mean glandular dose) and image quality were compared between two modalities by using paired t-test and Wilcoxon rank sum test. Results: For entrance dose and mean glandular doses for each breasts, DBT had lower values compared with FFDM (p-value < 0.0001). Diagnostic accuracy did not have statistical difference, but lesion clarity score was higher in DBT with 2DSM and DBT was chosen as a better modality compared with FFDM. Conclusion: DBT showed lower radiation entrance dose and also lower mean glandular doses to both breasts compared with FFDM. Also, DBT with 2DSM had better image quality than FFDM with similar diagnostic accuracy, suggesting that DBT may have a potential to be performed as an alternative to FFDM.

Keywords: radiation dose, DBT, digital mammography, image quality

Procedia PDF Downloads 341