International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1110
Galois field-based approach for rotation and scale invariant texture classification
Статья научная
In this paper, a novel Galois Field-based approach is proposed for rotation and scale invariant texture classification. The commutative and associative properties of Galois Field addition operator are useful for accomplishing the rotation and scale invariance of texture representation. Firstly, the Galois field operator is constructed, which is applied to the input textural image. The normalized cumulative histogram is constructed for Galois Field operated image. The bin values of the histogram are considered as rotation and scale invariant texture features. The classification is performed using the K-Nearest Neighbour classifier. The experimental results of the proposed method are compared with that of Rotation Invariant Local Binary Pattern (RILBP) and Log-Polar transform methods. These results obtained using the proposed method are encouraging and show the possibility of classifying texture successfully irrespective of its rotation and scale.
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General Research on Automatic Image Shrinking in the Wireless Capsule Endoscopy
Статья научная
In this paper we propose a method of image shrinking without loss of the quality with regard to a modern field in medical research - wireless capsule endoscopy. The wireless capsule is a small devise with a size of 1,5x2 cm. That means that the memory chip on which the results of the examination of the gastrointestinal tract are stored should also be tiny. The scope of the device imposes strict restrictions on the shrinking scheme that should be taken into consideration. This article gives a brief overview of existing data shrinking methods and their application possibilities, namely triplets coding of binary combinations, conversion combination MTF (move-to-front) and Rice coding. Taking into consideration the specificity of application the more promising is the third way of image shifting without loss. This method is based on modified shrinking algorithms mentioned above. According to the carried out experiments the overall scheme of the device was developed. This scheme implements the most efficient method of coding. The described algorithm allows image shrinking on 20%. That means that endoscopic capsule may work significantly longer.
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General Research on Image Segmentation Algorithms
Статья научная
As one of the fundamental approaches of digital image processing, image segmentation is the premise of feature extraction and pattern recognition. This paper enumerates and reviews main image segmentation algorithms, then presents basic evaluation methods for them, and finally discusses the prospect of image segmentation. Some valuable characteristics of image segmentation come out based on a large number of comparative experiments.
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Generation of Undistorted Photomosaics of Cylindrical Vesicular Basalt Specimens
Статья научная
Photographic documentation of prepared rock core specimens may be required for scientific studies. For specimens that have surface features which vary circumferentially, it is advantageous to have a single photomosaic of the specimen surface rather than a series of surface photographs. A technique to develop a photomosaic from a series of overlapping images of prepared vesicular basalt core specimens is presented. The overlapping images of the specimen surface are subjected to an initial cropping, a geometric transformation, an intensity interpolation, a final cropping, and an image stitching algorithm. The final result is an undistorted photomosaic of the entire specimen surface. All steps except the initial cropping are implemented within MATLAB®.
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Статья научная
The introduction of remote sensing techniques had lead us into a new race of advanced data processing applications. The analysis ready data is also a part of it which is generated at the producer end to facilitate its user to directly go on to the application part. This paper highlights the generation, processing and cloud applications of the Analysis Ready Data (ARD) using ISRO's Satellites Resourcesat-2 and Resourcesat-2A's LISS-3 sensor data. The proposed work includes use of terrain corrected data for generating Radiance, Top of Atmosphere (ToA) Reflectance, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Time series analysis with pixel level Quality Assessment (QA) for all the generated data products. A graphical user interface has been developed for online ordering of data by the user. This paper also highlights the implementation of the developed application in cloud platform using the cloud computing model, Platform as a Service (PaaS).This facilitates the users to generate the ARD products from any device, facilitating a quick and all time available transmission rate for the customers.
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Genetic Algorithm Based Image Steganography for Enhancement of Concealing Capacity and Security
Статья научная
This paper proposes a Genetic Algorithm based steganography for enhancement of embedding capacity and security. Steganography is a method to provide secret communication between sender and receiver by concealing message in cover image. LSB bit encoding method is that the simplest encoding method to cover secret message in color pictures and grayscale pictures. Steganalysis is a method of detecting secret message hidden in a cover image. RS steganalysis is one of the most reliable steganalysis which performs statistical analysis of the pixels to successfully detection of hidden message in an image. This paper presents a secured steganography method using genetic algorithm to protect against the RS attack in color images. The proposed steganography scheme embeds message in integer wavelet transform coefficients by using a mapping function. This mapping function based on GA in an 8x8 block on the input cover color image. After embedding the message optimal pixel adjustment process is applied. By applying the OPAP the error difference between the cover image and stego image is minimized. Frequency domain technique is used to increase the robustness of proposed method. Use of IWT prevents the floating point precision problems of the wavelet filter. GA is used to increase the hiding capacity of image and maintains the quality of image. Experimental results are shows that the proposed steganography method is more secured against RS attack as compared to existing methods. Result showed that Peak signal to noise ratio and image utilization, 49.65 db and 100% respectively.
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Genetic Algorithm For Designing QMF Banks and Its Application In Speech Compression Using Wavelets
Статья научная
In this paper, real-coded genetic algorithm (GA) is used for designing two-channel quadrature mirror filter (QMF) banks based on the Kaiser Window. The shape of the Kaiser window and the cutoff frequency of the prototype filter are optimized using a simple GA. The optimized QMF banks are exploited as mother wavelets for speech compression based on discret wavelet transform (DWT). The simulation results show the efficiency of the GA for designing QMF banks using adjustable windows length and especially for optimizing wavelet filters used in speech compression based on wavelets. In addition, a comparative of performance of the developed wavelets filters using GA and others known wavelets is made in term of objective criteria (CR, SNR, PSNR, and NRMSE). The simulation results show that the optimized wavelets filters outperform others wavelets already exist used for speech compression.
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Genetic Algorithm Framework for Spike Sorting
Статья научная
Spike sorting involves clustering spikes according to the similarity of their shapes. Usually the sorting procedure is carried out by extracting appropriate features of neuronal spikes. In this study a new spike sorting procedure based on genetic algorithm is developed which contains two distinct phases. In the first phase a B-spline curve is fitted to each spike waveform and then the optimal features are selected from parameters of fitted B-spline curves. The genetic algorithm is used for searching the optimal parameters of B-spline curve in a way that the curve fitting error is minimized. In the second phase, clustering of spikes based on extracted features is performed by applying genetic algorithm. In this phase the fitness function is defined in a manner that both spatial distances between objects in the feature space and their similarity in the real world are considered. The proposed sorting method is tested on the real neural dataset which firstly are classified by an expert human. The results show that the proposed method based on genetic algorithm framework gives fewer errors of clustering in comparison with some other approaches currently used in the clustering purposes.
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Glaucoma Detection and Severity Diagnosis from Fundus Images Using Dual CNN Architectures
Статья научная
Glaucoma, a series of progressive eye illnesses, is a primary worldwide health concern. Glaucoma, sometimes known as the "silent thief of sight," progressively affects the optic nerve, resulting in permanent vision loss and, in extreme instances, blindness. It is essential to recognize glaucoma in its earlier stages so that patients can receive treatment sooner and prevent further vision loss. An effective method for detecting glaucoma by analyzing retinal images with the assistance of a deep learning strategy is presented as a potential solution in this article. The framework presented for detecting glaucoma comprises two modules that rely on one another: the Retinal Image Classification Module (RICM) and the Retinal Image Diagnosis Module (RIDM). The retinal image is classified as either a normal or a glaucoma retinal image by the RICM module, which uses the CNN classifier. The RIDM detects the neuro rim region from the glaucoma retinal image by segmenting OD and OC, and the Dual Functional CNN (DFCNN) classifier is proposed to diagnose the severity stages of the glaucoma image based on the feature patterns that are extracted from the neuroretinal rim in the glaucoma image. Both low- and high-resolution retinal image datasets, known as HRF and PAPILA, are utilized in this study to investigate the proposed approaches for glaucoma identification and severity estimate. Compared to other methods considered to be state-of-the-art, the simulation's findings show that it is successful. Ophthalmologists benefit from the suggested model since it assists them in effectively recognizing glaucoma in patients, which in turn allows for improved diagnosis and the prevention of premature vision loss.
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Graph Abstraction Based on Node Betweenness Centrality
Статья научная
There are many graph abstraction methods that are existed as solutions for problems of graphs visualization. Visualization problems include edge crossings and node occlusions that hide the potential existed patterns. The aim of this research is to abstract graphs using one of network analysis metrics which is node betweenness centrality. Betweenness centrality is calculated for all graph nodes. Graph abstraction is done by removing the nodes with their attached edges such that they have betweenness centrality lower than a certain examined threshold. Experiments have been conducted and results show that the proposed abstraction method can effectively reduce the complexity of the graph visualization in term of node degree. Modularity of clusters after filtering is decreased but the final graph visualization is simpler and more informative.
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Graph Modeling based Segmentation of Handwritten Arabic Text into Constituent Sub-words
Статья научная
Segmentation of Arabic text is a major challenge that shall be addressed by any recognition system. The cursive nature of Arabic writing makes it necessary to handle the segmentation issue at various levels. Arabic text line can be viewed as a sequence of words which in turn can be viewed as a sequence of sub-words. Sub-words have the frequently encountered intrinsic property of sharing the same vertical space which makes vertical projection based segmentation technique inefficient. In this paper, the task of segmenting handwritten Arabic text at sub-word level is taken up. The proposed algorithm is based on pulling away the connected components to overcome the impossibility of separating them by vertical projection based approach. Graph theoretic modeling is proposed to solve the problem of connected component extraction. In the sequel, these components are subjected to thorough analysis in order to obtain the constituent sub-words where a sub-word may consist of many components. The proposed algorithm was tested using variety of handwritten Arabic samples taken from different databases and the results obtained are encouraging.
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Graphic representations and frequency parameters of heart sound signals
Статья научная
Sounds produced by acoustic activity of the heart are series (sequences) of quasi-periodic events which are repeated throughout life, one period (cycle) of these events lasts less than one second. The advancements in technology have enabled us to create various tools for audio and graphic representations of these events. Physicians, by using such tools, can more accurately determine diagnosis by interpretation of heart sound and/or by visual interpretation of graphic displays of heart sounds. This paper presents frequency parameters and graphic illustrations of heart sound signals for two groups of heart murmurs: innocent Still’s murmur and pathologic heart murmur of Ventricular Septal Defect (VSD). Also, on behalf of the frequency analysis of acoustic cardiac signals with Still’s murmur was given a medical explanation of cause and origin of Still’s murmur.
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Grayscale Image Colorization Method Based on U-Net Network
Статья научная
A colorization method based on a fully convolutional neural network for grayscale images is presented in this paper. The proposed colorization method includes color space conversion, grayscale image preprocessing and implementation of improved U-Net network. The training and operating of the U-Net network take place for images represented in the space of the Lab color model. The trained U-Net network integrates realistic colors (generate data of a and b components) into grayscale images based on L-component data of the Lab color model. Median cut method of quantization is applied to L-component data before the training and operating of the U-Net network. Logistic activation function is applied to normalized results of convolution layers of the U-Net network. The proposed colorization method has been tested on ImageNet database. The evaluation results of the proposed method according to various parameters are presented. Colorization accuracy by the proposed method reachers more than 84.81%. The colorization method proposed in this paper is characterized by optimized architecture of convolution neural network that is able to train on a limited image set with a satisfactory training duration. The proposed colorization method can be used to improve the image quality and restoring data in the development of computer vision systems. The further research can be focused on the study of a technique of defining optimal number of the gray levels and the implementation of the combined quantization methods. Also, further research can be focused on the use of HSV, HLS and other color models for the training and operating of the neural network.
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Hand gesture interface based on skin detection technique for automotive infotainment system
Статья научная
The infotainment systems are acquiring wide popularity in automotive domain. These systems are manually operated and require physical contact for interaction. In the present scenario the consumers are demanding a smart phone like experience from the vehicle’s console unit. Thus, there is a wide scope for enhancing the mode of interaction and introducing a touch less interface system. The gesture interface approach is a new possibility in this domain. In this method the skin detection plays an important role in segmenting hand region. There are various approaches for hand detection based on skin region identification. The fundamental challenge in skin detection lies in various factors such as illumination, background, camera characteristics, and ethnicity. The gesture detection in automotive environment is further challenging task due to significant impact of wide variation in light, continuous changing background and hindrance caused by vehicle movement. In the present work, design of hand gesture interface for rear seat passenger is discussed. The interface is developed to interact with media player application of infotainment system based on efficient skin detection technique. The objectives of work include study of various skin color modeling, analysis of combination of color spaces, study of hand feature extraction and recognition techniques, design of lab setup for experimentation, implementing gesture interface to access media player application of an infotainment system. The developed prototype lab set up is used for analyzing the skin classifiers and designing a Hi-Vi skin classifier. Further, a user friendly interface is developed using Hi-Vi algorithm with multimode interface features. The evaluation of developed system shows high TPR and low FPR.
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Hardware Design and Simulation of Sobel Edge Detection Algorithm
Статья научная
In this paper, a hardware system for Sobel Edge Detection Algorithm is designed and simulated for a 128 pixel, 8-bit monochrome line-scan camera. The system is designed to detect objects as they move along a conveyor belt in a manufacturing environment, the camera will observe dark objects on a light conveyor belt. The edge detector is required to detect horizontal and vertical edges using Sobel edge detection method. The Sobel operator requires 3 lines and takes 3 pixels per line, thus using a 3×3 input block to process each pixel. The centre pixel of the 3×3 pixel block can be classified as an edge point or otherwise by thresholding the value from the operator. The FPGA based Sobel edge detector is designed and simulated using Altera Quartus II 8.1 web edition by targeting Cyclone II development boards.
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Heart disease detection using predictive optimization techniques
Статья научная
Health care is a major research domain needed instantaneous solutions. Due to the digitalization of data in each and every domain it is becoming tedious to store and analysis. So, the demand of proficient algorithms for health care data analysis is also increasing. Predictive analytics is the major demand from the health care community to the computing researches in order to predict and reduce the potential health catastrophes. Parallel research attempts are made to predict the possibilities of the disease on the different health care domains at various regions. However, those attempts are limited and not remarkable to achieve the desired outcomes. Recently, in the field of data analytics; Machine Learning techniques became popular in generating optimized solutions with effective data processing capabilities. Henceforth, this research work considers the heart disease analysis using machine learning techniques to determine the disease severity levels. Experiments are made on UCI heart disease dataset and our results shows 92% accuracy the heart severity detection.
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Herbs Recognition Based on Android using OpenCV
Статья научная
Herbs are used in traditional medicine. There are so many herbs are spread across the world, it is difficult to memorize it all. This paper describes an android application to recognize herbs by their leaf characteristics (shape, veins, and keypoints). Shape and veins of leaves are recognized by Invariant Moment Method as the feature extraction. City Block Distance used to calculate the distance between the features. Whereas for detection and keypoints extraction using Oriented FAST and Rotated BRIEF on OpenCV library. This keypoints distance calculation using Brute-Force Hamming. Matching is done by calculating the shortest distance between test image and reference image. If the result is less than or equal to threshold then image is match. Experiment result show this application can achieve 79% of success rate by using keypoints. This result is influenced by glossy leaf surface, so there is many reflected light that become noise.
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High Payload Watermarking using Residue Number System
Статья научная
Information hiding or data hiding, also known as watermarking, has become a part and parcel of covert communication and copyright protection. Maximizing watermark payload is a major challenge for watermark researchers. To overcome this issue, we have proposed a new color image watermarking technique, using residue number system (RNS). RNS refers to a large integer using a set of smaller integers which relies on the Chinese remainder theorem of modular arithmetic for its operation. The proposed method takes pixel values from three watermark images and embeds them into the main cover image. Experimental results presented in this paper shows that the watermark can be successfully embedded and extracted from an image, without distorting the original image using the proposed technique. The high peak signal to noise ratio (PSNR) and payload values claims the robustness of the proposed method.
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High Resolution Identification of Wind Turbine Faults Based on Optimized ESPRIT Algorithm
Статья научная
Many researchers employ ESPRIT method as robust detection tool to identify fault frequency and amplitude in induction machines. However, this algorithm presents some limitation in terms of computational time and required data memory size. This drawback makes this technology unusable in real time diagnosis application. In the fact that wind turbine machine necessitates an on-line regular maintenance to guarantee an acceptable lifetime and to maximize its productivity. Thus, an improved version of ESPRIT-TLS method has been proposed and simulated to extract accurately fault frequencies and their magnitudes from the wind stator current with minimum computation time and less memory cost. The proposed approach has been evaluated by computer simulations under many fault kinds. Study outcomes prove the benefits and the performance of Fast-ESPRIT.
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Статья научная
This paper presents a high speed image compression based on the combination of modified self-organizing maps and Back-Propagation neural networks. In the self-organizing model number of the neurons are in a flat topology. These neurons in interaction formed self-organizing neural network. The task this neural network is estimated a distribute function. Finally network disperses cells in the input space until estimated probability density of inputs. Distribute of neurons in input space probability is an information compression. So in the proposed method first by Modified Self-Organizing Feature Maps (MSOFM) we achieved distributed function of the input image by a weight vector then in the next stage these information compressed are applied to back-propagation algorithm until image again compressed. The performance of the proposed method has been evaluated using some standard images. The results demonstrate that the proposed method has High-speed over other existing works.
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