International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1110
Application of Texture Characteristics for Urban Feature Extraction from Optical Satellite Images
Статья научная
Quest of fool proof methods for extracting various urban features from high resolution satellite imagery with minimal human intervention has resulted in developing texture based algorithms. In view of the fact that the textural properties of images provide valuable information for discrimination purposes, it is appropriate to employ texture based algorithms for feature extraction. The Gray Level Co-occurrence Matrix (GLCM) method represents a highly efficient technique of extracting second order statistical texture features. The various urban features can be distinguished based on a set of features viz. energy, entropy, homogeneity etc. that characterize different aspects of the underlying texture. As a preliminary step, notable numbers of regions of interests of the urban feature and contrast locations are identified visually. After calculating Gray Level Co-occurrence matrices of these selected regions, the aforementioned texture features are computed. These features can be used to shape a high-dimensional feature vector to carry out content based retrieval. The insignificant features are eliminated to reduce the dimensionality of the feature vector by executing Principal Components Analysis (PCA). The selection of the discriminating features is also aided by the value of Jeffreys-Matusita (JM) distance which serves as a measure of class separability Feature identification is then carried out by computing these chosen feature vectors for every pixel of the entire image and comparing it with their corresponding mean values. This helps in identifying and classifying the pixels corresponding to urban feature being extracted. To reduce the commission errors, various index values viz. Soil Adjusted Vegetation Index (SAVI), Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) are assessed for each pixel. The extracted output is then median filtered to isolate the feature of interest after removing the salt and pepper noise present, if any. Accuracy assessment of the methodology is performed by comparing the pixel-based evaluation on the basis of visual assessment of the image and the resultant mask image. This algorithm has been validated using high resolution images and its performance is found to be satisfactory.
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Статья научная
Proteomic analysis is a powerful technology to enhance our insight into the pathogenesis, biomarkers and prevention of disease. Two-dimensional polyacrylamide gel electrophoresis (2-DE) is an important proteomics tool, where thousands of protein spots can be visualized, resulting in a global view of the state of a proteome. Viral infection will modify the patterns of host cell protein expression, which can affect the normal physiological function of host cell and determine viral pathogenic progress and consequence. To uncover host cellular responses in the early stage of classical swine fever virus infection, a proteomic analysis was conducted using 2DE followed by MALDI-TOF-TOF identification. Altered expression of 21 protein spots in infected pk-15 cells at 24 h p.i. were identified in 2D gels, with 13 of these being characterized by MALDI-TOF-MS/MS. These proteins function in cytoskeletal, energy metabolism, nucleic acid/processing, and cellular stress. The expression alteration of these proteins presents the changes in physiological functions of host cells and provides a clue for further understanding of the mechanisms of CSFV infection and pathogenesis.
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Application of the Chaotic Ergodicity of Standard Map in Image Encryption and Watermarking
Статья научная
Thanks to the exceptionally good properties in chaotic systems, such as sensitivity to initial conditions and control parameters, pseudo-randomness and ergodicity, chaos-based image encryption algorithms have been widely studied and developed in recent years. Standard map is chaotic so that it can be employed to shuffle the positions of image pixels to get a totally visual difference from the original images. This paper proposes two novel schemes to shuffle digital images. Different from the conventional schemes based on Standard map, we disorder the pixel positions according to the orbits of the Standard map. The proposed shuffling schemes don’t need to discretize the Standard map and own more cipher leys compared with the conventional shuffling scheme based on the discretized Standard map. The shuffling schemes are applied to encrypt image and disorder the host image in watermarking scheme to enhance the robustness against attacks. Experimental results show that the proposed encryption scheme yields good secure effects. The watermarked images are robust against attacks as well.
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Application-Oriented Farsi ALPD Using Deterministic Edge Clustering
Статья научная
In this paper a new application-oriented method for automatic Farsi license plate detection (ALPD), based on morphology and a modified edge clustering algorithm is proposed. Access control (AC), law enforcement (LE), and road patrol (RP) are mainly three applications for ALPD. After image enhancement by preprocessing, the edge statistics analysis and the morphology filter are used to decrease the search regions and remove the unwanted edges. Then the expectation-maximization (E-M) algorithm is used to estimate the corresponding Gaussian components for edges of remained regions. In this way the results of edge clustering and Gaussian components estimation are deterministic whereas the processing time in comparison with similar approaches in literature, is decreased significantly. Candidate regions are obtained by applying application-oriented thresholds to the properties of estimated Gaussian components. Finally for each candidate region, the maximally stable extremal region (MSER) detector is used to detect character-like regions and then select the region(s) of interest containing license plates. The proposed method is evaluated by using a database which includes images for the three groups AC, LE and RP applications, whereas some images suffer of being low quality, low contrast and blur and some images have complex background through existing multiple license plates. The experimental results show that our proposed method is reliable for images of different quality and illumination condition and it is able to detect the rotated and skewed license plates even in images containing multiple license plates and complex backgrounds.
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Applications of Barcode Images by Enhancing the Two-Dimensional Recognition Rate
Статья научная
The paper not only proposed the latest Two-Dimensional Barcodes Image-processing Module, but also captured the smallest camera screens (320 240) with different focal distances and tried to find out “Finder Pattern” for positioning images. Further, use CROBU (Conversion Ratio of Basic Unit) the thesis proposed to convert 2-D barcodes into 1-pixel ratio to match images before judging recognition rate of 2-D barcodes through matching. Normally speaking, 2-D barcodes are deciphered and recognized by software while the thesis recognizes 2-D barcodes and enhances implementation speed up to 10-cm accurate max. using image matching. The 2-D barcodes image-processing module the thesis proposed does capture and standardize image with complicated background or raw edge, which enhances 2-D barcodes recognition rate. The main point of this study is to construct a platform to manage or suggest nutrients human body needs. The Quick Response Code image of 2-D barcodes represents vitamin and calories information. 2-D barcodes taken instantly by MATLAB and CCD camera can be used to list nutrients from foods you eat recently and suggest what else you should eat for the purpose of health management.
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Applied Computational Engineering in Magnetic Resonance Imaging: A Tumor Case Study
Статья научная
This paper solves the biomedical engineering problem of the extraction of complementary and/or additional information related to the depths of the anatomical structures of the human brain tumor imaged with Magnetic Resonance Imaging (MRI). The combined calculation of the signal resilient to interpolation and the Intensity-Curvature Functional provides with the complementary and/or additional information. The steps to undertake for the calculation of the signal resilient to interpolation are: (i) fitting a polynomial function to the signal, (ii) the calculation of the classic-curvature of the signal, (iii) the calculation of the Intensity-Curvature term before interpolation of the signal, (iv) the calculation of the Intensity-Curvature term after interpolation of the signal, (v) the solution of the equation of the two aforementioned Intensity-Curvature terms of the signal provides with the signal resilient to interpolation. The Intensity-Curvature Functional is the result of the ratio between the two Intensity-Curvature terms before and after interpolation. Because of the fact that the signal resilient to interpolation and the Intensity-Curvature Functional are derived through the process of re-sampling the original signal, it is possible to obtain an immense number of images from the original MRI signal. This paper shows the combined use of the signal resilient to interpolation and the Intensity-Curvature Functional in diagnostic settings when evaluating a tumor imaged with MRI. Additionally, the Intensity-Curvature Functional can identify the tumor contour line.
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Applying Aging Effect on Facial Image with Multi-domain Generative Adversarial Network
Статья научная
Face Aging is an important and challenging application in computer vision. This is an application of conditional image generation. Until recently generative model was not good enough to generate considerable good resolution images. A generative model called generative adversarial network has introduced impressive capabilities in generating realistic images in both unconditional and conditional settings. Still, the task of generating images of different age conditioning on a given image is a very challenging task. Because there are two constraints to satisfy here in the generated images. The generated image must preserve the identity of the person in the source image and the image must have the features of the target age. In this work, we have applied the generative adversarial network in conditional settings along with custom loss function to satisfy the two mentioned constraints. The experiment has shown improved performance both in preserving the person’s identity and classification accuracy of generated images in the target class compared to previous known approach to this problem.
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Applying Quaternion Fourier Transforms for Enhancing Color Images
Статья научная
The Fourier transforms play a critical role in a broad range of image processing applications, including enhancement, analysis, restoration, and compression. Until recently, it was common to use the conventional methods to deal with colored images. These methods are based on RGB decomposition of the colored image by separating it into three separate scalar images and computing the Fourier transforms of these images separately. The computing of the Hypercomplex 2D Fourier transform of a color image as a whole unit has only recently been realized. This paper is concerned with frequency domain noise reduction of color images using quaternion Fourier transforms. The approach is based on obtaining quaternion Fourier transform of the color image and applying the Gaussian filter to it in the frequency domain. The filtered image is then obtained by calculating the inverse quaternion Fourier transforms.
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Approximating Spline filter: New Approach for Gaussian Filtering in Surface Metrology
Статья научная
This paper presents a new spline filter named approximating spline filter for surface metrology. The purpose is to provide a new approach of Gaussian filter and evaluate the characteristics of an engineering surface more accurately and comprehensively. First, the configuration of approximating spline filter is investigated, which describes that this filter inherits all the merits of an ordinary spline filter e.g. no phase distortion and no end distortion. Then, the approximating coefficient selection is discussed, which specifies an important property of this filter-the convergence to Gaussian filter. The maximum approximation deviation between them can be controlled below 4.36% , moreover, be decreased to less than 1% when cascaded. Since extended to 2 dimensional (2D) filter, the transmission deviation yields within -0.63% : +1.48% . It is proved that the approximating spline filter not only achieves the transmission characteristic of Gaussian filter, but also alleviates the end effect on a data sequence. The whole computational procedure is illustrated and applied to a work piece to acquire mean line whereas a simulated surface to mean surface. These experimental results indicate that this filtering algorithm for 11200 profile points and 2000 × 2000 form data, only spends 8ms and 2.3s respectively.
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Area Optimized High Throughput IDMWT/DMWT Processor for OFDM on Virtex-5 FPGA
Статья научная
OFDM is one of the most popular modulation techniques that is been widely used in most of the wireless and wired communication links. The OFDM architecture consists of QAM modulator and orthogonal frequency modulator. In this work we propose DMWT based orthogonal frequency modulator for achieving higher BER. The IDMWT architecture is designed considering N=4, thus the preprocessing unit converts the QAM samples of N to 2N and is modulated using DMWT filters. The filtered output is further transmitted and is received at the receiver. During the post processing, N samples are extracted by use of DMWT demodulation technique. The complex architecture of IDMWT and DMWT are reduced for its complexity and speed by the modified architecture. The DMWT architecture is modified for FPGA implementation improving the area, power and speed performances. The modified DMWT architecture is implemented on VirtexII pro FPGA which operates at 300MHz frequency and occupies area of less than 1%, with power consumption less than 28mW. The proposed design is suitable for real time and low power applications.
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Area Reduction in Redundancy Module for an ECC Based Fault Tolerance in Digital Filters
Статья научная
Due to the wide usage of digital filters in communication systems, reliability and area has to be considered and deficiency tolerant channel usage are required. Throughout the decades, there are number of techniques that have been proposed to achieve fault tolerance. As the number of parallel filters are increasing in any digital device, the redundancy module should also be small in size. In this paper, a simple technique of constant multiplication reduction method is introduced in the Error Correction Codes (ECC) based parallel filters in order to reduce the size of the redundant module. Main agenda is to reduce the size of the redundant module by not affecting the functionalityof the system. The proposed scheme is coded in HDL and simulation results are obtained by using Xilinx 12.1i. The presented result shows that the slices can be reduced and hence the size. As a result of reduction in size, the optimization of area can also be concluded.
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Arterial Parameters and Elasticity Estimation in Common Carotid Artery Using Deep Learning Approach
Статья научная
The risk of cardiovascular diseases is growing worldwide, and its early detection is necessary to reduce the level of risk. Structural parameters of the carotid artery as intima-media thickness and functional parameters such as arterial elasticity are directly associated with cardiovascular diseases. Segmentation of the carotid artery is required to measure the structural parameters and its temporal value that is used to estimate the arterial elasticity. This paper has two primary objectives: (i) Segmentation of the sequence of carotid artery ultrasound to measure temporal value of intima-media thickness and lumen-diameter, and (ii) Young’s modulus of elasticity estimation. The proposed segmentation method uses the contextual feature of the image pattern and is based on multi-layer extreme learning machine auto-encoder network. This segmentation method has two parts: (a) region of interest localization and (b) lumen-intima interface and media-adventitia interface detection at the far wall. ROI localization algorithm divides the ultrasound frame into columns and also divides each column into overlapping blocks, ensuring that every column has a region of interest block. A multi-layer extreme learning machine with auto-encoder is trained with labelled data and in testing; system classifies the blocks into ‘region of interest’ and ‘non-region of interest’. Pixels belonging to the region of interest are classified in the first part and a similar network-based method is proposed for lumen-intima and media-adventitia interface detection at the near wall of the carotid artery. Structural parameter of the artery, intima-media thickness and lumen diameter are measured in a sequence of images of the cardiac cycle. The temporal values of structural parameters are used to estimate the young’s modulus of elasticity.
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Artificial Neural Networks in Fruits: A Comprehensive Review
Статья научная
This review discusses the application of artificial neural networks (ANN) modeling in fruits. It covers all fruits in which ANN modeling has been applied. ANN is quite a new and easy computational modeling approach used for prediction, which has become popular and accepted by food industry, researchers, scientists and students. ANNs have been applied in almost every field of science and technology, viz., speech synthesis & recognition, pattern classification, adaptive interfaces between humans & complex physical systems, clustering, function approximation, image data compression, non-linear system modeling, associative memory, combinatorial optimization, control and several others, as they have proved valuable tools for obtaining the required output. ANN provides an exciting alternative method for solving a variety of problems in different areas of science and engineering. The aim of this communication is to discover the recent advances of ANN technology implemented in fruits, and discuss the critical role that ANN plays in predictive modelling.
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Attention-based deep learning model for image captioning: a comparative study
Статья научная
Image captioning is the description generated from images. Generating the caption of an image is one part of computer vision or image processing from artificial intelligence (AI). Image captioning is also the bridge between the vision process and natural language process. In image captioning, there are two parts: sentence based generation and single word generation. Deep Learning has become the main driver of many new applications and is also much more accessible in terms of the learning curve. Image captioning by applying deep learning model can enhance the description accuracy. Attention mechanisms are the upward trend in the model of deep learning for image caption generation. This paper proposes the comparative study for attention-based deep learning model for image captioning. This presents the basic analyzing techniques for performance, advantages, and weakness. This also discusses the datasets for image captioning and the evaluation metrics to test the accuracy.
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Author Based Rank Vector Coordinates (ARVC) Model for Authorship Attribution
Статья научная
Authorship attribution is one of the important problem, with many applications of practical use in the real-world. Authorship identification determines the likelihood of a piece of writing produced by a particular author by examining the other writings of that author. Most of the research in this field is carried out by using instance based model. One of the disadvantages of this model is that it treats the different documents of each author differently. It produces a matrix per each document of the author, thus creating a huge number of matrices per author, i.e. the dimensionality is very high. This paper presents authorship identification using Author based Rank Vector Coordinates (ARVC) model. The advantage of the proposed ARVC model is that it integrates all the author's profile documents into a single integrated profile document (IPD) and thus overcomes the above disadvantage. To overcome the ambiguity created by common words of authors ARVC model removes the common words based on a threshold. Singular value decomposition (SVD) is used on IPD after removing the common words. To reduce the overall dimension of the matrix, without affecting its semantic meaning a rank-based vector coordinates are derived. The eigenvector features are derived on ARVC model. The present paper used cosine similarity measure for author attribution and carries out authorship attribution on English poems and editorial documents
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Autism Spectrum Disorder Screening on Home Videos Using Deep Learning
Статья научная
Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by difficulty in social interactions, communication, and repetitive behaviors. Protocols like ADOS (Autism Diagnostic Observation Schedule) and ADI-R (Autism Diagnostic Interview Revised) are used by experts to assess the subject’s behavior which is time-consuming. Over the decade, researchers have studied the application of various Machine Learning techniques for ASD diagnosis through facial feature analysis, eye movement tracking, questionnaire analysis, functional magnetic resonance imaging (fMRI) analysis, etc. However, these techniques are not helpful for the parent or guardian of the child to perform an initial screening. This research proposes a novel deep learning model to diagnose ASD using general videos of the subject performing some tasks with the parent/guardian. Since there is no publicly available dataset on ASD videos, a dataset is created by collecting the videos of autistic children performing some activities with parents/guardians from YouTube from different demographic locations. These videos are then converted to skeletal key points to extract the child's engagement and social interaction in a given task. The proposed CNN-LSTM model is trained on 80% of the collected videos and then tested on the remaining 20%. The experiment results on various combinations of pre-trained CNN models and LSTM/BiLSTM show that the proposed model can be used as an initial autism screening tool. Among the different combinations, the MobileNet and Bi-LSTM combo achieved the best test accuracy of 84.95% with 89% precision, recall and F1-score.
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Automated Human Identification Scheme using Ear Biometrics Technology
Статья научная
Biometrics identification methods have proved to be very efficient, more natural and easy for users than traditional methods of human identification. Biometrics methods truly identify humans, not keys and cards they posses or passwords they should remember. Ear on the other hand, has a more uniform distribution of color, so almost all information is conserved when converting the original image into gray scales. We propose the ear as a biometric and investigate it with both 2D and 3D data. The ICP-based algorithm also demonstrates good scalability with size of dataset. These results are encouraging in that they suggest a strong potential for 3D ear shape as a biometric. Multi-biometric 2D and 3D ear recognition are also explored. The proposed automatic ear detection method will integrate with the current system, and the performance will be evaluated with the original one. The investigation of ear recognition under less controlled conditions will focus on the robustness and variability of ear biometrics. Multi-modal biometrics using 3D ear images will be explored, and the performance will be compared to existing biometrics experimental results.
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Automated Pre-Seizure Detection for Epileptic Patients Using Machine Learning Methods
Статья научная
Epilepsy is a neurological disorder resulting from unusual electrochemical discharge of nerve cells in the brain, and EEG (Electroencephalography) signals are commonly used today to diagnose the disorder that occurs in these signals. In this study, it was aimed to use EEG signals to automatically detect pre-epileptic seizure with machine learning techniques. EEG data from two epileptic patients were used in the study. EEG data is passed through the preprocessing stage and then subjected to feature extraction in time and frequency domain. In the feature extraction step 26 features are obtain to determine the seizure time. When the feature vector is analyzed, it is observed that the characteristics of the pre-seizure and non-seizure period are unevenly distributed. A systematic sampling method has been applied for this imbalance. For the balanced data, two test sets with and without Eta correlation are established. Finally, the classification process is performed using the k-Nearest Neighbor classification method. The obtained data are evaluated in terms of Eta-correlated and uncorrelated accuracy, error rate, precision, sensitivity and F-criterion for each channel.
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Automated Quality Inspection of PCB Assembly Using Image Processing
Статья научная
Quality inspection of PCB is a crucial stage in the assembly line as it provides an insight on whether the board works correctly or not. When the inspection is done manually, it is susceptible to human errors and is time consuming. The boards should thus be inspected at every stage of the assembly line and the process should be dynamic. This is achieved in this work through three crucial stages in the assembly line and by replacing the conventional manual inspection by using image processing to obtain a faster and more precise quality inspection. The solder paste inspection consists of pre-processing using blue plane conversion, comparing with the unsoldered board in blue color plane and post processing using overlay. The X-ray inspection basically consists of pre- processing the captured image by RGB to gray conversion with thresholding, comparing with the expected image and post processing using overlay to show the shorts that has occurred along the assembly. The conformal coating inspection uses conversion of the blue intensity emitted off the board under UV light to RGB scale. Each of the algorithms were tested using 48 actual in-production boards from Vinyas IT Pvt Ltd, a PCB assembly company based in Mysore. The processing time of the algorithms were found to be less than 2 seconds with an accuracy of 85.7%. The system was also found to be cost effective over existing systems available in the market.
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Automated paddy variety recognition from color-related plant agro-morphological characteristics
Статья научная
The paper presents an image-based paddy plant variety recognition system to recognize 15 different paddy plant varieties using 18 color-related agro-morphological characteristics. The k-means color clustering method has been used to segment the target regions in the paddy plant images. The RGB, HSI and YCbCr color models have been employed to construct color feature vectors from the segmented images and the feature vectors are reduced using Principal Component Analysis (PCA) technique. The reduced color feature vectors are used as input to back propagation neural network (BPNN) and support vector machine (SVM). The set of six combined agro-morphological characteristics recorded during maturity growth stage has given the highest average paddy plant variety recognition accuracies of 91.20% and 86.33% using the BPNN and SVM classifiers respectively. The work finds application in developing a tool for assisting botanists, Rice scientists, plant breeders, and certification agencies.
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