Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
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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Automatic Detection of Surface Defects on Citrus Fruit based on Computer Vision Techniques
Статья научная
In this paper, we present computer vision based technique to detect surface defects of citrus fruits. The method begins with background removal using k-means clustering technique. Mean shift segmentation is used for fruit region segmentation. The candidate defects are detected using threshold based segmentation. In this stage, it is very difficult to differentiate stem-end from actual defects due to similarity in appearance. Therefore, we proposed a novel technique to differentiate stem-end from actual defects based on the shape features. We conducted experiments on our citrus data set captured in controlled environment. The experiment results demonstrate that our technique outperforms the existing techniques.
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Статья научная
This paper describes automatic detection and classification of visual symptoms affected by fungal disease. Algorithms are developed to acquire and process color images of fungal disease affected on commercial crops like chili, cotton and sugarcane. The developed algorithms are used to preprocess, segment, extract and reduce features from fungal affected parts of a crop. The feature extraction is done with discrete wavelet transform (DWT) and features are further reduced by using Principal component analysis (PCA). Reduced features are then used as inputs to classifiers and tests are performed to classify image samples. We have used statistical based Mahalanobis distance and Probabilistic neural network (PNN) classifiers. The average classification accuracies using Mahalanobis distance classifier are 83.17% and using PNN classifier are 86.48%
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Automatic Image Segmentation Base on Human Color Perceptions
Статья научная
In this paper we propose a color image segmentation algorithm based on perceptual color vision model. First, the original image is divide into image blocks which are not overlapped; then, the mean and variance of every image back was calculated in CIEL*a*b* color space, and the image blocks were divided into homogeneous color blocks and texture blocks by the variance of it. The initial seed regions are automatically selected depending on calculating the homogeneous color blocks' color difference in CIEL*a*b* color space and spatial information. The color contrast gradient of the texture blocks need to calculate and the edge information are stored for regional growing. The fuzzy region growing algorithm and coloredge detection to obtain a final segmentation map. The experimental segmentation results hold favorable consistency in terms of human perception, and confirm effectiveness of the algorithm.
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Automatic Recognition and Counterfeit Detection of Ethiopian Paper Currency
Статья научная
Currency recognition is a technology used to identify currencies of various countries. The use of automatic methods of currency recognition has been increasing due its importance in many sectors such as vending machine, railway ticket counter, banking system, shopping mall, currency exchange service, etc. This paper describes the design of automatic recognition of Ethiopian currency. In this work, we propose hardware and software solutions which take images of an Ethiopian currency from a scanner and camera as an input. We combined characteristic features of currency and local feature descriptors to design a four level classifier. The design has a categorization component, which is responsible to denominate the currency notes into their respective denomination and verification component which is responsible to validate whether the currency is genuine or not. The system is tested using genuine Ethiopian currencies, counterfeit Ethiopian currencies and other countries' currencies. The denomination accuracy for genuine Ethiopian currency, counterfeit currencies and other countries' currencies is found to be 90.42%, 83.3% and 100% respectively. The verification accuracy of our system is 96.13%.
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Automatic Speech Segmentation Based On Audio and Optical Flow Visual Classification
Статья научная
Automatic speech segmentation as an important part of speech recognition system (ASR) is highly noise dependent. Noise is made by changes in the communication channel, background, level of speaking etc. In recent years, many researchers have proposed noise cancelation techniques and have added visual features from speaker’s face to reduce the effect of noise on ASR systems. Removing noise from audio signals depends on the type of the noise; so it cannot be used as a general solution. Adding visual features improve this lack of efficiency, but advanced methods of this type need manual extraction of visual features. In this paper we propose a completely automatic system which uses optical flow vectors from speaker’s image sequence to obtain visual features. Then, Hidden Markov Models are trained to segment audio signals from image sequences and audio features based on extracted optical flow. The developed segmentation system based on such method acts totally automatic and become more robust to noise.
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