Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
Online trial room based on human body shape detection
Статья научная
High returning rate of garments products have become a notable problem for online fashion shopping. This problem is partially caused by using different standards for measuring cloth sizes on different websites. In this research, we have designed a set of equipment to capture images of t-shirts of any color and propose an automatic cloth measurement approach using image processing techniques. A method has been introduced to recognize feature points, which has been used to calculate the cloth sizes. The method has provided a useful and efficient tool for cloth measurement. The photographs have been taken in a controlled environment, and then clothes have been categorized with the proportions of the neck, shoulder, chest width, upper waist, lower waist, and length. In this method, we have measured the t-shirt size for men by calculating the chest width and length of men. For this, a dataset has been created in a specific environment. This method has integrated with a web-based application. We have validated our work by calculating RMSE values.
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Статья научная
Failure modeling is an essential component of reliability engineering. Enhanced failure rate modeling techniques are vital to the effective development of predictive and analytical methodologies, demonstration of the engineering procedure, allocation of procedures, design, and control of procedures. However, failure rate modeling has not been given adequate treatment in the literature. The need to investigate failure rate modeling leveraging cutting-edge techniques cannot be overemphasized. This paper proposed and applied a joint support vector regression (SVR) and wavelet transform (WT) approach termed (WT-SVR) to training and learning the call failures rate in wireless system networks. The wavelet transform has been accomplished using the wavelet compression sensing technique. In this technique, the standardized call failure rate data first go through a wavelet filtering transformation matrix. This is followed by separating and outputting the transformed filtered components in the compression phase. Finally, the transformed filtered output components were trained and evaluated using the SVR based on statistical learning theory. The resultant outcome revealed that the proposed WT-SVR learning method is by far better than using only the SVR method for call rate prognostic analysis. As a case in point, the WT-SVR attained STD values of 0.12, 0.21, 2.32, 0.22, 0.90, 0.81 and 0.34 on call failure data estimation compared to the basic SVR that attained higher STD values of 0.45, 0.98, 0.99, 0.46, 1.44, 2.32 and 3.22, respectively.
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Статья научная
This paper applies a neural-network-based approximate dynamic programming (ADP) method, namely, the action dependent heuristic dynamic programming (ADHDP), to an industrial sucrose crystallization optimal control problem. The industrial sucrose crystallization is a nonlinear and slow time-varying process. It is quite difficult to establish a precise mechanism model of the crystallization, because of complex internal mechanism and interacting variables. We developed a neural network model of the crystallization based on the data from the actual sugar boiling process of sugar refinery. The ADHDP is a learningand approximation-based approach which can solve the optimization control problem of nonlinear system. The paper covers the basic principle of this learning scheme and the design of neural network controller based on the approach. The result of simulation shows the controller based on action dependent heuristic dynamic programming approach can optimize industrial sucrose crystallization.
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Статья научная
In this paper, the Series-Parallel (SP) and Total-Cross-Tied (T-C-T) type of conventional solar photovoltaic (SPV) array configurations or topologies are considered for modeling and comparative analysis and the parameters mainly maximum power of array, shading losses, number of interconnections or ties between array SPV modules are compared with the proposed optimal configuration under six partial shading scenarios and one un shaded case. The proposed optimal topology, optimize the number of ties required among PV modules and improves the output power of SPV array as compare to TCT configuration and also minimizes the shading power losses. These optimal interconnections are based on the location of number of shaded modules in the SPV Array. For this study, the Vikram Solar ELDORA 270 PV modules are used for modeling and simulation of SP, TCT and proposed optimal topologies in MATLAB/ Simulink software.
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Optimization in image fusion using genetic algorithm
Статья научная
Day by day, the advancement in sensor technology is increasing which is used for image acquisition. Different sensors can acquire the information of different wavelength. These sensors are not able to capture the complete information from the scene. Thus it is necessary to combine the images from different sensors to produce more informative image. Image fusion is the process of combing the information from input images. According to the application or need, image fusion technique can be used. Number of techniques with varieties of solutions is available for image fusion process. And thus it becomes difficult task to find an optimal solution for image fusion. Genetic algorithm is an optimization technique used for searching solution for large number of complex problems [15]. This paper gives the quality index of image fusion obtained using the combinations of different selection methods and crossover techniques in genetic algorithm. These techniques have been compared using root mean square error to obtain information about relative performance. The experimental result on some standard test images shows that performance parameters i.e. root mean square error (RMSE) and peak signal to noise ratio (PSNR) are good for multifocus and multisensor image fusion.
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Optimization of Graph Coloring to Determine Culinary Tourism in Samarinda
Статья научная
A problem that often arises is that many places to eat are available, making everyone confused to choose a place to eat and takes a long time to decide on where to eat. Because it requires a system and methods that can be applied to recommend places to eat. Application recommendations for places to eat in this final assignment were made to help everyone in finding a place to eat with the same menu choices. The method used is the Graph Tinting Method, with the application development method used is Waterfall consisting of data analysis, technology analysis, system analysis, information analysis, and user analysis. The results of this study are the making of a restaurant determination application that can recommend places to eat with the same menu. Users can enter menus according to their wishes, then the application will recommend places to eat using a simple line coloring algorithm at the point. After processing, the application will be able to display the results of recommendations for restaurants with the same menu.
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Optimization of MUSIC and Improved MUSIC Algorithm to Estimate Direction of Arrival
Статья научная
The signal processing applications are limited with high-resolution signal parameter estimation. Therefore the Direction of Arrival estimation algorithm needs to be effective and efficient in order to improve the performance of smart antennas. This paper presents the simulation for a subspace based DOA estimation algorithm with high resolution. MUSIC (Multiple signal classification) and the IMUSIC (Improved MUSIC) are presented and optimized by varying various parameters. The basic MUSIC algorithm is ineffective in estimating the incoming coherent signals. The new improved MUSIC algorithm overcomes this ineffectiveness and correctly estimates the related signals with improved accuracy. The improved version of MUSIC algorithm is brought about by taking into account the conjugate of the data matrix for MUSIC algorithm and then reconstructing it. The various factors like the number of array elements, number of snapshots, varying the distance between array elements, varying SNR and the difference in arrival angles can bring about better resolutions. The comparisons for MUSIC and Improved MUSIC algorithm are widely discussed.
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Optimization of Matched and Mismatched Filters in Short Range Pulse Radars using Genetic Algorithm
Статья научная
Matched and mismatched filters are considered important parts of a radar signal processing unit. In this paper, we present an approach to optimize the matched filters and mismatched filters in short range pulse radars. For radar, the matched filter coefficients are the complex conjugates of transmitted code. We used binary phase codes as transmitted pulse. The disadvantage of binary phase codes is having high sidelobe levels in the output of correlation function. Thus, we decided to use optimization algorithms for finding binary phase codes with minimum peak sidelobe levels (MPS). After that, we succeeded in producing mismatched filter coefficients (Mis-co) for each code using floating point genetic algorithm (FGA) and we could generate and test the filter coefficients with maximum peak to sidelobe level ratio (PSR). For testing the filter, we plotted ambiguity function for each set of coefficients and tested the filter with Doppler shift.
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Статья научная
Optimization is one of the techniques used in the estimation of projects to obtain the optimal parameter sequence at different levels for the best project conditions, such as size, duration and function points. In this paper, to select the significant process parameter sequence at different levels, a combination of Grey Relational Analysis (GRA) and Taguchi method applied during the estimation. This parameter sequence is essential for the industries in producing quality product at a lower cost. Taguchi method is used to improve the product quality and reduce the cost. Among the various methods of Taguchi as a standard Orthogonal Array (OA) produces better parameters to be considered at different levels. This paper uses L16 Orthogonal Array (OA) whose efficiency is proven in the experimental results. Here, a variant of GRA, GRG has been used to assign grades for projects in the dataset. Finally, the optimized process parameter sequence at different levels is obtained through the application of GRG over L16 Orthogonal Array (OA). In this paper, Grey-Taguchi method is implemented to find out the levels of software process parameters such as Duration, KSLOC, Adjustment Function Points and Raw Function Points necessary for minimizing software effort. Experimental results show that parameter levels suggested by Grey-Taguchi method result in improved GRG, which results in better software effort estimation.
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Optimized Low Power Dual Edge Triggered Flip-flop with Speed Enhancement
Статья научная
This paper gives a novel low-power approach with pulse generating circuits using dual edge triggered flip-flops. By doing so, flip-flop might operate at 1.2Volts, with the novel quick latch and conditional precharging. This paper aims at a new proposed low power dual edge triggered flip-flop with speed enhancement to achieve low power consumption with a shorter delay in power usage, hence, it is well suited for low-power digital system applications. The new proposed low power dual edge triggered flip-flop also aims at comparison with the three DETFF, Static Output Controlled Discharge Flip-Flop (SCDFF), Dual Edge Triggered Static Pulsed Flip-flop (DETSPFF), and Pervious work on Dual Edge Triggered flip-flop, proves to achieves with reduction in numbers of transistors in the stack and increases the number of charge-paths results in a faster operational speed. According to simulation on Spectre simulator, it has been observed that total power consumption of proposed flip flop at 0.67 switching activity is 30.16 % and 27.36 % less than that of previous arts DSPFF and SCDFF respectively. Clock-gated sense-amplifier is incorporated to reduce power consumption at low switching activity. The simulation is done using Cadence tool with 45nm standard CMOS technology.
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Статья научная
This paper investigates the application of adaptive Bacteria Forging Algorithm (BFA) to design optimal controllers for voltage stability of off-grid hybrid power system (OGHPS).Voltage fluctuations will have great impact on the quality of power supply. Voltage rise/drop depends on the surplus / shortage of reactive power in the system, hence it has become extremely important to manage the reactive power balance for voltage control in the off-grid hybrid power system. The off -grid hybrid power system considered in this work as a test system, consist of an Induction generator (IG) for wind power systems, Photo-Voltaic (PV) system with inverter, Synchronous generator (SG) for diesel power generation and composite load. The Over-rated PV inverter has ample amount of reactive power capacity while sourcing PV real power. Two control structures are incorporated, to regulate system voltage. The first control structure is for the reactive power compensation of the system by inverter, by controlling the magnitude of inverter output voltage and the second control structure is for controlling the SG excitation by an automatic voltage regulator (AVR) and hence the load voltage. Both control structures have proportional-integral (PI) controller. Both control loops are coordinated by tuning their parameters optimally and simultaneously using an adaptive Bacterial forging optimization algorithm. Small signal model of all components of OGHPS is simulated in SIMULINK, tested for reactive load disturbance and /or wind power input disturbance of different magnitudes to investigate voltage stability.
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Optimum Features selection by fusion using Genetic Algorithm in CBIR
Статья научная
The evaluation of the performance of the Content Based Image Retrieval is undertaken for the consideration in this paper. Here the point of the discussion is the performance of the CBIR system using object oriented image segmentation and the evolutionary computational technique. The visual characteristics of the objects such as color, intensity and texture are extracted by the conventional methods. Object oriented image segmentation along with the evolutionary computational technique is proposed here for Image Retrieval Algorithm. Unsupervised Curve evolution method is used for object oriented segmentation of the Image and genetic Algorithm is used for the Optimum Classification and reduction in the Feature dimensionality. The Algorithm is tested on the images which are characterized by the low depth. The Berkeley database is found to be suitable for this purpose. The experimental result shows that the Genetic Algorithm enhances the performance of this Content Based Image Retrieval and found to be suitable for optimization of features selection and compression technique for Feature space.
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Optimum Fuzzy based Image Edge Detection Algorithm
Статья научная
Edge detection is important in image processing to aid operations such as object classification and identification amongst others. This is soley to improve interpretability of the image. Common edge detection techniques such as Sobel, Prewittt, Canny, Laplacian of Gaussian (LOG), Robertss and Zero-Crossing has attracted the attention of researchers to perform a comparative analysis on these techniques excepts fuzzy, using different type of images. Fuzzy logic based edge detection algorithms development and comparison with existing algorithm became important due to the fact that the pixels’ boundaries identifying image degs are crystal clear as expected, hence other edge detection algorithms using crisp values will be omitting some vital information pixels, this impairs the quality of the image edge detected and further application through proper interpretation. This research further extends the investigation of edge detection techniques optimality, through comparing Sobel, Prewittt, Canny, Laplacian of Gaussian (LOG), and Robertss edge detection algorithms with our proposed fuzzy based edge detection algorithm designed using MATLAB. The result indicated that the novel fuzzy based edge detection algorithm developed in this research outperforms the Canny, Sobel, Prewittt, Robertss and LOG edge detection algorithms in three different experiments with different images
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Palatal Patterns Based RGB Technique for Personal Identification
Статья научная
Biometric system is an alternative way to the traditional identity verification methods. This research article provides an overview of recently / currently used single and multiple biometrics based personal identification systems which are based on human physiological (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, footprints and palates) and behavioral (such as body language, facial expression, signature verification and speech recognition) characteristics. This paper focuses on RGB based palatal pattern analysis of persons and the proposed technique uses RGB values with silhouette computes of palatal patterns for identifying a person. We have tested our proposed technique for palatal patterns of 50 persons including males & females and it is observed that RGB values based silhouette technique are accurately identifying the persons on the basis of their palatal patterns. For each person seven palatal images were taken. Out of these seven palatal images, four images were used for training dataset and last three palatal patterns were used for identifying the persons. The proposed technique is reliable & secure and it is a foolproof method which is clearly differentiating the persons on the basis of their palatal patterns.
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Parallel Implementation of Texture Based Image Retrieval on The GPU
Статья научная
Most image processing algorithms are inherently parallel, so multithreading processors are suitable in such applications. In huge image databases, image processing takes very long time for run on a single core processor because of single thread execution of algorithms. Graphical Processors Units (GPU) is more common in most image processing applications due to multithread execution of algorithms, programmability and low cost. In this paper we implement texture based image retrieval system in parallel using Compute Unified Device Architecture (CUDA) programming model to run on GPU. The main goal of this research work is to parallelize the process of texture based image retrieval through entropy, standard deviation, and local range, also whole process is much faster than normal. Our work uses extensive usage of highly multithreaded architecture of multi-cored GPU. We evaluated the retrieval of the proposed technique using Recall, Precision, and Average Precision measures. Experimental results showed that parallel implementation led to an average speed up of 140.046×over the serial implementation. The average Precision and the average Recall of presented method are 39.67% and 55.00% respectively.
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Статья научная
This paper addresses the problem of detecting the partially-correlated χ2 fluctuating targets with two and four degrees of freedom. It presents the performance analysis, in its exact form, of GTM-CFAR processor when the operating environment is contaminated with extraneous targets and the radar receiver post-detection integrates M pulses of exponentially correlated targets. Mathematical formulas for the detection and false alarm probabilities are derived, in the absence as well as in the presence of spurious targets which are fluctuating in accordance with the so-called moderately fluctuating χ2 targets. A thorough performance assessment by several numerical examples, which has considered the role that each parameter can play in the processor performance, is also given. The results show that the processor performance improves, for weak SNR of the primary target, as the correlation coefficient ρs increases and this occurs either in the absence or in the presence of outlying targets. As the strength of the target return increases, the processor tends to invert this behavior. The SWI & SWII and SWIII & SWIV models enclose the correlated target cases when the target correlation follows χ2 fluctuation models with two and four degrees of freedom, respectively, and this behavior is common for all GTM based detectors.
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Patch based image inpainting technique using adaptive patch size and sequencing of priority terms
Статья научная
Image Inpainting is a system used to fill lost information in an image in a visually believable manner so that it seems original to the human eye. Several algorithms are developed in the past which tend to blur the inpainted image. In this paper, we present an algorithm that improves the performance of patch based image inpainting by using adaptive patch size and sequencing of the priority terms. The patch width (wxw) is made adaptive (proportional) to the area of the damaged region and inversely proportional to standard deviation of the known values in the patch around point of highest priority. If the neighbourhood region is a smooth region then standard deviation is small therefore large patch size is used and if standard deviation is large patch size is small. The algorithm is tested for various input images and compared with some standard algorithm to evaluate its performance. Results show that the time required for inpainting is drastically reduced while the quality factor is maintained equivalent to the existing techniques.
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Pattern Recognition: Invariance Learning in Convolutional Auto Encoder Network
Статья научная
The ability of the human visual processing system to accommodate and retain clear understanding or identification of patterns irrespective of their orientations is quite remarkable. Conversely, pattern invariance, a common problem in intelligent recognition systems is not one that can be overemphasized; obviously, one's definition of an intelligent system broadens considering the large variability with which the same patterns can occur. This research investigates and reviews the performance of convolutional networks, and its variant, convolutional auto encoder networks when tasked with recognition problems considering invariances such as translation, rotation, and scale. While, various patterns can be used to validate this query, handwritten Yoruba vowel characters have been used in this research. Databases of images containing patterns with constraints of interest are collected, processed, and used to train and simulate the designed networks. We provide extensive architectural and learning paradigms review of the considered networks, in view of how built-in invariance is learned. Lastly, we provide a comparative analysis of achieved error rates against back propagation neural networks, denoising auto encoder, stacked denoising auto encoder, and deep belief network.
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Pattern averaging technique for facial expression recognition using support vector machines
Статья научная
Facial expression is one of the nonverbal communication methods of identifying an emotional state of a human being. Due to its crucial importance in Human-Robot interaction, facial expression recognition (FER) is in the limelight of recent research activities. Most of the studies consider the whole expression images in their analysis, and it has several has several drawbacks concerning illumination, orientation, texture, zoom level, time and space complexity. In this paper, a novel feature extraction technique called the pattern averaging is studied on whole image data using reduction in the dimension of the image by averaging the neighboring pixels. The study is found to give better results on standard datasets using support vector machine classifier.
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Pavement Crack Detection Using Spectral Clustering Method
Статья научная
Pavement crack detection plays an important role in pavement maintaining and management, nowadays, which could be performed through remote image analysis. Thus, edges of pavement crack should be extracted in advance; in general, traditional edge detection methods don’t consider phase information and the spatial relationship between the adjacent image areas to extract the edges. To overcome the deficiency of the traditional approaches, this paper proposes a pavement crack detection algorithm based on spectral clustering method. Firstly, a measure of similarity between pairs of pixels is taken into account through orientation energy. Then, spatial relationship is needed to find regions where similarity between pixels in a given region is high and similarity between pixels in different regions is low. After that, crack edge detection is completed with spectral clustering method. The presented method has been run on some real life images of pavement crack, experimental results display that the crack detection method of this paper could obtain ideal result.
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