Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1110
Performance Analysis of Various Image Feature Extractor Filters for Pothole Anomaly Classification
Статья научная
Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the other hand, has an accuracy of roughly 87% when it comes to classifying potholes in low-light conditions. These findings are crucial in the selection of feature extraction filters for use by ML classifiers in the development of a robust autonomous pothole detection and classification system for improved navigation on anomalous roads and possible integration into self-driving cars.
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Статья научная
In this paper image compression using hybrid wavelet transform is proposed. Hybrid wavelet transform matrix is formed using two component orthogonal transforms. One is base transform which contributes to global features of an image and another transform contributes to local features. Here base transform is varied to observe its effect on image quality at different compression ratios. Different transforms like Discrete Kekre Transform (DKT), Walsh, Real-DFT, Sine, Hartley and Slant transform are chosen as base transforms. They are combined with Discrete Cosine Transform (DCT) that contributes to local features of an image. Sizes of component orthogonal transforms are varied as 16-16, 32-8 and 64-4 to generate hybrid wavelet transform of size 256x256. Results of different combinations are compared and it has been observed that, DKT as a base transform combined with DCT gives better results for size 16x16 of both component transforms.
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Статья научная
A hybrid watermarking technique using Singular value Decomposition with orthogonal transforms like DCT, Haar, Walsh, Real Fourier Transform and Kekre transform is proposed in this paper. Later, SVD is combined with wavelet transforms generated from these orthogonal transforms. Singular values of watermark are embedded in middle frequency band of column/row transform of host image. Before embedding, Singular values are scaled with suitable scaling factor and are sorted. Column/row transform reduces the computational complexity to half and properties of singular value decomposition and transforms add to robustness. Behaviour of proposed method is evaluated against various attacks like compression, cropping, resizing, and noise addition. For majority of attacks wavelet transforms prove to be more robust than corresponding orthogonal transform from which it is generated.
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Performance Evaluation and Comparative Analysis of Different Filters for Noise Reduction
Статья научная
The quality of microscopic images is generally degraded during the image acquisition by quantizing noise, electrical noise, light illumination etc. Noise reduction is considered as a very important preprocessing step as the quality of the images can determine the accuracy of the results. The work done focuses on the noise reduction using different filters on the different types of noises applied on the common digital images and specifically the Leukemia images. 40 images were taken for the comparison purpose; 20 digital images and 20 Leukemia images of different types of Leukemia. The qualitative as well as quantitative analysis of the performance of the filters on the different noises is done. For the quantitative analysis the parameters used for the evaluation of the images are MSE, PSNR and CoC. For the qualitative analysis visual analysis in terms of quality is also done using the resultant images and their histograms. Simulation has been done in Matlab 11b. From the test cases it has been observed that Adaptive Filter produces good results on Salt and Pepper, Speckle and Gaussian noise in case of the digital images. Whereas in case of Leukemia images results of Median Filter are best for the Gaussian, Poisson and Speckle noise corrupted images.
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Performance Evaluation of Image Fusion Algorithms for Underwater Images-A study based on PCA and DWT
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In this paper, a comparative study between two image fusion algorithm based on PCA and DWT is carried out in underwater image domain. Underwater image fusion is emerged as one of the main image fusion area, here two or more images will be fused by retaining the most desirable characteristics of each underwater images. The DWT technique is used to decompose the input image into four frequency sub bands and the low-low sub band images will be considered in fusion processing. In PCA method significant eigen values will be considered in fusion process to retain the important characteristics of the input images. The results acquired from both experiments are tabulated and compared by considering the statistical measures such as Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Entropy. Results shows that underwater image fusion based on DWT outperforms the PCA based method.
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Статья научная
The present paper aims at performance evaluation of Doubly Truncated Generalized Laplace Mixture Model and Hierarchical clustering (DTGLMM-H) for image analysis concerned to various practical applications like security, surveillance, medical diagnostics and other areas. Among the many algorithms designed and developed for image segmentation the dominance of Gaussian Mixture Model (GMM) has been predominant which has the major drawback of suiting to a particular kind of data. Therefore the present work aims at development of DTGLMM-H algorithm which can be suitable for wide variety of applications and data. Performance evaluation of the developed algorithm has been done through various measures like Probabilistic Rand index (PRI), Global Consistency Error (GCE) and Variation of Information (VOI). During the current work case studies for various different images having pixel intensities has been carried out and the obtained results indicate the superiority of the developed algorithm for improved image segmentation.
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Статья научная
Super resolution (SR) images play an important role in Image processing applications. Spatial resolution is the key parameter in many applications of image processing. Super resolution images can be used to improve the spatial resolution. In this paper a new SR image reconstruction algorithm is proposed using Integer wavelet transform (IWT) and Binary plane technique (BPT). The proposed method is analyzed in different color space transforms such as CIELAB, YCbCr and RGB. In this paper we compared PSNR, ISNR, Blocking effect and Homogeneity with different colour images in RGB, YCbCr and CIELAB domains. Qualitative analysis shows that the proposed method in CIELAB color space transforms has better performance.
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Performance analysis of statistical approaches and NMF approaches for speech enhancement
Статья научная
Super-Gaussian Based Bayesian Estimators plays significant role in noise reduction. However, the traditional Bayesian Estimators process only DFT spectral amplitude of noisy speech and the phase is left unprocessed. While deriving Bayesian estimators, consideration of phase information provides improved results. The main objective of this paper is twofold. Firstly, the Super-Gaussian based Complex speech coefficients given Uncertain Phase (CUP) based Bayesian estimators are compared under different noise conditions like White noise, Babble noise, Pink noise, Modulated Pink noise, Factory noise, Car noise, Street noise, F16 noise and M109 noise. Secondly, a novel speech enhancement method is proposed by combining CUP estimators with different NMF approaches and online bases updation. The statistical estimators show less effective results under completely non-stationary assumptions. Non-negative Matrix Factorization (NMF) based algorithms show better performance for non stationary noises. The drawback of NMF is, it requires training and/or requires clean speech and noise signals. This drawback can be overcome by taking the advantages of both statistical approaches and NMF approaches. Such approaches like Posteriori Regularized NMF (PR-NMF), Weibull Rayleigh NMF (WR-NMF), Nakagami Rayleigh (NR-NMF), CUP estimator with Gamma and Generalized Gamma distributions + NMF + Online bases Update (CUP-GG + NMF + OU) and CUP-GG + WR-NMF / NR-NMF + OU are considered for comparison. The objective of this paper is to analyze the performance of speech enhancement methods using Bayesian estimators, NMF approaches, Combination of statistical and NMF approaches. The objective performance measures Perceptual Evaluation of Speech Quality (PESQ), Short-Time Objective Intelligibility (STOI), Signal to Noise Ratio (SNR), Signal to Distortion Ratio (SDR), Segmental SNR (Seg SNR) are considered for comparison.
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Performance framework for HPC applications on homogeneous computing platform
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In scientific fields, solving large and complex computational problems using central processing units (CPU) alone is not enough to meet the computation requirement. In this work we have considered a homogenous cluster in which each nodes consists of same capability of CPU and graphical processing unit (GPU). Normally CPU are used for control GPU and to transfer data from CPU to GPUs. Here we are considering CPU computation power with GPU to compute high performance computing (HPC) applications. The framework adopts pinned memory technique to overcome the overhead of data transfer between CPU and GPU. To enable the homogeneous platform we have considered hybrid [message passing interface (MPI), OpenMP (open multi-processing), Compute Unified Device Architecture (CUDA)] programming model strategy. The key challenge on the homogeneous platform is allocation of workload among CPU and GPU cores. To address this challenge we have proposed a novel analytical workload division strategy to predict an effective workload division between the CPU and GPU. We have observed that using our hybrid programming model and workload division strategy, an average performance improvement of 76.06% and 84.11% in Giga floating point operations per seconds(GFLOPs) on NVIDIA TESLA M2075 cluster and NVIDIA QUADRO K 2000 nodes of a cluster respectively for N-dynamic vector addition when compared with Simplice Donfack et.al [5] performance models. Also using pinned memory technique with hybrid programming model an average performance improvement of 33.83% and 39.00% on NVIDIA TESLA M2075 and NVIDIA QUADRO K 2000 respectively is observed for saxpy applications when compared with pagable memory technique.
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Performance of Personal Identification System Technique Using Iris Biometrics Technology
Статья научная
The Iris identification as one of the significant techniques of biometric identification systems s and iris recognition algorithm is described. Biometric technology advances intellectual properties are wanted by many unauthorized personnel. As a result many researchers have being searching ways for more secure authentication methods for the user access. Iris recognition uses iris patterns for personnel identification. The system steps are capturing iris patterns; determining the location of iris boundaries; converting the iris boundary to the stretched polar coordinate system; extracting iris code based on texture analysis. The system has been implemented and tested using dataset of number of samples of iris data with different contrast quality. The developed algorithm performs satisfactorily on the images, provides 93% accuracy. Experimental results show that the proposed method has an encouraging performance.
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Permutation-based Homogeneous Block Content Authentication for Watermarking
Статья научная
In modern days, digital watermarking has become an admired technique for hitting data in digital images to help guard against copyright infringement. The proposed Permutation-based Homogeneous Block Content authentication (PHBC) methods develop a secure and excellence strong watermarking algorithm that combines the reward of permutation-based Homogeneous block (PHB) with that of significant and insignificant bit values with X0R encryption function using Max coefficient of least coordinate value for embedding the watermark. In the projected system uses the relationship between the permutation blocks to embed many data into Homogeneous blocks without causing solemn distortion to the watermarked image. The experimental results show that the projected system is very efficient in achieving perceptual invisibility with an increase in the Peak Signal to Noise Ratio (PSNR). Moreover, the projected system is robust to a variety of signal processing operations, such as image Cropping, Rotation, Resizing, Adding noise, Filtering , Blurring and Motion blurring.
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Persian Sign Language Recognition Using Radial Distance and Fourier Transform
Статья научная
This paper provides a novel hand gesture recognition method to recognize 32 static signs of the Persian Sign Language (PSL) alphabets. Accurate hand segmentation is the first and important step in sign language recognition systems. Here, we propose a method for hand segmentation that helps to build a better vision based sign language recognition system. The proposed method is based on YCbCr color space, single Gaussian model and Bayes rule. It detects region of hand in complex background and non-uniform illumination. Hand gesture features are extracted by radial distance and Fourier transform. Finally, the Euclidean distanceis used to compute the similarity between the input signs and all training feature vectors in the database. The system is tested on 480 posture images of the PSL, 15 images for each 32 signs. Experimental results show that our approach is capable to recognize all 32 PSL alphabets with 95.62% recognition rate.
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Pipelined Vedic-Array Multiplier Architecture
Статья научная
In this paper, pipelined Vedic-Array multiplier architecture is proposed. The most significant aspect of the proposed multiplier architecture method is that, the developed multiplier architecture is designed based on the Vedic and Array methods of multiplier architecture. The multiplier architecture is optimized in terms of multiplication and addition to achieve efficiency in terms of area, delay and power. This also gives chances for modular design where smaller block can be used to design the bigger one. So the design complexity gets reduced for inputs of larger number of bits and modularity gets increased. The proposed Vedic-Array multiplier is coded in Verilog, synthesized and simulated using EDA (Electronic Design Automation) tool - XilinxISE12.3, Spartan 3E, Speed Grade-4. Finally the results are compared with array and booth multiplier architectures. Proposed multiplier is better in terms of delay and area as compared to booth multiplier and array multiplier respectively. The proposed multiplier architecture can be used for high-speed requirements.
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Plants Leaves Images Segmentation Based on Pseudo Zernike Moments
Статья научная
Leaves images segmentation is an important task in the automated plant identification. Images leaf segmentation is the process of extracting the leaf from its background, which is a challenging task. In this paper, we propose an efficient and effective new approach for leaf image segmentation, we aim to separate the leaves from the background and from their shadow generated when the photo was taken. The proposed approach calculates the local descriptors for the image that will be classified for the separation of the different image's region. We use Pseudo Zernike Moments (PZM) as a local descriptor combined with K-means algorithm for clustering. The efficient of PZM for features extraction lead to very good results in very short time. The validation tests applied on a variety of images, showed the ability of the proposed approach for segmenting effectively the image. The results demonstrate a real improvement compared to those of new existing segmentation method.
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Polynomial Differentiation Threshold based Edge Detection of Contrast Enhanced Images
Статья научная
This paper uses a two-step method for edge detection using a polynomial differentiation threshold on contrast-enhanced images. In the first step, to enhance the image contrast, the mean absolute deviation and harmonic mean brightness values of the images are calculated. Mean absolute deviation is used to perform the histogram clipping to restrict over-enhancement. First, the clipped histogram is divided in half, and then two sub-images are created and equalized, and combined into a final image that keeps image quality. The second phase involves edge detection using a polynomial differentiation-based threshold on contrast-improved visuals. The polynomial differentiation curve-fitting method was used to smooth the histogram data. The nearest index value to zero is utilized to calculate the threshold value to detect the edges. The significance of the proposed work is to contrast enhancement of low-light images to extract the edge lines. Its value or merit is to achieve improved edge results in terms of various image quality metrics. The findings of the proposed research work are to detect the edges of low-contrast images. Image quality metrics are computed and it is observed that the suggested algorithm surpasses former methods in respect of Edge-based contrast measure (EBCM), Performance Ratio, F-Measure, and Edge-strength similarity-based image quality metric (ESSIM).
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Polyphase Structure Based Eigen Design of Two-Channel Quadrature Mirror Filter Bank
Статья научная
This paper presents a method for the design of two-channel quadrature mirror filter (QMF) banks with linear phase in frequency domain. Low-pass prototype filter of the QMF bank is implemented using polyphase decomposition. Prototype filter coefficients are optimized to minimize an objective function using eigenvalue-eigenvector approach without matrix inversion. The objective function is formulated as a weighted sum of four terms, pass-band error and stop-band residual energy of low-pass analysis filter, the square error of the overall transfer function at the quadrature frequency and amplitude distortion of the filter bank. The simulation results clearly show that the proposed method requires less computational efforts in comparison to the other state-of-art existing design methods.
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Pose Normalization based on Kernel ELM Regression for Face Recognition
Статья научная
Pose variation is the one of the main difficulty faced by present automatic face recognition system. Due to the pose variations, feature vectors of the same person may vary more than inter person identity. This paper aims to generate virtual frontal view from its corresponding non frontal face image. The approach presented in this paper is based on the assumption of existence of an approximate mapping between the non frontal posed image and its corresponding frontal view. By calculating the mapping between frontal and posed image, the problem of estimating the frontal view will become the regression problem. In the present approach, non linear mapping, kernel extreme learning machine (KELM) regression is used to generate virtual frontal face image from its non frontal counterpart. Kernel ELM regression is used to compensate for the non linear shape of the face. The studies are performed on GTAV database with 5 posed images and compared with linear regression approach.
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Статья научная
In this article, power optimization is investigated in Configurable Logic Block (CLB) of Field Programmable Gate Array (FPGA) for 65nm technology via controlling Virtual Ground Voltage (Vssv) state that follows Power-Gated standard. Initially different Configurable Logic Block are designed through the logic gates and then expanded via adding Look Up Table circuit (LUT) in inputs; afterwards, the samples of Configurable Logic blocks are investigated in two logic states of Virtual Ground Voltage =0 and Virtual Ground Voltage =1 regarding the power dissipation; whereas 100µs is time reference for simulation of time controller of Virtual Ground Voltage function. First Configurable Logic Block are kept at logic state of Virtual Ground Voltage =1(power gated) for 10µs out of 100µs and remaining time at logic state of Virtual Ground Voltage =0 (power not gated); then the simulation test is repeated up to 50µs in 5 steps for each Configurable Logic Block sample. Finally the result shows that reduction being at logic state of Virtual Ground Voltage =0 in a constant time period has linear effect on decreasing average power. With the Configurable Logic Block in operation for 50% of the total time in Virtual Ground Voltage =1 logic state, the average power reduces up to 49% in the best case scenario. Meanwhile the Configurable Logic Block can still preserve its logic state.
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Prototyping an Automated Video Surveillance System Using FPGAs
Статья научная
Because of increasing terrorist activities, the resolution of video cameras and the number of cameras deployed for surveillance are increasing exponentially – producing huge amount of video data. Manual analysis of this large volume of video data by human operators for crime scene and forensic analysis is neither reliable nor scalable. This has generated enormous interest in research activities related to automation of video surveillance systems which allows real-time automatic extraction and analysis of information from live incoming video streams and enables automatic detection and tracking of targets without human intervention. To meet the real-time requirements of automated video surveillance systems, very different technologies and design methodologies have been used in literature. These range from use of General Purpose Processors (GPPs) or special purpose Digital Signal Processors (DSPs) or Graphics Processing Units (GPUs) to Application Specific Integrated Circuits (ASICs) or Applications Specific Instruction Set Processors (ASIPs) or even programmable logic devices like Field Programmable Gate Arrays (FPGAs). FPGAs provide real-time performance that is hard to achieve with GPPs/DSPs, limit the extensive design work, time, and cost required for ASICs, and allow algorithmic changes in later stages of system development. Due to these features FPGAs are being increasingly used for prototyping automated video surveillance system quickly. In this paper we present the top level description of a complete automated video surveillance system along with the elaboration of different challenges/issues involved in its design and implementation, a comparative analysis of design methodologies and existing FPGA platforms, complete design flow for prototyping the FPGA-based automated video surveillance system, and details of various primary input/output interfaces required for designing smart automated video surveillance systems for future.
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Pyramid Image and Resize Based on Spline Model
Статья научная
The paper is based around the formalization of the image model as a linear combination of B-splines, which is close to interpolation. The authors present, on average, its corresponding explicit aspects and low-frequency filtering and scaling operators. The possibility to obtain digital images scaled to an arbitrary, not necessarily integer, number of times is demonstrated in the article and the corresponding algorithm is provided. The article provides with the examples on estimation of the quality of approximation of the indicated spline model. Also there are given grounds for its introduction as an alternative to the well-known image model based on the two-dimensional Gaussian function. It is noted that with the increasing order, B-splines differ little from Gaussian, and their simpler calculation makes the spline model attractive for research and use. Applying the well-known formalization of the approach to the construction of a pyramid of digital images based on Gaussian functions, the authors suggest its extension onto the case of a spline model. The use of image pyramids is conditioned by the task of finding special points in a digital image in order to determine the unambiguous correspondence between the images of the same object in different digital photographs. The paper presents linear operators based on B-splines of 2-6 orders aimed at the construction of a pyramid, it also demonstrates an example of their usage. Based on the convolution of the raster with a mask with variable coefficients the possibility to obtain digital images scaled to an arbitrary, not necessarily integer, number of times is demonstrated in the article and the corresponding algorithm is provided. Image resizing based on the suggested algorithm is also demonstrated by examples. The authors believe that the research conducted in the paper in the future will allow for digital images to obtain more computationally simple algorithms for determining special points and their detectors. Results of paper: 1. The model of a DI has been formalized on the basis of two-dimensional polynomial splines, on the basis of B-splines of the second-sixth orders which are close to interpolation on the average. 2. The convolution operators of low-frequency DI filtering based on the spline model are presented. 3. Provided are the scaling operators used to build image pyramids, in order to further search for special points. 4. An algorithm for scaling the DI to an arbitrary, not necessarily an integer number of times based on a continuous spline approximation has been suggested. 5. Algorithm for scaling a digital image based on a spline model allows you to change the size of the image in any (not necessarily an integer) number of times, differs in that it provides high scaling accuracy and no artifacts due to high approximate properties and smoothness of the spline model;6. The scaling algorithm allows digital image processing at high computational speed due to the optimal computational scheme with a minimum of simpler mathematical operations, compared with models based on the two-dimensional Gaussian function.
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