Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1092
Pedestrian Detection in Thermal Images Using Deep Saliency Map and Instance Segmentation
Статья научная
Pedestrian detection is an established instance of computer vision task. Pedestrian detection from the color images has achieved robust performance but in the night time or in bad light conditions it has low detection accuracy. Thermal images are used for detecting people at night time, foggy weather or in bad lighting situations when color images have a lower vision. But in the daytime where the surroundings are warm or warmer than pedestrians then the thermal image has lower accuracy. Hence thermal and color image pair can be a solution but it is expensive to capture color-thermal pair and misaligned imagery can cause low detection accuracy. We proposed a network that achieved better accuracy by extending the prior works which introduced the use of the saliency map in pedestrian detection tasks from the thermal images into instance-level segmentation. We worked on a subdivision of KAIST Multispectral Pedestrian Detection Dataset [8] which has pixel-level annotations. We have trained Mask-RCNN for pedestrian detection task and report the added effect of saliency maps generated using PiCA-Net. We have achieved an accuracy of 88.14% over day and 91.84% over night images. So, our model has reduced the miss rate by 24.1% and 23% over the existing state-of-the-art method in day and night images.
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Статья научная
A human eye can detect a face in an image whether it is in a digital image or also in some video. The same thing is highly challenging for a machine. There are lots of algorithms available to detect human face. In this paper, a technique has been made to detect and locate the position of human faces in digital images. This approach has two steps. First, training the artificial neural network using Levenberg–Marquardt training algorithm and then the proposed algorithm has been used to detect and locate the position of the human faces from digital image. The proposed algorithm has been implemented for six color spaces which are RGB, YES, YUV, YCbCr, YIQ and CMY for each of the image formats bmp, jpeg, gif, tiff and png. For each color space training has been made for the image formats bmp, jpeg, gif, tiff and png. Finally, one color space and particular image format has been selected for face detection and location in digital image based on the performance and accuracy.
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Статья научная
Object Tracking is becoming increasingly important in areas of computer vision, surveillance, image processing and artificial intelligence. The advent of high powered computers and the increasing need of video analysis has generated a great deal of interest in object tracking algorithms and its applications. This said it becomes even more important to evaluate these algorithms to quantify their performance. In this paper, we have implemented three algorithms namely Alpha Beta filter, Kalman filter and Meanshift to track an object in a video sequence and compared their tracking performance based on various parameters in normal and noisy conditions. The proposed parameters employed are error plots in position and velocity of the object, Root mean square error, object tracking error, tracking rate and time taken to track the object. The goal is to illustrate practically the performance of each algorithm under such conditions quantitatively and identify the algorithm that performs the best.
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Performance Analysis of Fingerprint Denoising Using Stationary Wavelet Transform
Статья научная
Finger print is the finest and cheapest recognition system because of its easy extraction of unique features like bifurcation and termination. But the quality of fingerprint data are easily degraded by dryness of skin, wet, wound and other types of noises. Hence, denoising of fingerprint image is vital step for automatic fingerprint recognition system. In the proposed paper the removal of noise from fingerprint images by using stationary wavelet transform and adaptive thresholding method is analysed. The proposed algorithm is developed using MATLAB (R2010b) and tested in the fingerprint images collected from FVC2004 database and R303A optical scanner. The performance of the method is analysed by calculating the quality metrics like Peak Signal to Noise Ratio, Universal Quality Index , Structure Similarity and Multi-Scale Structure Similarity (MS-SSIM). The quality of fingerprint image after noise removal using proposed analysis confirms the suggested method is better than the conventional techniques.
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Performance Analysis of Non-Linear Equalizer in MIMO System for Vehicular Channel
Статья научная
All wireless technologies face the challenges of multipath signal fading, attenuation delay and phase delay which led to the interference between users and there is the possibility of limited spectrum. Linear and Non-Linear receiver is used to combat the effect of multipath signal fading and delay. The linear receiver gives best result in case of static environment but in case of dynamic environmental condition it fails to give better results and hence in order to improve the system performance non-linear receiver is used in dynamic environment condition. As a dynamic channel, Vehicular Channel model is considered because there is growing interest in vehicular networking and it is also a challenging channel model because of the complexity of the environment, and rapid variation in channel conditions. This paper studies the comparison between Zero Forcing (ZF) and Minimum Mean Square Error (MMSE) receiver in the Vehicular Channel. A comparative study between linear equalizer and non-linear equalizer in the Vehicular Channel is done and analyze the effect of the varying modulation and antenna configuration on the performance.
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Performance Analysis of Texture Image Classification Using Wavelet Feature
Статья научная
This paper compares the performance of various classifiers for multi class image classification. Where the features are extracted by the proposed algorithm in using Haar wavelet coefficient. The wavelet features are extracted from original texture images and corresponding complementary images. As it is really very difficult to decide which classifier would show better performance for multi class image classification. Hence, this work is an analytical study of performance of various classifiers for the single multiclass classification problem. In this work fifteen textures are taken for classification using Feed Forward Neural Network, Naïve Bays Classifier, K-nearest neighbor Classifier and Cascaded Neural Network.
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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
Статья научная
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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