Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1110
Статья научная
In this paper a hybrid digital video watermarking scheme based on discrete wavelet transform and singular value decomposition is proposed. Unlike the most existing watermarking schemes, the used watermark is a gray scale image instead of a binary watermark. The watermark is embedded in the original video frames by first converted it into YCbCr color space and than decomposing the luminance part (Y component) into four sub-bands using discrete wavelet transform and finally the singular values of LL sub-band are shaped perceptually by singular values of watermark image. The experimental result shows a tradeoff between imperceptibility and resiliency against intentional attacks such as rotation, cropping, histogram stretching, JPEG compression on individual frames, Indeo5 video compression and unintentional attacks like frame swapping, frame averaging, frame insertion and different types of noise addition. Superiority of the proposed scheme is carried out by comparison with existing schemes to reveal its efficiency for practical applications.
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Статья научная
Digital spectral analysis is the significant factor of consideration by which numerous applications importantly need of effective reception and analysis of signals, that is, the reception of signals is needed with improved spectral characteristics and simple techniques. To meet the above requirements, a novel technique is proposed in digital bandpass filter bank, supported by 'Modified Kaiser window' based Finite impulse response method in Multirate processing followed by Fast Fourier transform. The novel technique influences largely in the proposed method in such a way that it involves the modification of samples of input signal for deriving the advantages in respect of selectivity, stopband attenuation, peak output and constant width cum sharp rise of response apart from smooth spectral output when compared with existing methods. Further, reduction in computational complexity and hardware complexity are the additional features of the proposed method, henceforth; its spectral output is suitable in many of real time applications and moreover advantageous in digital hearing aids. The simulation results are drawn and its performance is compared to elucidate the advantages in the proposed method.
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Histogram Bins Matching Approach for CBIR Based on Linear grouping for Dimensionality Reduction
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This paper describes the histogram bins matching approach for CBIR. Histogram bins are reduced from 256 to 32 and 16 by linear grouping and effect of this dimensionality reduction is analyzed, compared, and evaluated. Work presented in this paper contributes in all three main phases of CBIR that are feature extraction, similarity matching and performance evaluation. Feature extraction explores the idea of histogram bins matching for three colors R, G and B. Histogram bin contents are used to represent the feature vector in three forms. First form of feature is count of pixels, and then other forms are obtained by computing the total and mean of intensities for the pixels falling in each of the histogram bins. Initially the size of the feature vector is 256 components as histogram with the all 256 bins. Further the size of the feature vector is reduced to 32 bins and then 16 bins by simple linear grouping of the bins. Feature extraction processes for each size and type of the feature vector is executed over the database of 2000 BMP images having 20 different classes. It prepares the feature vector databases as preprocessing part of this work. Similarity matching between query and database image feature vectors is carried out by means of first five orders of Minkowski distance and also with the cosine correlation distance. Same set of 200 query images are executed for all types of feature vector and for all similarity measures. Performance of all aspects addressed in this paper are evaluated using three parameters PRCP (Precision Recall Cross over Point), LS (longest string), LSRR (Length of String to Retrieve all Relevant images).
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Histogram Equalization-A Simple but Efficient Technique for Image Enhancement
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This paper demonstrates the significance of histogram processing of an image particularly the histogram equalization (HE). It is one of the widely used image enhancement technique. It has become a popular technique for contrast enhancement because the method is simple and effective. The basic idea of HE is to re-map the gray levels of an image. Here we propose two different techniques of Histogram Equalization namely, the global HE and local HE. The Histogram Equalization has been performed in the MATLAB environment. The merits and demerits of both techniques of Histogram Equalization have also been discussed. It is seen after exhaustive experimentation on a number of sample images that the proposed image enhancement techniques can be considered as an improvement over the inbuilt MATLAB function histeq.
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Статья научная
Face recognition is one of the conventional problems in computer vision. Its recognition rate falls steeply when the images are captured in an unconstrained environment. One of the fundamental issues that creep into unconstrained environment capturing is that of the face pose variation. Due to face pose variation, occlusion of crucial features takes place. Occlusion may lead to information loss in the face descriptor which describes the face appearance. In this paper, we propose learning-based descriptor that combines horizontal, vertical and diagonal pattern of blocks generated from the convolution of face image with Gabor filter bank. To use only discriminative features, Probabilistic Linear Discriminant Analysis (PLDA) is used. The fusion of non-uniform texture based descriptor along with the PLDA approach aids in retaining enough of discriminative information to overcome the information loss occurring during feature occlusion. Since HVDGBP face descriptor utilizes the fundamental concept of Linear Binary Pattern (LBP) henceforth it helped in meeting low processing demands and ease of computing characteristic required for good face descriptors. Comprehensive comparative performance analysis of the robustness of the proposed face descriptor to withstand pose variations is presented. UMIST and AT&T Database is used for experimental analysis.
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Human Abnormal Activity Recognition from Video Using Motion Tracking
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The detection of violent behavior in the public environment using video content has become increasingly important in recent years due to the rise of violent incidents and the ease of sharing and disseminating video content through social media platforms. Efficient and effective techniques for detecting violent behavior in video content can assist authorities with identifying potential hazards, preventing crimes, and promoting public safety. Violence detection can also help to mitigate the psychological damage caused by viewing violent content, particularly in vulnerable populations such as infants and victims of violence. We have proposed an algorithm to calculate new descriptors using the magnitude and orientation of optical flow (MOOF) in the video. Descriptors are extracted from MOOF based on four binary histograms each by applying various weighted thresholds. These descriptors are used to train Support Vector Machine (SVM) and classify the video as violent or nonviolent. The proposed algorithm has been tested on the publicly available Hockey Fight Dataset and Violent Flow dataset. The results demonstrate that the proposed descriptors outperform the state-of-the-art algorithms with an accuracy of 91.5% and 78.5% on the Hockey Fight and Violent Flow datasets, respectively.
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Human Balance and Stability Behavior Analysis Using Spatial and Temporal Stabilometric Parameters
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Certain aspects of balance control can be affected by some entries as vision, proprioception, direction, age, Gender, height and weight. The knowledge of the effects of these factors on postural equilibrium allows differentiating pathological and physiological postural aspects. The aim of this study is to define the effects of these entries on postural control by analyzing the parameters: mean velocity of CoP (center of pressure), RMS (root mean square) CoP of displacement, Range of COP, CEA (confidence ellipse area). We examined healthy subjects between 19-42 years of age during the quiet stance under static conditions: keeping foot outspread and opened eyes (PE_YO), tighten foot and opened eyes (PS_YO), outspread foot and closed eyes (PE_YF), tightened foot and closed eyes (PS_YF). Experimental results through all studied parameters permit to conclude that the lack of vision and the situation with tighten foot cause a degradation of balance maintaining. They indicate also that it is easier to maintain equilibrium on the anteroposterior direction than mediolateral direction. Results show also a less well-controlled posture for male related to female. Results display also that the postural parameters studied failed to find significant effect of the height, weight and age on the postural stability.
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Human Distraction Detection from Video Stream Using Artificial Emotional Intelligence
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This paper addresses the problem of identifying certain human behavior such as distraction and also predicting the pattern of it. This paper proposes an artificial emotional intelligent or emotional AI algorithm to detect any change in visual attention for individuals. Simply, this algorithm detects human’s attentive and distracted periods from video stream. The algorithm uses deviation of normal facial alignment to identify any change in attentive and distractive activities, e.g., looking to a different direction, speaking, yawning, sleeping, attention deficit hyperactivity and so on. For detecting facial deviation we use facial landmarks but, not all landmarks are related to any change in human behavior. This paper proposes an attribute model to identify relevant attributes that best defines human’s distraction using necessary facial landmark deviations. Once the change in those attributes is identified, the deviations are evaluated against a threshold based emotional AI model in order to detect any change in the corresponding behavior. These changes are then evaluated using time constraints to detect attention levels. Finally, another threshold model against the attention level is used to recognize inattentiveness. Our proposed algorithm is evaluated using video recording of human classroom learning activity to identify inattentive learners. Experimental results show that this algorithm can successfully identify the change in human attention which can be used as a learner or driver distraction detector. It can also be very useful for human distraction detection, adaptive learning and human computer interaction. This algorithm can also be used for early attention deficit hyperactivity disorder (ADHD) or dyslexia detection among patients.
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Human Emotion Recognition System
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This paper discusses the application of feature extraction of facial expressions with combination of neural network for the recognition of different facial emotions (happy, sad, angry, fear, surprised, neutral etc..). Humans are capable of producing thousands of facial actions during communication that vary in complexity, intensity, and meaning. This paper analyses the limitations with existing system Emotion recognition using brain activity. In this paper by using an existing simulator I have achieved 97 percent accurate results and it is easy and simplest way than Emotion recognition using brain activity system. Purposed system depends upon human face as we know face also reflects the human brain activities or emotions. In this paper neural network has been used for better results. In the end of paper comparisons of existing Human Emotion Recognition System has been made with new one.
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Статья научная
Gait analysis is basically referred to study of human locomotion. From the surveillance point of view behavioral biometrics and recognition at a distance are becoming more popular in researchers rather than interactive and Physiological biometrics. In this paper, a time efficient Human gait identification system is proposed. Initially Human silhouettes are extracted by using temporal median background subtraction on video frames, which successfully removes shadows and models even complex background, proposed gait algorithm extracts contours from foreground silhouettes images and then three bounding boxes are drawn around contoured human image 1) upper part for arms movement 2) middle part for thigh and knee angles 3) Lower part for legs movement, knee and ankle angles. Gait cycles are extracted to find gait period and to take final decision for gait features selection, which is used for training. Thigh, Knee, Ankle angles and bounding boxes' widths are used as gait signatures but middle portion of human contains less variations of width in gait cycle hence computing efficiency can be achieved by ignoring width factor of middle part. SVM based training and identification is performed on extracted gait features. The proposed system is assessed using publicly available gait datasets and some indoor experimental videos created for this research work. The results reveal that the proposed algorithm is able to achieve an outstanding recognition rate.
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Human Identification by Gait Using Corner Points
Статья научная
Recently human gait has become a promising and very important biometric for identification. Current research on gait recognition is usually based on an average gait image or a silhouette sequence, or a motion structure model. In this paper, the information about gait is obtained from the disparity on time and space of the different parts of the silhouette. This paper proposes a gait recognition method using edge detection, identification of corner points from edges, and selection of control points out of those corner points. Here, the images of moving human figures are subtracted from background by simple background modeling technique to obtain binary silhouettes. A gait signature of a person is taken as silhouette images of a complete gait cycle. A complete gait cycle is then divided into different frames in such a way that the information of the person’s gait style can be represented fully. One given unknown gait cycle is compared with stored gait cycles in terms of a cyclic distances between control points of an image of input gait cycle with that of corresponding image of the stored gait cycle. Experimental results show that our method is encouraging in terms of recognition accuracy.
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Human action recognition using modified bag of visual word based on spectral perception
Статья научная
Human action recognition has a very vast application such as security, patient care, etc. Background cluttering, appearance change due to variation in viewpoint and occlusion are the prominent hurdles that can reduce the recognition rate significantly. Methodologies based on Bag-of-visual-words are very popular because they do not require accurate background subtraction. But the main disadvantage with these methods is that they do not retain the geometrical structural information of the clusters that they form. As a result, they show intra-class mismatching. Furthermore, these methods are very sensitive to noise. Addition of noise in the cluster also results in the misclassification of the action. To overcome these problems we proposed a new approach based on modified Bag-of-visual-word. Proposed methodology retains the geometrical structural information of the cluster based on the calculation of contextual distance among the points of the cluster. Normally contextual distance based on Euclidean measure cannot deal with the noise but in the proposed methodology contextual distance is calculated on the basis of a difference between the contributions of cluster points to maintain its geometrical structure. Later directed graphs of all clusters are formed and these directed graphs are described by the Laplacian. Then the feature vectors representing Laplacian are fed to the Radial Basis Function based Support Vector Machine (RBF-SVM) classifier.
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Human stress management through heart rate variability
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Nowadays, there are many people having stress issues. Most of them do not know how to handle stress properly which may cause harm to their health condition. Moreover, they also may not notice that they are having stress until it become worsen. As we know, a calming surrounding and environment helps in soothing the emotion of a stressed person. In this project, an Android mobile application named “Intelligent Stress Relief App” will be developed to minimize the problems above. This application allows user to check their stress level based on their heart rate data through Bluetooth heart rate sensor. With the records of stress patterns, user is able to keep track of their stress condition in order to seek for better stress management. Furthermore, this application will provide user with a database of meditation techniques and relaxing music to assist them in releasing their stress.
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Статья научная
With the rapid development of video coding technology, all kinds of video coding standards have been advanced in recent years with a variety of different and complex algorithms. They share common and/or similar coding tools, yet there is currently no explicit way to exploit such commonalities at the level of specifications or implementations. Reconfigurable video coding (RVC) is to develop a video coding standard that overcomes many shortcomings of current standardization and specification process by updating and progressively incrementing a modular library of components. In this paper, a hybrid decoder reconfiguration is instantiated in the RVC framework by grouping the coding tools from AVS-P7 and MPEG-4/AVC. Experimental results show that compared with MPEG-4/AVC baseline profile, the reconfigurable coding system reduces the computational complexity and guarantees the coding performance at low bit rate. Moreover, it enriches the RVC video tool library (VTL) by introducing the coding tools of AVS-P7, and also verifies the flexibility and re-configurability of RVC framework to meet the needs of different applications.
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Hybrid approach for facial expression recognition using HJDLBP and LBP histogram in video sequences
Статья научная
Any kind of compassionate thoughts can't be expressed through words, but it appears on their facial expression. So, the facial expression reveals the emotions of individuals. The recognition of such emotions can be understood correctly or sometimes ambiguously from the opponent. Hence, there is a scope for automatic facial expression recognition (FER) in the context of image processing. The FER system has three different phases: face detection, feature extraction and expression classifi-cation. In face detection phase, Viola Jones face detector is used to crop the original image such that only the face region is retained by removing the unwanted region. In feature extraction stage, High-order Joint Derivative Lo-cal Binary Pattern (HJDLBP) and Local Binary Pattern (LBP) histogram algorithms are used for extracting fea-tures from the cropped image. In last stage, Support Vec-tor machine (SVM) classifier is used in finding the precise facial expression.CK+ dataset has been used for training and testing, which consist of 442 image samples. We have considered six different universal possible ex-pressions such as, happy, anger, disgust, fear, surprised, and sad for identification. The experimental results indi-cate that the overall accuracy of the proposed system was 74.8%, which is high compare to the results available in literature.
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Hybrid of Fuzzy Logic and Random Walker Method for Medical Image Segmentation
Статья научная
The procedure of partitioning an image into various segments to reform an image into somewhat that is more significant and easier to analyze, defined as image segmentation. In real world applications, noisy images exits and there could be some measurement errors too. These factors affect the quality of segmentation, which is of major concern in medical fields where decisions about patients’ treatment are based on information extracted from radiological images. Several algorithms and techniques have developed for image segmentation and have their own advantages and disadvantages. Random walker method is a supervised segmentation method and it requires that it should be more efficient in producing effective segmentation results in case of medical images which are complex images. In the present paper, we are going to incorporate the advantages of fuzzy logic with a random walker to make resulting segmentation better in texture and quality. For this, we will use fuzzy rules to approximate boundaries in images which will improve segmentation results.
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Hybridization of DCT and SVD in the Implementation and Performance Analysis of Video Watermarking
Статья научная
In this Paper, We worked and documented the implementation and performance analysis of digital video watermarking that uses the hybrid features of two of the most powerful transform domain processing of the video and fundamentals of the linear algebra. We have taken into the account fundamentals of Discrete Cosine Transform and Singular Value Decomposition for the development of the proposed algorithm. We first used the Singular Value Decomposition and then used the singular values for the insertion of the message behind the video. Finally we used two of the visual quality matrices for the analysis purpose. We also applied various attacks on the video and found the proposed scheme more robust.
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Статья научная
The distribution of multimedia has revolutionized the web technology at very fast pace. Duplication of computerized information are created and conveyed through the web technology. Unlawful acts, for example, tampering, forging, copyright isolations and frauds on multimedia data are becoming very common in the society. Digital watermarking is one such innovation that has been produced to shield multimedia data from illegal manipulations. The advancement of technical solutions in view of watermarking method for copyright assurance has been a subject of active research amid the most recent decade. In this paper, hybridization Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) using redundant wavelet is proposed for robust digital video watermarking. The results of combining the two transform using redundant wavelets shows that the performance of proposed video watermarking scheme is enhanced and satisfies the requirement of imperceptibility. The presented results also showed that the scheme has the capacity to withstand an assortment of video processing attacks.
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As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypoint-based SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image. It has been observed that this paper’s technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise.
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Identification of handwritten complex mathematical equations
Статья научная
The mathematical notation is well known and used throughout the world. Humanity has evolved from simple methods to represent accounts to the current formal notation capable of modeling complex problems. In addition, mathematical equations are a universal language in the scientific world, and many resources such as science and engineering technology, medical field also not an exception containing mathematics have been created during the last decades. However, to efficiently access all that information, scientific documents must be digitized or produced directly in electronic formats. Although most people are able to understand and produce mathematical information, introducing mathematical equations into electronic devices requires learning special notations or using editors. The proposed methodology is focused on developing a method to recognize intricate handwritten mathematical equations. For pre-processing, Gray conversion and Weiner filtering are used. Segmentation is performed using the morphological operations, which increase the efficiency of the subsequent image of equation. Finally Neural Network based template matching technique is used to recognize the image of handwritten mathematical equation.
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