International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1146
Speech Feature Extraction for Gender Recognition
Статья научная
Speech Recognition Technology can be embedded in various real time applications in order to increase the human-computer interaction. From robotics to health care and aerospace, from interactive voice response systems to mobile telephony and telematics, speech recognition technology have enhanced the human-machine interaction. Gender recognition is an important component for the application embedding speech recognition as it reduces the computational complexity for the further processing in these applications. The paper involves the extraction of one of the most dominant and most researched up on speech feature, Mel coefficients and its first and second order derivatives. We extracted 13 values for each of these from a data-set 46 speech samples containing the Hindi vowels (आ, इ, ई, उ, ऊ, ऋ, ए, ऎ, ऒ, ऑ) and trained them using a combined model of SVM and neural network classification to determine their gender using stacking. The results obtained showed the accuracy of 93.48% after taking into consideration the first Mel coefficient. The purpose of this study was to extract the correct features and to compare the performance based on first Mel coefficient.
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Spliced image classification and tampered region localization using local directional pattern
Статья научная
In this paper the authors have proposed a spliced image detection algorithm based on Local Directional Pattern (LDP). The output of many splicing detection techniques is either to classify spliced image from authentic images or to localize the spliced region. But the proposed algorithm has ability to classify and to localize the spliced region. First, the original image (RGB color space) is converted to Ycbcr color space. The histogram of LDP of chrominance component of suspect image is used in classification. Whereas for localization of spliced region, the chrominance component of input image is divide into overlapping blocks; then, the LDP of each block is calculated. The standard deviation of each block is used as clue to visualize the spliced region. The experimental results are calculated in terms of accuracy, specificity (true negative tare), sensitivity (true positive rate) and error rate and proves effectiveness of the proposed algorithm. The accuracy of the proposed algorithm is 98.55 %. The algorithm is also robust against post splicing image processing operation such as gaussian blur, additive white gaussian noise, JPEG compression and scaling however, previous techniques have not considered these experimental environment.
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Статья научная
Face anti-spoofing (FAS) detection is essential for assuring the safety and dependability of facial identification systems. This study introduces the implementation of a new approach called Spoof-formerNet, which utilizes the high-resolution vision transformer (HR-ViT) system for detecting face anti-spoofing. The Vision Transformer (ViT) architecture has revealed remarkable execution in numerous computer vision applications, and we are now applying it to the intricate field of spoof detection. In order to distinguish between real faces and spoofing attempts, the Spoof-formerNet is engineered to detect minute details and subtle elements embedded in facial photos. We have conducted experimental research wherein the model is trained independently on color (RGB) and depth data in parallel using two streams of HR-ViT networks. Before applying to a classification head, the features from the two streams were concatenated. Spoof-formerNet is trained and tested using well-known benchmark datasets such as CelebA-Spoof, CASIA-SURF, WMCA, and MSU-MFSD, which are commonly used in the field of anti-face spoofing. The suggested model excels in performance and is cutting-edge in identifying genuine faces from spoofing assaults. We assess the model's efficacy by providing comprehensive findings, such as Area Under the Curve (AUC), Attack Presentation Classification Error Rate (APCER), Bona Fide Presentation Classification Error Rate (BPCER), Equal Error Rate (EER), and Average Classification Error Rate (ACER). The results of this work show how cascaded high-resolution vision transformer networks can be used to improve the safety of facial recognition approaches in real-world applications, in addition to advancing facial anti-spoofing technology. The Spoof-formerNet method for face anti-spoofing detection shows good results, with an average AUC of 99.22 and average APCER, BPCER, and ACER of 0.95, 0.66, and 0.81 correspondingly.
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Stabilogram mPCA Decomposition and Effects Analysis of Several Entries on The Postural Stability
Статья научная
This paper presents an analysis of stabilogram using the modified Principal Component Analysis (mPCA) decomposition which will be employed to highlight the effects of different aspects on the human postural stability. The aim of this study is to analyze stabilogram center of pressure time series using the mPCA decomposition method. The mPCA is a decomposition method applied to a complex signal. It decomposes the stabilogram, considered as an additive model, into three components: trend, rambling and trembling. The study of the trace of analytic trembling (respectively of rambling) in the complex plan highlights a unique rotation center. So the phase is defined and two parameters are extracted: the area of the circle in which 95% of the trace's data points are located and the angular frequency. In this study 25 healthy volunteers (average age 31± 11 years) are required to stand upright on an electromagnetic platform either with eyes closed or open and with feet outspread or tighten. Experimental results show the efficiency of the parameter area to identify the effect of visual, proprioceptive and directional entries on the postural stability. These results are able to discriminate between control and young groups and indicate a less well-controlled posture for control subjects (34.5± 7.5y) relatively to young subjects (22.5 ±2. 5y). Results serve also to display that female subjects are more stable than males, that fat subjects are more stable than thin and that tall subjects are more stable than small.
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Statistical Image Classification for Image Steganographic Techniques
Статья научная
Steganography is the method of information hiding. Free selection of cover image is a particular preponderance of steganography to other information hiding techniques. The performance of steganographic system can be improved by selecting the reasonable cover image. This article presents two level unsupervised image classification algorithm based on statistical characteristics of the image which helps Sender to make reasonable selection of cover image to enhance performance of steganographic method based on his specific purpose. Experiments demonstrate the effect of classification in satisfying steganography requirements.
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Statistical Texture Features Based Automatic Detection and Classification of Diabetic Retinopathy
Статья научная
Diabetes is a globally prevalent disease that can cause microvascular compilation such as Diabetic Retinopathy (DR) in the human eye organs and it might prompt a significant reason for visual deficiency. The present study aimed to develop an automatic detection and classification system to diagnosing diabetic retinopathy from digital fundus images. An automated diabetic retinopathy detection and classification system from retinal images is proposed in our work to reduce the workload of ophthalmologists. This work comprises three main stages. Our proposed method first extracts the blood vessels from color fundus image. Secondly, the method detects whatever the input image as normal or diabetic retinopathy and then illustrates an automatic diabetic retinopathy classification technique through statistical texture features. It embeds Gray Level Co-occurrence Matrix (GLCM) and Gray Level Run Length Matrix (GLRLM) for second-order and higher-order statistical texture feature as a feature extraction technique into three renowned classifiers namely K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM). The evaluation results containing a dataset of 644 retinal images indicate that the proposed method based on random forest classifier is found to be effective with a weighted sensitivity, precision, F1-score and accuracy of 95.53% 96.45%, 95.38% and 95.19% respectively for the detection and classification of diabetic retinopathy. These outcomes propose, that the method could decrease the cost of screening and diagnosis while achieving higher than suggested performance and that the system could be implemented in clinical assessments requiring better evaluating.
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Steganography Based on Integer Wavelet Transform and Bicubic Interpolation
Статья научная
Steganography is the art and science of hiding information in unremarkable cover media so as not to observe any suspicion. It is an application under information security field, being classified under information security, Steganography will be characterized by having set of measures that rely on strengths and counter attacks that are caused by weaknesses and vulnerabilities. The aim of this paper is to propose a modified high capacity image steganography technique that depends on integer wavelet transform with acceptable levels of imperceptibility and distortion in the cover image as a medium file and high levels of security. Bicubic interpolation causes overshoot, which increases acutance (apparent sharpness). The Bicubic algorithm is frequently used for scaling images and video for display. The algorithm preserves fine details of the image better than the common bilinear algorithm.
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Stochastic Characterization of a MEMs based Inertial Navigation Sensor using Interval Methods
Статья научная
The aim here remains to introduce effectiveness of interval methods in analyzing dynamic uncertainties for marine navigational sensors. The present work has been carried out with an integrated sensor suite consisting of a low cost MEMs inertial sensor, GPS receiver of moderate accuracy, Doppler velocity profiler and a magnetic fluxgate compass. Error bounds for all the sensors have been translated into guaranteed intervals. GPS based position intervals are fed into a forward-backward propagation method in order to estimate interval valued inertial data. Dynamic noise margins are finally computed from comparisons between the estimated and measured inertial quantities It has been found that the intervals as estimated by proposed approach are supersets of 95% confidence levels of dynamic errors of accelerations. This indicates a significant drift of dynamic error in accelerations which may not be clearly defined using stationary error bounds. On the other side bounds of non-stationary error for rate gyroscope are found to be in consistence with the intervals as predicted using stationary noise coefficients. The guaranteed intervals estimated by the proposed forward backward contractor, are close to 95% confidence levels of stationary errors computed over the sampling period.
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Статья научная
Texture deals with the visual properties of an image. Texture analysis plays a dominant role for image segmentation. In texture segmentation, model based methods are superior to model free methods with respect to segmentation methods. This paper addresses the application of multivariate generalized Gaussian mixture probability model for segmenting the texture of an image integrating with hierarchical clustering. Here the feature vector associated with the texture is derived through DCT coefficients of the image blocks. The model parameters are estimated using EM algorithm. The initialization of model parameters is done through hierarchical clustering algorithm and moment method of estimation. The texture segmentation algorithm is developed using component maximum likelihood under Bayesian frame. The performance of the proposed algorithm is carried through experimentation on five image textures selected randomly from the Brodatz texture database. The texture segmentation performance measures such as GCE, PRI and VOI have revealed that this method outperform over the existing methods of texture segmentation using Gaussian mixture model. This is also supported by computing confusion matrix, accuracy, specificity, sensitivity and F-measure.
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Study for License Plate Detection
Статья научная
License Plate Detection (LPD) system is the application of computer vision and image processing technology. LPD system is the first and main step of License Plate Recognition (LPR) system. So, it performs as the main driver of the LPR system. License plate detection step is always performed in front of the license plate recognition step. LPD system takes the vehicle images as input, follows with the general steps: such as reprocessing, localization, region extraction, and region detection, and the detected image are the output of the system. There are many algorithms for LPD while detecting a license plate in different conditions is still a complex task. For the LPD system, morphological operation and deep learning model are mostly used. This paper presents the critical study of the license plate detection system and also examines the implementation of new technologies of the license plate detection system.
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Study of Noise Detection and Noise Removal Techniques in Medical Images
Статья научная
In this work we taken different medical images like MRI, Cancer, X-ray, and Brain and calculated standard derivations and mean of all these medical images. To finding salt & pepper noise and then applied median filtering technique for removal of noise. After removing a noise by using median filtering techniques again standard derivations and mean are evaluated. This experimental analysis will improve the accuracy of MRI, Cancer, X-ray and Brain images for easy diagnosis. The results, which we have achieved, are more useful and they prove to be helpful for general medical practitioners to analyze the symptoms of the patients.
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Study of segmentation techniques for assessment of osteoarthritis in knee X-ray images
Статья научная
Arthritis is one of the chronic joint disorders that have affected many lives including middle age and older age group. Arthritis exists in many forms and one among them is Osteoarthritis. Osteoarthritis affects the bigger joints like knee, hip, spine, feet etc. Early detection of Osteoarthritis is most essential if not treated properly may result in deformity. The researchers have become more concerned to detect the disorder in the early stage by merging their medical knowledge with machine vision approach in an appropriate way. The objective of this work is to study various segmentation techniques for the detection of Osteoarthritis in the early stage. The different segmentation technique like Sobel and Prewitt edge segmentation, Otsu’s method of segmentation and Texture based segmentation are used to carry out the experimentation. The different statistical features are computed, analyzed and classified. The accuracy rate of 91.16% for Sobel method, 96.80% for Otsu’s method, 94.92% for texture method and 97.55% for Prewitt method is obtained. The results are more promising and competitive which are validated by medical experts.
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Статья научная
Under the background of the deficiencies and shortcomings in traditional diesel engine fault diagnostic, the naïve Bayesian classifier method which built on the basis of the probability density function is adopted to diagnose the fault of diesel engine. A new approach is proposed to weight the super-parent one dependence estimators. To verify the validity of the proposed method, the experiments are performed using 16 datasets collected by University of California Irvine (UCI) and 5 diesel engine datasets collected by our lab. The comparison experimental results with other algorithms demonstrate the effectiveness of the proposed method.
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Study on the Hippocampal Neuron's Minimal Models' Discharge Patterns
Статья научная
The hippocampal CA1 pyramid neuron has plenty of discharge actions. The one-compartment model of CA1 pyramid neuron developed by David is a nine-dimension complex dynamic model. In the thesis, the currents related to the nine-dimension complex model are analyzed and classified by the model’s reduction theory and methods based on neurodynamics, and four minimal models are gotten: (INa+IKdr)-minimal model, (INa+IM)-minimal model, (INa+ICa+Iy)-minimal model, and (INa+ICa+IsAHP)-minimal model. These minimal models have plenty of dynamic actions, and under the current’s stimulation, they can all generate regular discharge and have period discharge pattern, bursting pattern, the chaos discharge pattern, and so on. Compared with the initial nine-dimension complex model, these minimal models’ dimension are much reduced, and are more convenient to numerical simulation, calculating, and analyzing. In addition, these minimal models provide a simpler and flexible method to discuss the specific currents’ dynamic characteristics and functions of the initial nine-dimension complex model by the theory of neurodynamics.
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Subspace based Expression Recognition Using Combinational Gabor based Feature Fusion
Статья научная
This paper demonstrates mainly on enhancement of extracted feature and proposes a novel approach for feature level fusion for efficient expression recognition. Extracted Gabor filter magnitude feature vector has been fused with upper face part geometrical features and Gabor phase feature vector has been fused with lower face part geometrical features respectively. Both these high dimensional feature dataset have been projected into low dimensional subspace for de-correlating the feature data redundancy by preserving local and global discriminative features of various expression classes of JAFFE, YALE and FD databases. The effectiveness of subspace of fused dataset has been measured with different dimensional parameters of Gabor filter. The experimental results reveal that performance of the subspace approaches for high dimensional proposed feature level fused dataset yields higher accuracy rates compared to state of art approaches.
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Статья научная
Premature ventricular contractions (PVC) are premature heartbeats originating from the ventricles of the heart. These heartbeats occur before the regular heartbeat. The Fractal analysis is most mathematical models produce intractable solutions. Some studies tried to apply the fractal dimension (FD) to calculate of cardiac abnormality. Based on FD change, we can identify different abnormalities present in Electrocardiogram (ECG). Present of the uses of Poincaré plot indexes and the sample entropy (SE) analyses of heart rate variability (HRV) from short term ECG recordings as a screening tool for PVC. Poincaré plot indexes and the SE measure used for analyzing variability and complexity of HRV. A clear reduction of standard deviation (SD) projections in Poincaré plot pattern observed a significant difference of SD between healthy Person and PVC subjects. Finally, a comparison shows for FD, SE and Poincaré plot parameters.
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Super Resolution of PET Images using Hybrid Regularization
Статья научная
Positron emission tomography images are used to diagnose, staggering, and monitoring several diseases like cancer and Alzheimer, also, this technique is used in clinical research to help to assess the therapeutic and toxic effects of drugs. However, a main drawback of this modality is the poor spatial resolution due to limiting factors such as positron range, instrumentation limits and the allowable doses of radiotracer for administration to patients. These factors also lead to low signal to noise ratios in the images. In this paper, we proposed to increment the resolution of the image and reduce noise by implementing a super resolution scheme, we proposed to use a hybrid regularization consisting of a TV term plus a Tikhonov term to solve the problem of low resolution and heavy noise. By using an anatomical driven scheme to balance between regularization terms we attain a better resolution image with preservation of small structures like lesions and reduced noise without blurring the edges of images. Experimental results and comparisons with other methods of the state-of-the-art show that our proposed scheme produces better preservation of details without adding artifacts when the resolution factor is increased.
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Supervised Classification Approaches to Analyze Hyperspectral Dataset
Статья научная
In this paper, Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) classification approaches were used to classify hyperspectral image of Georgia, USA, using Environment of Visualizing Images (ENVI). It is a software application used to process and analyze geospatial imagery. Spatial, spectral subset and atmospheric correction have been performed for SAM and SID algorithms. Results showed that classification accuracy using the SAM approach was 72.67%, and SID classification accuracy was 73.12%. Whereas, the accuracy of SID approach is better than SAM approach. Consequently, the two approaches (SID and SAM) have proven to be accurately converged in classification of hyperspectral image of Georgia, USA.
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Survey of Region-Based Text Extraction Techniques for Efficient Indexing of Image/Video Retrieval
Статья научная
With the dramatic increase in multimedia data, escalating trend of internet, and amplifying use of image/video capturing devices; content based indexing and text extraction is gaining more and more importance in research community. In the last decade, many techniques for text extraction are reported in the literature. Methodologies of text extraction from images/videos is generally comprises of text detection and localization, text tracking, text segmentation and optical character recognition (OCR). This paper intends to highlight the contributions and limitations of text detection, localization and tracking phases. The problem is exigent due to variations in the font styles, size and color, text orientations, animations and backgrounds. The paper can serve as the beacon-house for the novice researchers of the text extraction community.
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Survey of Sparse Adaptive Filters for Acoustic Echo Cancellation
Статья научная
This paper reviews the existing developments of adaptive methods of sparse adaptive filters for the identification of sparse impulse response in both network and acoustic echo cancellation from the last decade. A variety of different architectures and novel training algorithms have been proposed in literature. At present most of the work in echo cancellation on using more than one method. Sparse adaptive filters take the advantage of each method and showing good improvement in the sparseness measure performance. This survey gives an overview of existing sparse adaptive filters mechanisms and discusses their advantages over the traditional adaptive filters developed for echo cancellation.
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