International Journal of Image, Graphics and Signal Processing @ijigsp
Journal articles - International Journal of Image, Graphics and Signal Processing
All articles: 1207
A Preliminary Model of Infrared Image Generation for Exhaust Plume
Scientific article
Based on the irradiance calculation of all pixels on the focal plane array, a preliminary infrared imaging prediction model of exhaust plume that have considered the geometrical and the thermal resolution of the camera was developed to understanding the infrared characteristics of exhaust plume. In order to compute the irradiance incident on each pixel, the gas radiation transfer path in the plume for the instantaneous field of view corresponds to the pixel was solved by the simultaneous equation of a enclosure cylinder which covers the exhaust plume and the line of sight. Radiance of the transfer path was calculated by radiation transfer equation for nonscattering gas. The radiative properties of combustion needed in the equation was provided by employing Malkmus model with EM2C narrow band database(25cm-1). The pressure, species concentration along the path was determination by CFD analysis. The relative irradiance intensity of each pixel was converted to color in the display according to gray map coding and hot map coding. Infrared image of the exhaust plumes from a subsonic axisymmetric nozzle with different relative position of camera and the plume was predicted with the model. By changing the parameters, such as FOV and space resolution, the image of different imaging system can be predicted.
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A Progressive Image Transmission Method Based on Discrete Wavelet Transform (DWT)
Scientific article
In this paper, a wavelet-based progressive image transmission (PIT) scheme is proposed. Here a combined method is proposed to reduce the image browsing time. The proposed scheme transforms a digital image from spatial domain into frequency domain by using discrete wavelet transformation. For wavelet transformation phase we have used Haar wavelet transformation. But it is computationally rigorous. Using concurrent computing we have significantly reduced computation time overhead as well as transmission time. According to the experimental results, the proposed scheme provides the accuracy of reconstructed image and the image browsing time reduces significantly.
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A Quantitative Directional Relations Model Considering Topology and Distance
Scientific article
Original models for direction relations ignored the restriction of topology and distance relations to direction representation. To improve the representation of direction relations model by pondering about the influence of topology and distance relations on direction relations, we categorize direction reference frame into topological reference and coarse directions reference and present a new direction relations quantitative and statistics models based on the new direction reference frames. Instead of degree, this new model uses a coordinate-based quantitative method to describe direction relations for the distance restrain, while it reflects the constraints of topology by the direction reference frame and by the coordinate representation. It covers all intricacies imposed by different types of objects and has more sensitivity to the configuration of objects. Experiments have been carried out and the results indicate the excellent efficiency in view of directional description.
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Scientific article
The rapid growth of motor vehicles in urban areas has led to an increasing demand for parking facilities and requires tariff policies that are more adaptive to real field conditions. This study aims to develop a parking revenue potential classification model to support the formulation of progressive parking tariff policies using a data-driven and spatial analysis approach. The dataset includes vehicle attributes, parking volume, and temporal parking usage patterns. Parking revenue potential is categorized into low, medium, and high classes using a quantile-based approach. Unlike most previous studies that focus on parking occupancy prediction, this study proposes a revenue-oriented spatiotemporal classification model integrating spatial coordinates and temporal parking patterns to support adaptive tariff policy formulation. The Random Forest algorithm is applied to classify parking revenue potential into low, medium, and high categories, achieving an accuracy of 90.24% for two-wheeled vehicles and 89.02% for four-wheeled vehicles. The classification results are integrated into an interactive Streamlit-based dashboard that visualizes the spatial distribution of parking revenue potential and enables simulations of progressive tariff adjustments based on spatial zones and temporal conditions. The proposed system functions as a decision support tool for parking management, aiming to improve operational efficiency and sustainably increase regional parking retribution revenue.
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A Relook and Renovation over State-of-Art Salt and Pepper Noise Removal Techniques
Scientific article
Salt and pepper noise is a type of impulse noise, where certain amount of black and white dots appear in the image. The intensity is accumulated in 8 bit integer, giving 256 possible gray levels in the range (0 – 255).In this range salt and pepper noise takes either minimum or maximum intensity. Positive impulse appears as white (salt) points with intensity '255' and negative impulse appears as black (pepper) points with intensity '0' respectively. Salt and pepper noise removal is not an easy task mostly when noise density in the contaminated image is high and restoration of image quality is essential. Different filters like MF, SMF, AMF, PSMF, DBA, DBUTMF, and MDBUTMF and so on are noticed useful for taking away low, moderate and high density salt and pepper noise. The purpose of this paper is to present these filters first and then revise their art to enhance their performances and usefulness. The comparison shows that some of these filters are very fruitful in some particular noise density levels and hence classified applications on these situations are recommended based on the output of investigations.
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A Research on Visual Analysis of Badminton for Skill Learning
Scientific article
A lot of researches that detect the difference of the proficiency are reported for the dynamic scene of sports. Athlete population increases in late years. However, coach same as before population. In this research, it aimed at the helpful information in the beginner’s skill improvement by using the dynamic scene to play badminton for the clearing shot and aimed to acquire it. We compare standard deviation and average time from Ragging-back of stroke in beginner group and expert group to shot. It pretends and it compares it the detection of tracks of the joint part of the racket head to the shot from the Lagging-back beginning. Beginner and expert’s difference and common features are clarified by comparing images of the shot in the there is a shuttle state and the state of pretense. As a result, the feature and the beginner who drew yen while swinging to expert’s tracks got the feature such as gradual seen from the lowest part of tracks to the shot compared with the expert. Moreover, it has been understood that there is a difference between the beginner group and the expert group also at the time that hangs the shot and stability of the shot.
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A Review Comparison of Wavelet and Cosine Image Transforms
Scientific article
Image compression is the methodology of reducing the data space required to store an image or video. It finds great application in transferring videos and images over the web to reduce data transfer time and resource consumption. A number of methods based on DCT and DWT have been proposed in the past like JPEG, MPEG, EZW, SPIHT etc. The paper presents a review comparison between DCT and DWT compression techniques based on multiple important evaluation parameters like (i) mean squared error and SNR for different threshold values (ii) SNR values and mean squared error for different coefficients (iii) SNR values and mean squared error for different window size. In addition, the paper also makes two advanced studies (i) CPU utilization and compression ratio for different window sizes (ii) SNR and compression with different compression ratio. The experimentation is performed on multiple 8x8 jpeg images.
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Scientific article
Ophthalmology is the study of structures, functions, treatment and disorders of eye. Computer aided analysis of retina images is still an open research area. Numerous efforts have been made to automate the analysis of retina images. This paper presents a review of various existing research in detection of anatomical structures in retina and lesions for the diagnosis of diabetic retinopathy (DR). The research in detection of anatomical structures is further divided into subcategories, namely, vessel segmentation and vessel centerline extraction, optic disc segmentation and localization, and fovea/ macula detection and extraction. Various research works in each of the categories are reviewed highlighting the techniques employed and comparing the performance figures obtained. The issues/ lacuna of various approaches are brought out. The following major observations are made: Most of the vessel detection algorithms fail to extract small thin vessels having low contrast. It is difficult to detect vessels at regions where close vessels are merged, at regions of missing of small vessels, at optic disc regions, and at regions of pathology. Machine learning based approaches for blood vessel tracing requires long processing time. It is difficult to detect optic disc radius or boundary with simple blood vessel tracing. Automatic detection of fovea and macular region extraction becomes complicated due to non-uniform illuminations while imaging and diseases of the eyes. Techniques requiring prior knowledge leads to complexity. Most lesion detection algorithms underperform due to wide variations in the color of fundus images arising out of variations in the degree of pigmentation and presence of choroid.
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A Review of Self-supervised Learning Methods in the Field of Medical Image Analysis
Scientific article
In the field of medical image analysis, supervised deep learning strategies have achieved significant development, while these methods rely on large labeled datasets. Self-Supervised learning (SSL) provides a new strategy to pre-train a neural network with unlabeled data. This is a new unsupervised learning paradigm that has achieved significant breakthroughs in recent years. So, more and more researchers are trying to utilize SSL methods for medical image analysis, to meet the challenge of assembling large medical datasets. To our knowledge, so far there still a shortage of reviews of self-supervised learning methods in the field of medical image analysis, our work of this article aims to fill this gap and comprehensively review the application of self-supervised learning in the medical field. This article provides the latest and most detailed overview of self-supervised learning in the medical field and promotes the development of unsupervised learning in the field of medical imaging. These methods are divided into three categories: context-based, generation-based, and contrast-based, and then show the pros and cons of each category and evaluates their performance in downstream tasks. Finally, we conclude with the limitations of the current methods and discussed the future direction.
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A Review on Digital Image Watermarking Techniques
Scientific article
Nowadays, Multimedia security [1] is a major issue. Images, video, audio, text files are losing their credibility day by day as they can be distorted or manipulated by using several tools. Ensuring the authenticity [2] and integrity of digital media is a major issue. The manipulation made by forgery tools are so smoothly done that we don’t even suspect that forgery may be involved in digital content. Multimedia data is facing several issues related to illegal distribution, duplication and manipulation of information conveyed by them. The digital watermarking [3] technique plays an important role in protecting digital content. In this paper, On the basis of their operating principles different watermarking techniques are categorized [4]. Attacks, applications and requirements [5] related to watermarking techniques are also discussed. Different watermarking techniques proposed by researchers for protecting copyrights of digital media are presented which are based on spatial and frequency domain. Frequency domain are getting much more attention due to use of wavelets which have high degree of resemblance to human visual system. In digital watermarking, secret information is embedded with original data for maintaining ownership rights of the digital content. Spatial domain watermarking techniques work over pixel characteristics and frequency domain watermarks concerned about different transformations that can be used with digital content. Imperceptibility, robustness, security, complexity and capacity are some requirements of the digital watermarking which completely depends on the algorithm used for watermarking.
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A Review on Graph Based Segmentation
Scientific article
Image segmentation plays a crucial role in effective understanding of digital images. Past few decades saw hundreds of research contributions in this field. However, the research on the existence of general purpose segmentation algorithm that suits for variety of applications is still very much active. Among the many approaches in performing image segmentation, graph based approach is gaining popularity primarily due to its ability in reflecting global image properties. This paper critically reviews existing important graph based segmentation methods. The review is done based on the classification of various segmentation algorithms within the framework of graph based approaches. The major four categorizations we have employed for the purpose of review are: graph cut based methods, interactive methods, minimum spanning tree based methods and pyramid based methods. This review not only reveals the pros in each method and category but also explores its limitations. In addition, the review highlights the need for creating a database for benchmarking intensity based algorithms, and the need for further research in graph based segmentation for automated real time applications.
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A Review on HEVC Video Forensic Investigation under Compressed Domain
Scientific article
In recent years, video forensic investigation has become a prominent research area, due to the adverse effect of fake videos on networks, people and society. This paper summarizes all the existing methodologies used for forgery detection in H.265/HEVC videos. HEVC video forgery is generally classified into two categories as video quality forgery and video content forgery. The occurrence of various forgeries such as transcoding, fake-bitrate, inter-frame forgery and intra-frame forgery is deeply analyzed based on features extracted from the HEVC compression domain. The major findings of this research are (i) Less focus on transcoding detection, (ii) Non-availability of HEVC forged video dataset (iii) More focus on double compression detection for forgery detection, and (iv) Non-consideration of adaptive-GOP structure. The forgery detection in the video is critically important due to its wide use as the primary source of information in criminal investigations and proving the authenticity of contents. So, the forgery detection accuracy is of major concern at the present time. Although, various forgery detection methods are developed in past but the findings of this review point out the need of developing more effective detection methods with high accuracy.
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A Review on Image Reconstruction Using Compressed Sensing Algorithms: OMP, CoSaMP and NIHT
Scientific article
A sampled signal can be properly reconstructed if the sampling rate follows the Nyquist criteria. If Nyquist criteria is imposed on various image and video processing applications, a large number of samples are produced. Hence, storage, processing and transmission of these huge amounts of data make this task impractical. As an alternate, Compressed Sensing (CS) concept was applied to reduce the sampling rate. Compressed sensing method explores signal sparsity and hence the signal acquisition process in the area of transformation can be carried out below the Nyquist rate. As per CS theory, signal can be represented by alternative non-adaptive linear projections, which preserve the signal structure and the reconstruction of the signal can be achieved using optimization process. Hence signals can be reconstructed from severely undersampled measurements by taking advantage of their inherent low-dimensional structure. As Compressed Sensing, requires a lower sampling rate for reconstruction, data captured within the specified time will be obviously less than the traditional method. In this paper, three Compressed Sensing algorithms, namely Orthogonal Matching Pursuit (OMP), Compressive Sampling Matching Pursuit (CoSaMP) and Normalized Iterative Hard Thresholding (NIHT) are reviewed and their performance is evaluated at different sparsity levels for image reconstruction.
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A Review on Image Reconstruction through MRI k-Space Data
Scientific article
Image reconstruction is the process of generating an image of an object from the signals captured by the scanning machine. Medical imaging is an interdisciplinary field combining physics, biology, mathematics and computational sciences. This paper provides a complete overview of image reconstruction process in MRI (Magnetic Resonance Imaging). It reviews the computational aspect of medical image reconstruction. MRI is one of the commonly used medical imaging techniques. The data collected by MRI scanner for image reconstruction is called the k-space data. For reconstructing an image from k-space data, there are various algorithms such as Homodyne algorithm, Zero Filling method, Dictionary Learning, and Projections onto Convex Set method. All the characteristics of k-space data and MRI data collection technique are reviewed in detail. The algorithms used for image reconstruction discussed in detail along with their pros and cons. Various modern magnetic resonance imaging techniques like functional MRI, diffusion MRI have also been introduced. The concepts of classical techniques like Expectation Maximization, Sensitive Encoding, Level Set Method, and the recent techniques such as Alternating Minimization, Signal Modeling, and Sphere Shaped Support Vector Machine are also reviewed. It is observed that most of these techniques enhance the gradient encoding and reduce the scanning time. Classical algorithms provide undesirable blurring effect when the degree of phase variation is high in partial k-space. Modern reconstructions algorithms such as Dictionary learning works well even with high phase variation as these are iterative procedures.
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A Robust Color Image Watermarking Scheme Using Discrete Wavelet Transformation
Scientific article
Information hiding in digital media such as audio, video and or images in order to establish the owner rights and to protect the copyrights commonly known as digital watermarking has received considerable attention of researchers over last few decades and lot of work has been done accordingly. A number of schemes and algorithms have been proposed and implemented using different techniques. The effectiveness of the technique depends on the host data values chosen for information hiding and the way watermark is being embedded in them. However, in view of the threats posed by the online pirates, the robustness and the security of the underlying watermarking techniques have always been a major concern of the researchers. This paper presents a secure and robust watermarking technique for color images using Discrete Wavelet Transformation. The results obtained have shown that the technique is robust against various common image processing attacks.
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Scientific article
Because of the nature of the Internet and the growing number of people using digital media, copyright protection is becoming more important. One of the most common ways to protect this is by implementing digital image watermarking. This protection method safeguards the image from unauthorized access. The Gorilla Troop Optimization Algorithm (GTO), a new evolutionary algorithm, is what we propose to be a powerful watermarking technique. Initially, we applied Discrete Wavelet Transform (DWT) to the cover image, followed by Singular Value Decomposition (SVD) for enhanced security, and finally, we applied SVD to the Watermark image for its embedding into the cover image. In this process, we aim to optimize the multiple scaling factors (MSFs) by applying the GTO algorithm and testing the proposed algorithm in the MATLAB environment using some standard images. We then evaluated the experiment using performance metrics such as Normalized Cross-Correlation (NCC), the Structural Similarity Index (SSIM), and the Peak Signal-to-Noise Ratio (PSNR). These metrics proved the imperceptibility of different attacks and the proposed algorithm’s performance.
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A Robust Face Recognition System in Image and Video
Scientific article
Face detection and recognition has always been one of the research interests to researchers in the field of the biometric identification of individuals. Problems such as environmental lighting, different skin color, complex background, etc affect on the detection and recognition of individuals. This paper proposes a method to enhance the performance of face detection and recognition systems. Our method, basically consists of two main parts: firstly, we detect faces and then recognize the detected faces. In the detection step, we use the skin color segmentation combined with AdaBoost algorithm, which is fast and also more accurate compared to the other known methods. Also, we use a series of morphological operators to improve the face detection performance. Recognition part consists of three steps: dimension reduction using Principal Component Analysis (PCA), feature selection using Linear Discriminant Analysis (LDA), and k-Nearest Neighbor (K-NN) or Support Vector Machine (SVM) based classification. Combination of PCA and LDA is used for improving the capability of LDA when a few samples of images are available. We test the system on the face databases. Experimental results show that the system is robust enough to detect faces in different lighting conditions, scales, poses, and skin colors from various races. Also, the system is able to recognize face with less misclassification compared to the previous methods.
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A Robust Hybrid Deep Learning Model for Multiclass Depression Classification from Speech Audio
Scientific article
Depression remains one of the most prevalent and underdiagnosed mental health disorders globally, necessitating scalable, objective, and non-invasive diagnostic tools. Speech, as a rich biomarker of emotional and psychological states, offers a promising avenue for automated depression detection. This study proposes a robust hybrid deep learning framework that integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer architectures to classify depression severity into three levels: normal, mild, and severe. Using a curated multimodal dataset comprising 400 labeled audio recordings, we extract comprehensive acoustic features, including MFCC, Chroma, Spectrogram, Contrast, and Tonnetz representations. Models are evaluated using precision, recall, F1-score, and accuracy. Experimental results show that the proposed hybrid models outperform traditional architectures, achieving up to 99% accuracy and strong generalization across all classes. This study demonstrates the potential of attention-enhanced hybrid architectures in mental health assessment and provides a foundation for future deployment in clinical and real-world settings. Future work includes multimodal fusion with EEG data and the implementation of explainable AI for clinical interpretability.
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A Robust Image Watermarking Scheme Based Multiresolution Analysis
Scientific article
Digital watermarking has been widely applied to solve copyright protection problems of digital media relating to illegal use of distributions. In digital watermarking, a watermark is embedded into a cover image in such way that the resulting watermarked signal is robust to certain distortion. This paper presents a digital image watermarking based on Discrete Wavelet Transform (DWT). In the proposed method, the watermark as well as the cover image seldom looses the quality in both embedding and extraction process. The embedding process is carried out by tetra-furcating the watermark and embedded into the sub-bands of cover image. Signal to Noise Ratio (SNR) and Peak Signal to Noise Ratio (PSNR) are computed to measure image quality for the DWT transform. We present traces of host and watermarked images. From the traces we observe that, we get good SNR and PSNR with DWT. Experiment evaluation demonstrates that the proposed scheme is able to withstand a variety of attacks. This scheme shows good performance on different types of cover images in terms of imperceptibility and resist to jpeg compression.
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A Robust Median-based Background Updating Algorithm
Scientific article
Image processing techniques for object tracking, identification and classification have become common today as a result of improved quality of cameras as well as prices of cameras becoming cheaper and cheaper day by day. The use of cameras also make it possible for human analysis of video streams or images where it is difficult for robots or algorithms or machines to effectively deal with the images. However, the use of cameras for basic tracking and analysing do not come without challenges such as issues with sudden changes in illumination, shadows, occlusion, noise, and high computational time and space complexities of algorithms. A typical image processing task may involve several subtasks such as capturing, and pre-processing which demand high computational resources to complete. One of the main pre-processing tasks used in image processing is image segmentation which enables images to be divided into sections of interest in order to perform analysis on them. Background Subtraction is commonly used to segment images into Background and Foreground for further processing. Algorithms producing highly accurate results during this segmentation task normally demand high computation time or memory space, while algorithms that use smaller memory space and shorter time to complete this segmentation task may also suffer from limitations that may lead to undesired results at some point in time. Poor outputs from algorithms will eventually lead to system failure which must be avoided as much as possible. This paper proposes a median based background updating algorithm which determines the median of a buffer containing values that are highly correlated. The algorithm achieves this by deletingan extreme valuefrom the buffer whenever data is to be added to it.Experiments show that the method produces good results with less computational time which will make it possible to implement on devices that do not have much computation resources.
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