International Journal of Image, Graphics and Signal Processing @ijigsp
Статьи журнала - International Journal of Image, Graphics and Signal Processing
Все статьи: 1181
Статья научная
Biometrics has gained significant popularity for individual identification in the last decades as a necessity of supporting especially the law enforcement and personal authentication required applications. The face is one of the distinctive biometrics that can be used to identify an individual. Henceforth, Face Recognition (FR) has attracted the great interest of the scientists and academicians. One of the most popular methods preferred for FR is extracting textual features from face images and subsequently performing classification according to these features. A substantial portion of the previous texture analysis and classification studies have based on extracting features from Gray Level Co-occurrence Matrix (GLCM). In this study, we present an alternative method that utilizes Gray Level Total Displacement Matrix (GLTDM) which holds statistical information about the Discrete Wavelet Transform (DWT) of the original face image. The approximation and three detail sub-bands of the image are first calculated. GLTDMs that are specific to these four matrices are subsequently generated. The Haralick features are extracted from those generated four GLTDMs. At the following stage, a new joint feature vector is formed using these four groups of Haralick features. Lastly, extracted features are classified by using K-NN algorithm. As demonstrated in the simulation results, the proposed approach performs promising results in the context of classification.
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Статья научная
Effective communication is paramount in election campaigns, and slogans are crucial in conveying messages and eliciting voter sentiment. This paper introduces CRFVReC (Conditional Random Field with Variable-Length Receptive Fields), a statistical modeling technique to categorize and generate election campaign slogans by analyzing their sentiment. It is a novel approach for sentiment-based slogan generation and analysis in election campaigns. The reason was to choose datasets that precisely capture voter sentiment from a variety of sources such as social media (SM) posts, public comments, and news articles. The datasets were meticulously chosen to encompass a broad spectrum of sentiments and issues that are pertinent to voters. The CRFVReC model was set up to maximize the performance of sentiment classification and slogan generation. Modifying parameters such as the length of the receptive field to match the length of slogans enhanced the model's adaptability and increased its accuracy. Utilizing Conditional Random Fields (CRFs), CRFVReC classifies election campaign slogans into optimistic and pessimistic sentiments and generates slogans that resonate emotionally with voters. The key objectives of this study are twofold: first, to accurately classify election campaign slogans into two primary sentiment categories, optimistic and pessimistic, and second, to generate emotionally resonant slogans that can effectively connect with voters. Extensive experiments and sentiment analyses are conducted using a diverse dataset of election campaign slogans to assess the efficiency of CRFVReC. The results highlight the model's remarkable precision in sentiment classification, demonstrating its capability to discriminate between optimistic and pessimistic sentiment in slogans. The model exhibits elevated accuracy, precision, recall, and AUC scores in sentiment classification, utilizing a diverse dataset. Furthermore, CRFVReC showcases its creative potential in generating slogans with emotionally compelling content. Its capability holds significant promise for campaign strategists and political communicators seeking to craft slogans that resonate with voters deeply emotionally. Additionally, the model's adaptability to slogans of varying lengths makes it a versatile tool for election campaign management and strategy development. The CRFVReC emerges as a robust and adaptable solution for sentiment-based slogan generation and analysis in the complex landscape of election campaigns. Its contributions lie not only in inaccurate sentiment classification but also in its potential to shape the narrative of political campaigns through the creation of emotionally impactful slogans. This research contributes to the fields of political communication and campaign management, providing valuable tools and insights for practitioners and researchers.
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Disorder Facial Emotion Recognition with Grey Wolf Optimized Gefficient-Net for Autism Spectrum
Статья научная
Autism spectrum disorder (ASD) is a neurological issue that impacts brain function at an earlier stage. The autistic person realizes several complexities in communication or social interaction. ASD detection from face images is complicated in the field of computer vision. In this paper, a hybrid GEfficient-Net with a Gray-Wolf (GWO) optimization algorithm for detecting ASD from facial images is proposed. The proposed approach combines the advantages of both EfficientNet and GoogleNet. Initially, the face image from the dataset is pre-processed, and the facial features are extracted with the VGG-16 feature extraction technique. It extracts the most discriminative features by learning the representation of each network layer. The hyperparameters of GoogleNet are optimally selected with the GWO algorithm. The proposed approach is uniformly scaled in all directions to enhance performance. The proposed approach is implemented with the Autistic children’s face image dataset, and the performance is computed in terms of accuracy, sensitivity, specificity, G-mean, etc. Moreover, the proposed approach improves the accuracy to 0.9654 and minimizes the error rate to 0.0512. The experimental outcomes demonstrate the proposed ASD diagnosis has achieved better performance.
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Displaying Images and Their Characteristics from Websites on Users Computers
Статья научная
The subject of the research in this scientific paper are images on the websites, with special emphasis on displaying images chosen by the Web designer, along with its characteristic, on computers of various users. In addition, users can have different operating systems, different browsers, and different preferences in terms of their computers settings. An overall direction for using images and their characteristics when designing web pages, as well as some advice and opinions on the same topic are presented here. After that, several problems which arise from displaying images on the web pages of the computer of users are analyzed, for which a few solutions for the problems, as well as recommendations on which solution when to be chosen are also given in this text. A problem with a speed for loading web pages in correlation with size of images on those pages is studied as well. Then, problems with a speed for loading web pages in correlation with number of images on the page, problems with loading speed of second image on rollover, problems with a speed for loading web pages in correlation with size of background image, problems with texture in vertical bars used for background in web pages, and problems with users monitor size and background image are also analyzed. Finally, the problem with displaying the page without specifying image height and width is also considered.
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Dominant Frequency Enhancement of Speech Signal to Improve Intelligibility and Quality
Статья научная
In mobile devices, perceived speech signal deteriorates significantly in the presence of near-end noise as the signal arrives directly at the listener's ears in a noisy environment. There is an inherent need to increase the clarity and quality of the received speech signal in noisier environment. It is accomplished by incorporating speech enhancement algorithms at the receiver end. The objective is to improve the intelligibility and quality of the speech signal by dynamically enhancing the speech signal when the near-end noise dominates. This paper proposes a speech enhancement approaches by inculcating the threshold of hearing and auditory masking properties of the human ear. Incorporating the masking properties, the speech samples that are audible can be obtained. In low SNR environments, selective audible samples can be enhanced to improve the clarity of the signal rather than enhancing every loud sample. Intelligibility and quality of the enhanced speech signal are measured using Speech Intelligibility Index and Perceptual Evaluation of Speech Quality. Experimental results connote the intelligibility and quality improvement of the speech signal with the proposed method over the unprocessed far-end speech signal. This approach is efficient in overcoming the deterioration of speech signals in a noisy environment.
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Driver's Face Tracking Based on Improved CAMShift
Статья научная
The statistic shows that the number of casualty increase in every year due to road accident related to driver drowsiness. After long journey or sleepless night, vehicle driver will perform some bio-features with regard to drowsiness on them face. It is self-evident that getting location information of head in continuous monitoring and surveillance system rapidly and accurately can help prevent many accidents, and consequently save money and reduce personal suffering. In this paper, according the real situation in vehicle, an improved CAMShift approach is proposed to tracking motion of driver’s head. Results from experiment show the significant performance of proposed approach in driver’s head tracking.
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Drone Detection from Video Streams Using Image Processing Techniques and YOLOv7
Статья научная
For ensuring the safety issues, a country should establish a secure monitoring system around the most important places. Due to the huge development in unmanned aerial vehicles (UAV), drone detection is a vital part of the safety monitoring system for reducing threats from neighboring countries or terrorist groups. This paper presents a deep learning-based drone detection method. A You Only Look Once (YOLO) v7 architecture is used to train on the dataset. The training dataset consists of drone images in various environments. The trained model was tested on multiple videos of drones from YouTube. Experimental results demonstrate that the model exhibited a recall of 0.9656 and a precision of 0.9509. In addition, the performance of the model compares with the state-of-art models with YOLOv8, YOLO-NAS, Faster-RCNN architectures and it outperforms the other models by maintaining a more stable precision and recall curve.
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Статья научная
Onion size is a crucial physiological characteristic that can be explained by a number of factors, including diameter, weight, volume, and length. Determining the size of onions is frequently necessary for sorting them for a variety of reasons, including processing machine specifications, legal requirements for sorting standards, and consumer preferences. In the process of phenotyping onions, size is another crucial quantitative feature to consider. Traditionally, algorithms based on morphology, colour, thresholding, and geometric approaches have been used to estimate the shape and size of onions. However, research that relies on these geometric or colour-based functions is limited to approximations and frequently produces erroneous results when conducted at precisely controlled heights. Healthy onions are collected and utilized as an input dataset for this paper. The gathered images are pre-processed to reduce noise and improve contrast by applying the circular adaptive median filter and homomorphic filtering with Elk-herd optimization. Next, utilizing the dilated and deformable feature pyramid network, object detection is performed on the pre-processed images. To segment the onion from the image for removing the unwanted portions, an edge-based segmentation algorithm is used, such as an edge-attention guidance network. The dual attention fusion-net, which ranks data into labelled groups and measures onion size. Accuracy, confusion metrics, FDR, hit rate, and other performance metrics are assessed for both the current and proposed models in the proposed model. Consequently, the suggested onion size detection approach outperforms the current algorithm. This method produced 97.6% accuracy, 2.9% FDR, 96% Hit Rate, 98.5% Selectivity, and 97.3% NPV. Thus, this proposed approach is the best choice for detecting the size of the onion.
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Dual Transition Uniform Lbp Matrix for Efficient Image Retrieval
Статья научная
Texture image retrieval plays a significant and important role in these days, especially in the era of big-data. The big-data is mainly represented by unstructured data like images, videos and messages etc. Efficient methods of image retrieval that reduces the complexity of the existing methods is need for the big-data era. The present paper proposes a new method of texture retrieval based on local binary pattern (LBP) approach. One of the main disadvantages of LBP is, it generates 256 different patterns on a 3x3 neighborhood and a method based on this for retrieval needs 256 comparisons which is very tedious and complex. The retrieval methods based on uniform LBP's which consists of 59 different patterns of LBP is also complex in nature. To overcome this, the present paper divided LBP into dual LBP's consisting four pixels. The present paper based on this dual LBP derived a 2-dimensional dual uniform LBP matrix (DULBPM) that contains only four entries. The texture image retrieval is performed using these four entries of DULBPM. The proposed method is evaluated on the animal fur, car, leaf and rubber textures.
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Dynamic Adaptive Median Filter (DAMF) for Removal of High Density Impulse Noise
Статья научная
This paper proposes a novel adaptive filtering scheme to remove impulse noise from images. The scheme replaces the corrupted test pixel with the median value of non-corrupted neighboring pixels selected from a window dynamically. If the number of non-corrupted pixels in the selected window is not sufficient, a window of next higher size is chosen. Thus window size is automatically adapted based on the density of noise in the image as well as the density of corruption local to a window. As a result window size may vary pixel to pixel while filtering. The scheme is simple to implement and do not require multiple iterations. The efficacy of the proposed scheme is evaluated with respect to subjective as well as objective parameters on standard images on various noise densities. Comparative analysis reveals that the proposed scheme has improved performance over other schemes, preferably in high density impulse noise cases. Further, the computational overhead is also less as compared its competent scheme.
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Dynamic Summarization of Video Using Minimum Edge Weight Matching in Bipartite Graphs
Статья научная
To select the long-running videos from online archives and other collections, the users would like to browse, or skim through quickly to get a hint on the semantic content of the videos. Video summarization addresses this problem by providing a short video summary of a full-length video. An ideal video summary would include all the important segments of the video and remain short in length. The problem of summarization is extremely challenging and has been a widely pursued subject of recent research. There are many algorithms presented in literature for video summarization and they represent visual information of video in concise form. Dynamic summaries are constructed with collection of key frames or some smaller segments extracted from video and is presented in the form of small video clip. This paper describes an algorithm for constructing the dynamic summary of a video by modeling every 40 consecutive frames of video as a bipartite graph. The method considers every 20 consecutive frames from video as one set and next 20 consecutive frames as second set of bipartite graph nodes with frames of the video representing nodes of the graph and edges connecting nodes denoting the relation between frames and edge weight depicting the mutual information between frames. Then the minimum edge weight maximal matching in every bipartite graph (a set of pair wise non-adjacent edges) is found using Hungarian method. The frames from the matchings which are represented by the nodes connected by the edges with weight below some empirically defined threshold and two neighbor frames are taken as representative frames to construct the summary. The results of the experiments conducted on data set containing sports videos taken from YOUTUBE and videos of TRECVID MED 2011 dataset have demonstrated the satisfactory average values of performance parameters, namely Informativeness value of 94 % and Satisfaction value of 92 %. These values and duration (MSD) of summaries reveal that the summaries constructed are significantly concise and highly informative and provide highly acceptable dynamic summary of the videos.
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E-Chars74k: An Extended Scene Character Dataset with Augmentation Insights and Benchmarks
Статья научная
Semantic understanding of camera-captured scene text images is an important problem in computer vision. Scene character recognition is the pivotal task in this problem, and deep learning is now-a-days the most prospective approach. However, limited sample-size of scene character datasets appear to be a major hindrance for training deep networks. In this paper, we present (i) various augmentation techniques for increasing the sample size of such datasets along with associated insights, (ii) an extended version of the popular Chars74k dataset (herein referred to as E-Chars74k), and (iii) the benchmark performance on the developed E-Chars74k dataset. Experiments on various sets of data such as digits, alphabets and their combination, belonging to the usual as well as wild scenarios, clearly reflect significant performance gain (20%-30% increase in scene character recognition accuracy). It is noteworthy to mention that in all these experiments, a deep convolutional neural network powered with two conv-pool pairs is trained with the uniform training test partition to foster comparison on equal bench.
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EEG based Autism Diagnosis Using Regularized Fisher Linear Discriminant Analysis
Статья научная
Diagnosis of autism is one of the difficult problems facing researchers. To reveal the discriminative pattern between autistic and normal children via electroencephalogram (EEG) analysis is a big challenge. The feature extraction is averaged Fast Fourier Transform (FFT) with the Regulated Fisher Linear Discriminant (RFLD) classifier. Gaussinaty condition for the optimality of Regulated Fisher Linear Discriminant (RFLD) has been achieved by a well-conditioned appropriate preprocessing of the data, as well as optimal shrinkage technique for the Lambda parameter. Winsorised Filtered Data gave the best result.
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EFF-ViBE: an efficient and improved background subtraction approach based on ViBE
Статья научная
Background subtraction plays an important role in intelligent video surveillance since it is one of the most used tools in motion detection. If scientific progress has enabled to develop sophisticated equipment for this task, algorithms used should be improved as well. For the past decade a background subtraction technique called ViBE is gaining the field. However, the original algorithm has two main drawbacks. The first one is ghost phenomenon which appears if the initial frame contains a moving object or in the case of a sudden change in the background situations. Secondly it fails to perform well in complicated background. This paper presents an efficient background subtraction approach based on ViBE to solve these two problems. It is based on an adaptive radius to deal with complex background, on cumulative mean and pixel counting mechanism to quickly eliminate the ghost phenomenon and to adapt to sudden change in the background model.
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Статья научная
Radio resource allocation in VCN is a challenging role in an intelligent transportation system due to traffic congestion. Lot of time is wasted because of traffic congestion. Due to traffic congestion, user have to miss their important work. In this paper, we propose radio resource allocation scheme so that user can utilize their time by taking the advantage of subscription plan. In this scenario, multitype vehicle identification scheme from real time traffic database is proposed, its history will match in transport database and vehicle travelling history database. Proposed method indicates 95% accuracy for multitype vehicle detection. Subscription plans are allocated to the user on the basis of resource allocation, scheduling, levelling and forecasting. This scheme is better for traffic management, vehicle tracking as well as time management.
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EQ: An Eigen Image Quality Assessment based on the Complement Feature
Статья научная
An Eigen formulation is proposed for image quality assessment IQA. Each block is represented by an array composed of feature vectors (intensity/color at this stage). After attaching the complement feature(s), the auto-correlation matrix is computed for each block. The proposed full reference FR-IQA is simply the deviation of the Eigen values of the degraded image from that of the original image. Interestingly, the second largest Eigen value was sufficient to perform this comparison. Results and comparisons with SSIM and GMSD schemes on different types of degradation are demonstrated to show the effectiveness of the proposed schemes. Using TID2013 database, the proposed scheme outperforms SSIM. In addition, the proposed schemes is closer to the MOS score compared to GMSD; however, the correlation with MOS is inferior as illustrated in the tables. These results are concluded from the average behaviour on all the images using all degradations (with 5 levels) on the database.
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Early detection of osteoarthritis based on cartilage thickness in knee X-ray images
Статья научная
Arthritis is a joint disorder featuring inflammation. There are numerous forms of Arthritis. Arthritis essentially causes joint dis-functioning which may further tend to cause deformity and disability. Osteoarthritis (OA) is one form of arthritis which is mostly seen in old age group. A patient suffering from OA needs to visit medical experts where clinical and radiographic examination is carried out. Analysis of bone structures in initial stage is bit complex. So any vague conclusion drawn from the radiographic images may make the treatment faulty and troublesome. Thus to overcome this we have developed an algorithm that computes the cartilage area/thickness using various shape descriptors. The computed descriptors obtained the accuracy of 99.81% for K-nearest neighbour classifier and 95.09% for decision tree classifier. The estimated cartilage thickness is validated by radiographic experts as per KL grading framework which will be helpful to the doctors for quick and appropriate analysis of ailment in the early stage. The results are competitive and promising as reported in the literature.
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Earth Observation Satellites Scheduling Based on Decomposition Optimization Algorithm
Статья научная
A decomposition-based optimization algorithm was proposed for solving Earth Observation Satellites scheduling problem. The problem was decomposed into task assignment main problem and single satellite scheduling sub-problem. In task assignment phase, the tasks were allocated to the satellites, and each satellite would schedule the task respectively in single satellite scheduling phase. We adopted an adaptive ant colony optimization algorithm to search the optimal task assignment scheme. Adaptive parameter adjusting strategy and pheromone trail smoothing strategy were introduced to balance the exploration and the exploitation of search process. A heuristic algorithm and a very fast simulated annealing algorithm were proposed to solve the single satellite scheduling problem. The task assignment scheme was valued by integrating the observation scheduling result of multiple satellites. The result was responded to the ant colony optimization algorithm, which can guide the search process of ant colony optimization. Computation results showed that the approach was effective to the satellites observation scheduling problem.
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Edge Detection Operators: Peak Signal to Noise Ratio Based Comparison
Статья научная
Edge detection is the vital task in digital image processing. It makes the image segmentation and pattern recognition more comfort. It also helps for object detection. There are many edge detectors available for pre-processing in computer vision. But, Canny, Sobel, Laplacian of Gaussian (LoG), Robert’s and Prewitt are most applied algorithms. This paper compares each of these operators by the manner of checking Peak signal to Noise Ratio (PSNR) and Mean Squared Error (MSE) of resultant image. It evaluates the performance of each algorithm with Matlab and Java. The set of four universally standardized test images are used for the experimentation. The PSNR and MSE results are numeric values, based on that, performance of algorithms identified. The time required for each algorithm to detect edges is also documented. After the Experimentation, Canny operator found as the best among others in edge detection accuracy.
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Edge Detection System using Pulse Mode Neural Network for Image Enhancement
Статья научная
Edge detection of an image reduces significantly the amount of data and filters out information that may be regarded as less irrelevant. Edge detection is efficient in medical imaging. Pulse mode neural networks are becoming an attractive solution for function approximation based on frequency modulation. Early pulse mode implementation suffers from some network constraints due to weight range limitations. To provide the best edge detection, the basic algorithm is modified to have pulse mode operations for effective hardware implementation. In this project a new pulse mode network architecture using floating point operations is used in the activation function. By using floating point number system for synapse weight value representation, any function can be approximated by the network. The proposed pulse mode MNN is used to detect the edges in images forming a heterogeneous data base. It shows good learning capability. In addition, four edge detection techniques have been compared. The coding is written in verilog and the final result have been simulated using Xilinx ISE simulator.
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