Electronic Letters on Computer Vision and Image Analysis最新文献

{"title":"Shot classification for human behavioural analysis in video surveillance applications","authors":"Newlin Shebiah R, Arivazhagan S","doi":"10.5565/rev/elcvia.1713","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1713","url":null,"abstract":"Human behavior analysis plays a vital role in ensuring security and safety of people in crowded public places against diverse contexts like theft detection, violence prevention, explosion anticipation etc. Analysing human behaviour by classifying of videos in to different shot types helps in extracting appropriate behavioural cues. Shots indicates the subject size within the frame and the basic camera shots include: the close-up, medium shot, and the long shot. If the video is categorised as Close-up shot type, investigating emotional displays helps in identifying criminal suspects by analysing the signs of aggressiveness and nervousness to prevent illegal acts. Mid shot can be used for analysing nonverbal communication like clothing, facial expressions, gestures and personal space. For long shot type, behavioural analysis is by extracting the cues from gait and atomic action displayed by the person. Here, the framework for shot scale analysis for video surveillance applications is by using Face pixel percentage and deep learning based method. Face Pixel ratio corresponds to the percentage of region occupied by the face region in a frame. The Face pixel Ratio is thresholded with predefined threshold values and grouped into Close-up shot, mid shot and long shot categories. Shot scale analysis based on transfer learning utilizes effective pre-trained models that includes AlexNet, VGG Net, GoogLeNet and ResNet. From experimentation, it is observed that, among the pre-trained models used for experimentation GoogLeNet tops with the accuracy of 94.61%.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"209 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135976937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced SVM Based Covid 19 Detection System Using Efficient Transfer Learning Algorithms","authors":"Abdelhai LATI, Khaled BENSID, Ibtissem LATI, Chahra GEZZAL","doi":"10.5565/rev/elcvia.1601","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1601","url":null,"abstract":"The detection of the novel coronavirus disease (COVID-19) has recently become a critical task for medical diagnosis. Knowing that deep Learning is an advanced area of machine learning that has gained much of interest, especially convolutional neural network. It has been widely used in a variety of applications. Since it has been proved that transfer learning is effective for the medical classification tasks, in this study; COVID -19 detection system is implemented as a quick alternative, accurate and reliable diagnosis option to detect COVID-19 disease. Three pre-trained convolutional neural network based models (ResNet50, VGG19, AlexNet) have been proposed for this system. Based on the obtained performance results, the pre-trained models with support vector machine (SVM) provide the best classification performance compared to the used models individually.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136078145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning Based Localisation and Segmentation of Prostate Cancer from mp-MRI Images","authors":"Takwa Ben Aïcha, Y. Bouslimi, Afef Kacem Echi","doi":"10.5565/rev/elcvia.1620","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1620","url":null,"abstract":"Prostate Cancer (PCa) is one of the most common diseases in adult males. Currently, mp-MRI imaging represents the most promising technique for screening, diagnosing, and managing this cancer. However, the multiple mp-MRI sequences' visual interpretation is not straightforward and may present crucial inter-reader variability in the diagnosis, especially when the images contradict each other. In this work, we propose a computer-aided diagnostic system to assist the radiologist inlocating and segmenting prostate lesions. As fully convolutional neural networks (UNet) have proved themselves the leading algorithm for biomedical image segmentation, we investigate their use to find PCa lesions and segment for accurate lesions contours jointly. We offer a fully automatic system via MultiResUNet, initially proposed to segment skin cancer. We trained and validated an altered version of the MultiResUnet model using an augmented Radboudumc prostate cancer dataset and obtained encouraging results. An accuracy of 98.34% is achieved, outperforming the concurrent system based on deep architecture.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41918557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Infrared Thermography For Seal Defects Detection On Packaged Products: Unbalanced Machine Learning Classification With Iterative Digital Image Restoration","authors":"Victor Guillot","doi":"10.5565/rev/elcvia.1567","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1567","url":null,"abstract":"Non-destructive and online defect detection on seals is increasingly being deployed in packaging processes, especially for food and pharmaceutical products. It is a key control step in these processes as it curtails the costs of these defects. \u0000To address this cause, this paper highlights a combination of two cost-effective methods, namely machine learning algorithms and infrared thermography. Expectations can, however, be restricted when the training data is small, unbalanced, and subject to optical imperfections. \u0000This paper proposes a classification method that tackles these limitations. Its accuracy exceeds 93% with two small training sets, including 2.5 to 10 times fewer negatives. Its algorithm has a low computational cost compared to deep learning approaches, and does not need any prior statistical studies on defects characterization.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43304210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved Classification of Histopathological images using the feature fusion of Thepade sorted block truncation code and Niblack thresholding","authors":"Sudeep D. Thepade, Abhijeet Bhushari","doi":"10.5565/rev/elcvia.1644","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1644","url":null,"abstract":"Histopathology is the study of disease-affected tissues, and it is particularly helpful in diagnosis and figuring out how severe and rapidly a disease is spreading. It also demonstrates how to recognize a variety of human tissues and analyze the alterations brought on by sickness. Only through histopathological pictures can a specific collection of disease characteristics, such as lymphocytic infiltration of malignancy, be determined. The \"gold standard\" for diagnosing practically all cancer forms is a histopathological picture. Diagnosis and prognosis of cancer at an early stage are essential for treatment, which has become a requirement in cancer research. The importance and advantages of classification of cancer patients into more-risk or less-risk divisions have motivated many researchers to study and improve the application of machine learning (ML) methods. It would be interesting to explore the performance of multiple ML algorithms in classifying these histopathological images. Something crucial in this field of ML for differentiating images is feature extraction. Features are the distinctive identifiers of an image that provide a brief about it. Features are drawn out for discrimination between the images using a variety of handcrafted algorithms. This paper presents a fusion of features extracted with Thepade sorted block truncation code (TSBTC) and Niblack thresholding algorithm for the classification of histopathological images. The experimental validation is done using 960 images present in the Kimiapath-960 dataset of histopathological images with the help of performance metrics like sensitivity, specificity and accuracy. Better performance is observed by an ensemble of TSBTC N-ary and Niblack's thresholding features as 97.92% of accuracy in 10-fold cross-validation.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48265401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Color Image Visual Secret Sharing with Expressive Shares using Color to Gray & Back and Cosine Transform","authors":"R. Chaturvedi, Sudeep D. Thepade, Swati Ahirrao","doi":"10.5565/rev/elcvia.1405","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1405","url":null,"abstract":"Color Visual Secret Sharing (VSS) is an essential form of VSS. It is so because nowadays, most people like to share visual data as a color image. There are color VSS schemes capable of dealing with halftone color images or color images with selected colors, and some dealing with natural color images, which generate low quality of recovered secret. The proposed scheme deals with a color image in the RGB domain and generates gray shares for color images using color to gray and back through compression. These shares are encrypted into an innocent-looking gray cover image using a Discrete Cosine Transform (DCT) to make meaningful shares. Reconstruct a high-quality color image through the gray shares extracted from an innocent-looking gray cover image. Thus, using lower bandwidth for transmission and less storage.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41834067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Machine Learning Approach","authors":"Md Ariful Islam, Mosa. Tania Alim Shampa","doi":"10.5565/rev/elcvia.1604","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1604","url":null,"abstract":"Beach hazards would be any occurrences potentially endanger individuals aswell as their activity. Rip current, or reverse current of the sea, is a typeof wave that pushes against the shore and moves in the opposite direction,that is, towards the deep sea. The management of access to the beach sometimes accidentally push unwary beachgoers forward into rip-prone regions,increasing the probability of a drowning on that beach. The research suggestsan approach for something like the automatic detection of rip currents withwaves crashing based on convolutional neural networks (CNN) and machinelearning algorithms (MLAs) for classification. Several individuals are unableto identify rip currents in order to prevent them. In addition, the absenceof evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have stillimages of something like the shore pervasive and represent a possible causeof rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beachimages, bathymetric images, and beach parameters using CNN and MLAs.The detection model based on CNN for the input features of beach imagesand bathymetric images has been implemented. MLAs have been applied todetect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantlyhigher accuracy and precision. The VGG16 model of CNN shows maximumaccuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. Forthe bathymetric images, the highest performance has been found with anaccuracy of 96.89% (Recall= 0.97, F1-score=0.92) for the DenseNet model of CNN. The MLA-based model shows an accuracy of 86.98% (Recall=0.89,F1-score= 0.90) for random forest classifier. Once we know about the potential zone of rip current continuosly generating rip current, then the coastalregion can be managed accordingly to prevent the accidents occured due tothis coastal hazards.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48479635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An efficient hybrid approach for medical images enhancement","authors":"Sushil Kumar Saroj","doi":"10.5565/rev/elcvia.1574","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1574","url":null,"abstract":"Medical images have various critical usages in the field of medical science and healthcare engineering. These images contain information about many severe diseases. Health professionals identify various diseases by observing the medical images. Quality of medical images directly affects the accuracy of detection and diagnosis of various diseases. Therefore, quality of images must be as good as possible. Different approaches are existing today for enhancement of medical images, but quality of images is not good. In this literature, we have proposed a novel approach that uses principal component analysis (PCA), multi-scale switching morphological operator (MSMO) and contrast limited adaptive histogram equalization (CLAHE) methods in a unique sequence for this purpose. We have conducted exhaustive experiments on large number of images of various modalities such as MRI, ultrasound, and retina. Obtained results demonstrate that quality of medical images processed by proposed approach has significantly improved and better than other existing methods of this field.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45514158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diabetic foot ulcer segmentation using logistic regression, DBSCAN clustering and mathematical morphology operators","authors":"Armando Heras-Tang, Damian Valdés-Santiago, Á. León-Mecías, Marta Lourdes Baguer Díaz-Romañach, J. A. Mesejo-Chiong, C. Cabal-Mirabal","doi":"10.5565/rev/elcvia.1413","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1413","url":null,"abstract":"Digital images are used for evaluation and diagnosis of a diabetic foot ulcer. Selecting the wound region (segmentation) in an image is a preliminary step for subsequent analysis. Most of the time, manual segmentation isn't very reliable because specialists could have different opinions over the ulcer border. This fact encourages researchers to find and test different automatic segmentation techniques. This paper presents a computer-aided ulcer region segmentation algorithm for diabetic foot images. The proposed algorithm has two stages: ulcer region segmentation, and post-processing of segmentation results. For the first stage, a trained machine learning model was selected to classify pixels inside the ulcer's region, after a comparison of five learning models. Exhaustive experiments have been performed with our own annotated dataset from images of Cuban patients. The second stage is needed because of the presence of some misclassified pixels. To solve this, we applied the DBSCAN clustering algorithm, together with dilation, and closing morphological operators. The best-trained model after the post-processing stage was the logistic regressor (Jaccard Index $0.81$, accuracy $0.94$, recall $0.86$, precision $0.91$, and F1 score $0.88$). The trained model was sensitive to irrelevant objects in the scene, but the patient foot. Physicians found these results promising to measure the lesion area and to follow-up the ulcer healing process over treatments, reducing errors.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43527167","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Robust Pedestrian Detection and Path Prediction using Improved YOLOv5","authors":"K. Hajari, U. Gawande, Yogesh Golhar","doi":"10.5565/rev/elcvia.1538","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1538","url":null,"abstract":"In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challengesdue to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a YOLOv5-based deep learning-based pedestrian recognition and path prediction method. The updated YOLOv5 model was first used to detect pedestrians of various sizes and proportions. The proposed path prediction method is then used to estimate the pedestrian's path based on motion data. The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. After then, the path prediction algorithm uses motion and directional data to estimate the pedestrian movement's direction. The proposed method outperforms the existing methods, according to the results of the experiments. Finally, we come to a conclusion and look into future study.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44705471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}