ELCVIA Electronic Letters on Computer Vision and Image Analysis https://elcvia.cvc.uab.cat/ Electronic Journal on Computer Vision and Image Analysis en-US Authors who publish with this journal agree to the following terms:<br /><ol type="a"><li>Authors retain copyright.</li><li>The texts published in this journal are – unless indicated otherwise – covered by the Creative Commons Spain <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">Attribution-NonComercial-NoDerivatives 4.0</a> licence. You may copy, distribute, transmit and adapt the work, provided you attribute it (authorship, journal name, publisher) in the manner specified by the author(s) or licensor(s). The full text of the licence can be consulted here: <a href="http://creativecommons.org/licenses/by-nc-nd/4.0">http://creativecommons.org/licenses/by-nc-nd/4.0</a>.</li><li>Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.</li><li>Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See <a href="http://opcit.eprints.org/oacitation-biblio.html" target="_new">The Effect of Open Access</a>).</li></ol> elcvia@cvc.uab.cat (Electronic Letters on Computer Vision and Image Analysis) elcvia@cvc.uab.cat (ELCVIA) Tue, 13 Sep 2022 07:29:30 +0200 OJS http://blogs.law.harvard.edu/tech/rss 60 An efficient hybrid approach for medical images enhancement https://elcvia.cvc.uab.cat/article/view/1574 <p>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.</p> Sushil Kumar Saroj Copyright (c) 2022 Sushil Kumar Saroj https://creativecommons.org/licenses/by-nc-nd/4.0 https://elcvia.cvc.uab.cat/article/view/1574 Tue, 11 Oct 2022 00:00:00 +0200 Cricket Video Highlight Generation Methods: A Review https://elcvia.cvc.uab.cat/article/view/1465 <p>The key events extraction from a video for the best<br>representation of its contents is known as video summarization.<br>In this study, the game of cricket is specifically considered<br>for extracting important events such as boundaries, sixes and<br>wickets. The cricket video highlight generation frameworks<br>require extensive key event identification. These key events can<br>be identified by extracting the audio, visual and textual features<br>from any cricket video.The prediction accuracy of the cricket<br>video summarization mainly depends on the game rules, player’s<br>form, their skill, and different natural conditions. This paper<br>provides a complete survey of latest research in cricket video<br>summarization methods. It includes the quantitative evaluation<br>of the outcomes of the existing frameworks. This extensive review<br>highly recommended developing deep learning-assisted video<br>summarization approaches for cricket video due to their more<br>representative feature extraction and classification capability<br>than the conventional edge, texture features, and classifiers. The<br>scope of this analysis also includes future visions and research<br>opportunities in cricket highlight generation.</p> Hansa Shingrakhia, Dr.Hetal Patel Copyright (c) 2022 Hansa Shingrakhia, Dr.Hetal Patel https://creativecommons.org/licenses/by-nc-nd/4.0 https://elcvia.cvc.uab.cat/article/view/1465 Tue, 13 Sep 2022 00:00:00 +0200 Diabetic foot ulcer segmentation using logistic regression, DBSCAN clustering and mathematical morphology operators https://elcvia.cvc.uab.cat/article/view/1413 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. Armando Heras-Tang, Damian Valdes-Santiago, Ángela Mireya León-Mecías, Marta Lourdes Baguer Díaz-Romañach, José Alejandro Mesejo-Chiong, Carlos Cabal-Mirabal Copyright (c) 2022 Armando Heras-Tang, Damian Valdes-Santiago, Ángela Mireya León-Mecías, Marta Lourdes Baguer Díaz-Romañach, José Alejandro Mesejo-Chiong, Carlos Cabal-Mirabal https://creativecommons.org/licenses/by-nc-nd/4.0 https://elcvia.cvc.uab.cat/article/view/1413 Tue, 13 Sep 2022 00:00:00 +0200 Robust Pedestrian Detection and Path Prediction using Improved YOLOv5 https://elcvia.cvc.uab.cat/article/view/1538 <pre style="-qt-block-indent: 0; text-indent: 0px; margin: 0px;"><span style="color: #000000;">In vision-based surveillance systems, pedestrian recognition and path prediction are critical concerns. Advanced computer vision applications, on the other hand, confront numerous challenges<br>due to differences in pedestrian postures and scales, backdrops, and occlusion. To tackle these challenges, we present a </span><span style="text-decoration: underline; color: #000000;">YOLOv5</span><span style="color: #000000;">-based deep learning-based pedestrian recognition and path prediction method. <br>The updated </span><span style="text-decoration: underline; color: #000000;">YOLOv5</span><span style="color: #000000;"> 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. <br>The suggested method deals with partial occlusion circumstances to reduce object occlusion-induced progression and loss, and links recognition results with motion attributes. <br>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, <br>according to the results of the experiments. Finally, we come to a conclusion and look into future study.</span></pre> Kamal Omprakash Hajari, Dr. Ujwalla Gawande, Prof. Yogesh Golhar Copyright (c) 2022 Kamal Omprakash Hajari, Dr. Ujwalla Gawande, Prof. Yogesh Golhar https://creativecommons.org/licenses/by-nc-nd/4.0 https://elcvia.cvc.uab.cat/article/view/1538 Tue, 13 Sep 2022 00:00:00 +0200 Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Machine Learning Approach https://elcvia.cvc.uab.cat/article/view/1604 <p><span class="fontstyle0">Beach hazards would be any occurrences potentially endanger individuals as<br>well as their activity. Rip current, or reverse current of the sea, is a type<br>of wave that pushes against the shore and moves in the opposite direction,<br>that is, towards the deep sea. The management of access to the beach sometimes accidentally push unwary beachgoers forward into rip-prone regions,<br>increasing the probability of a drowning on that beach. The research suggests<br>an approach for something like the automatic detection of rip currents with<br>waves crashing based on convolutional neural networks (CNN) and machine<br>learning algorithms (MLAs) for classification. Several individuals are unable<br>to identify rip currents in order to prevent them. In addition, the absence<br>of evidence to aid in training and validating hazardous systems hinders attempts to predict rip currents. Security cameras and mobile phones have still<br>images of something like the shore pervasive and represent a possible cause<br>of rip current measurements and management to handle this hazards accordingly. This work deals with developing detection systems from still beach<br>images, bathymetric images, and beach parameters using CNN and MLAs.<br>The detection model based on CNN for the input features of beach images<br>and bathymetric images has been implemented. MLAs have been applied to<br>detect rip currents based on beach parameters. When compared to other detection models, bathymetric image-based detection models have significantly<br>higher accuracy and precision. The VGG16 model of CNN shows maximum<br>accuracy of 91.13% (Recall = 0.94, F1-score = 0.87) for beach images. For<br>the bathymetric images, the highest performance has been found with an<br>accuracy of 96.89% (Recall= 0.97, F1-score=0.92) for the DenseNet model</span> <span class="fontstyle0">of CNN. The MLA-based model shows an accuracy of 86.98% (Recall=0.89,<br>F1-score= 0.90) for random forest classifier. Once we know about the potential zone of rip current continuosly generating rip current, then the coastal<br>region can be managed accordingly to prevent the accidents occured due to<br>this coastal hazards.</span> <br style="font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;"><br style="font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: -webkit-auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px;"></p> Md. Ariful Islam, Mosa. Tania Alim Shampa Copyright (c) 2023 Md. Ariful Islam, Mosa. Tania Alim Shampa https://creativecommons.org/licenses/by-nc-nd/4.0 https://elcvia.cvc.uab.cat/article/view/1604 Tue, 10 Jan 2023 00:00:00 +0100