Electronic Letters on Computer Vision and Image Analysis最新文献

{"title":"Behaviour understanding through the analysis of image sequences collected by wearable cameras","authors":"Estefanía Talavera Martínez","doi":"10.5565/rev/elcvia.1240","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1240","url":null,"abstract":"Describing people's lifestyle has become a hot topic in the field of artificial intelligence. Lifelogging is described as the process of collecting personal activity data describing the daily behaviour of a person. Nowadays, the development of new technologies and the increasing use of wearable sensors allow to automatically record data from our daily living. In this paper, we describe our developed automatic tools for the analysis of collected visual data that describes the daily behaviour of a person. For this analysis, we rely on sequences of images collected by wearable cameras, which are called egocentric photo-streams. These images are a rich source of information about the behaviour of the camera wearer since they show an objective and first-person view of his or her lifestyle.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"134 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77892919","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":"Scale Invariant Mask R-CNN for Pedestrian Detection","authors":"U. Gawande, K. Hajari, Yogesh Golhar","doi":"10.5565/rev/elcvia.1278","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1278","url":null,"abstract":"Pedestrian detection is a challenging and active research area in computer vision. Recognizing pedestrians helps in various utility applications such as event detection in overcrowded areas, gender, and gait classification, etc. In this domain, the most recent research is based on instance segmentation using Mask R-CNN. Most of the pedestrian detection method uses a feature of different body portions for identifying a person. This feature-based approach is not efficient enough to differentiate pedestrians in real-time, where the background changing. In this paper, a combined approach of scale-invariant feature map generation for detecting a small pedestrian and Mask R-CNN has been proposed for multiple pedestrian detection to overcome this drawback. The new database was created by recording the behavior of the student at the prominent places of the engineering institute. This database is comparatively new for pedestrian detection in the academic environment. The proposed Scale-invariant Mask R-CNN has been tested on the newly created database and has been compared with the Caltech [1], INRIA [2], MS COCO [3], ETH [4], and KITTI [5] database. The experimental result shows significant performance improvement in pedestrian detection as compared to the existing approaches of pedestrian detection and instance segmentation. Finally, we conclude and investigate the directions for future research.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"75 1","pages":"98-118"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86172564","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":"Covid19 Identification from Chest X-ray Images using Machine Learning Classifiers with GLCM Features","authors":"Sudeep D. Thepade, S. Bang, P. Chaudhari, M. Dindorkar","doi":"10.5565/rev/elcvia.1277","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1277","url":null,"abstract":"From staying quarantined at home, practicing work from home to moving outside wearing masks and carrying sanitizers, every individual has now become so adaptive to so called ‘New Normal’ post series of lockdowns across the countries. The situation triggered by novel Coronavirus has changed the behaviour of every individual towards every other living as well as non-living entity. In the Wuhan city of China, multiple cases were reported of pneumonia caused due to unknown reasons. The concerned medical authorities confirmed the cause to be Coronavirus. The symptoms seen in these cases were not much different than those seen in case of pneumonia. Earlier the research has been carried out in the field of pneumonia identification and classification through X-ray images of chest. The difficulty in identifying Covid19 infection at initial stage is due to high resemblance of its symptoms with the infection caused due to pneumonia. Hence it is trivial to well distinguish cases of coronavirus from pneumonia that may help in saving life of patients. The paper uses chest X-ray images to identify Covid19 infection in lungs using machine learning classifiers and ensembles with Gray-Level Cooccurrence Matrix (GLCM) features. The advocated methodology extracts statistical texture features from X-ray images by computing a GLCM for each image. The matrix is computed by considering various stride combinations. These GLCM features are used to train the machine learning classifiers and ensembles. The paper explores both the multiclass classification (X-ray images are classified into one of the three classes namely Covid19 affected, Pneumonia affected and normal lungs) and binary classification (Covid19 affected and other). The dataset used for evaluating performance of the method is open sourced and can be accessed easily. Proposed method being simple and computationally effective achieves noteworthy performance in terms of Accuracy, F-Measure, MCC, PPV and Sensitivity. In sum, the best stride combination of GLCM and ensemble of machine learning classifiers is suggested as vital outcome of the proposed method for effective Covid19 identification from chest X-ray images","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"26 1","pages":"85-97"},"PeriodicalIF":0.0,"publicationDate":"2020-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73880129","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":"Vostok: 3D scanner simulation for industrial robot environments","authors":"A. Rossi, Marco Barbiero, R. Carli","doi":"10.5565/rev/elcvia.1244","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1244","url":null,"abstract":"Computer vision will drive the next wave of robot applications. Latest three-dimensional scanners provide increasingly realistic object reconstructions. We introduce an innovative simulator that allows interacting with those scanners within the operating environment, thus creating a powerful tool for developers, researchers and students. In particular, we present a novel approach for simulating structured-light and timeof-flight sensors. Qualitative results demonstrate the efficiency and reliability in industrial environments. By using the programmability of modern GPUs, it is now possible to make greater use of parallelized simulative approaches. Apart from the easy modification of sensor parameters, the main advantage in simulation is the opportunity of carrying out experiments under reproducible conditions, especially for dynamic scene setups. Moreover, thanks to a great computational power, it is possible to generate huge amounts of synthetic data which can be used as test datasets for training machine learning models.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"22 1","pages":"71-84"},"PeriodicalIF":0.0,"publicationDate":"2020-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75042860","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":"Face Analysis Using Row and Correlation Based Local Directional Pattern","authors":"S. Ramalingam","doi":"10.5565/rev/elcvia.1254","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1254","url":null,"abstract":"Face analysis, which includes face recognition and facial expression recognition, has been attempted by many researchers and gave ideal solutions. The problem is still active and challenging due to an increase in the complexity of the problem viz. due to poor lighting, face occlusion, low-resolution images, etc. Local pattern descriptor methods introduced to overcome these critical issues and improve the recognition rate. These methods extract the discriminant information from the local features of the face image for recognition. In this paper, the local descriptor based two methods, namely row-based local directional pattern and correlation-based local directional pattern proposed by extending an existing descriptor -- local directional pattern (LDP). Further, the two feature vectors obtained by these methods concatenated to form a hybrid descriptor. Experimentation has carried out on benchmark databases and results infer that the proposed hybrid descriptor outperforms the other descriptors in face analysis.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"39 5","pages":"55-70"},"PeriodicalIF":0.0,"publicationDate":"2020-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72466622","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":"A Novel Method to Improve the Efficiency of Classification Phase of a Decision Tree","authors":"Naga Muneiah Janapati, D. SubbaRaoCh","doi":"10.5565/rev/elcvia.1267","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1267","url":null,"abstract":"So far, most of the research on classification algorithms in machine learning has been focused only on improving the training speed and further improving the technical performance evaluation measures of the constructed models. There is no focus on improving the runtime efficiency of the classification phase which is much required in some critical applications. In this paper, we are considering the computation complexity of a decision tree’s classification phase as the major criterion. A novel approach has been proposed to predict the class label of an unseen instance using the decision tree in less time than the regular tree traversal method. In the proposed method, the constructed decision tree is represented in the form of arrays. Then, the process of finding the class label is carried out by performing the bitwise operations between the elements of the arrays and test instance. Empirical results on various UCI data sets proved that the proposed method outperforms the standard method and five other benchmark classifiers and its classification is at least four times faster than the regular method.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"41 1","pages":"38-54"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84181140","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":"A review of movie recommendation system: Limitations, Survey and Challenges","authors":"Mahesh M. Goyani, N. Chaurasiya","doi":"10.5565/REV/ELCVIA.1232","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.1232","url":null,"abstract":"Recommendation System is a major area which is very popular and useful for people to take proper decision. It is a method that helps user to find out the information which is beneficial for the user from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user’s activity (Content Based Filtering) which he wants to engage with. So to overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have reviewed different similarity measures. Various companies like face book which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"2008 1","pages":"18-37"},"PeriodicalIF":0.0,"publicationDate":"2020-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82577306","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":"Algorithm for Iris recognition based on contourlet Transform and Entropy","authors":"Ayoub Ezzaki, Nadia Idrissi, Francisco-Angel Moreno, L. Masmoudi","doi":"10.5565/rev/elcvia.1190","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1190","url":null,"abstract":"The iris is one of the most secure biometric information that is widely employed in authentication systems. In this paper we present a method for iris recognition based on the Contourlet Transform and Entropy which entails i) the detection and segmentation of the iris, ii) its normalization, iii) the application of the Contourlet Transform, iv) the generation of the iris descriptor, and v) the matching between the query iris and those in the database. The proposed method has been tested with images taken from the popular CASIA-V4 and UBIRIS.v1 datasets and compared against four other iris recognition algorithms. The results show a higher true positive rate with a reduced computation time.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41853993","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":"Haar Hybrid Transform Based Melanoma Identification Using Ensemble of Machine Learning Algorithms","authors":"Sudeep D. Thepade, Gaurav Ramnani, Shubham Mandhare","doi":"10.5565/rev/elcvia.1236","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1236","url":null,"abstract":"Traditional methods of disease diagnosis can be time-intensive, error prone and invasive to the subject. These methods are also prejudiced by the doctor’s subjectivity. These issues can be resolved by using automated diagnosis methods. There is a considerable dearth of medical experts today, especially in the rural areas. The use of computing technology may help to assist in the diagnostic process. This paper proposes the utilization of computers to detect melanoma skin cancer. Melanoma skin cancer can be fatal, especially in its later stages. However, it shows a high recovery rate when it is detected in its early stages. Considering the lack of medical professionals, early diagnosis of melanoma may be tried using machine learning algorithms. This paper explores hybrid wavelet transform based melanoma identification using ensemble of machine learning algorithms. The hybrid wavelet transform is produced using Discrete Cosine Transform and Haar Wavelet Transform as its components. The sizes of both components are varied from 4x4 to 128x128 to obtain the hybrid wavelet transorm. Experimentation performed on the transformed dermoscopy skin images with machine learning algorithms and their ensembles gives rise to a total of 196 variations. Overall, if the average of the metrics accuracy, sensitivity and specificity is considered, the SVM algorithm using the hybrid transform of Haar 8x8 and DCT 64x64 gives the best performance, followed by the SVM algorithm using hybrid transform of Haar 128x128 and DCT 4x4.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"1 1","pages":"1-17"},"PeriodicalIF":0.0,"publicationDate":"2020-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79353612","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":"Retinal Blood Vessel Extraction from Fundus Images Using Enhancement Filtering and Clustering","authors":"Priyadarsan Parida, Jyotiprava Dash, N. Bhoi","doi":"10.5565/rev/elcvia.1239","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1239","url":null,"abstract":"Screening of vision troubling eye diseases by segmenting fundus images eases the danger of loss of sight of people. Computer assisted analysis can play an important role in the forthcoming health care system universally. Therefore, this paper presents a clustering based method for extraction of retinal vasculature from ophthalmoscope images. The method starts with image enhancement by contrast limited adaptive histogram equalization (CLAHE) from which feature extraction is accomplished using Gabor filter followed by enhancement of extracted features with Hessian based enhancement filters. It then extracts the vessels using K-mean clustering technique. Finally, the method ends with the application of a morphological cleaning operation to get the ultimate vessel segmented image. The performance of the proposed method is evaluated by taking two different publicly available Digital retinal images for vessel extraction (DRIVE) and Child heart and health study in England (CHASE_DB1) databases using nine different performance matrices. It gives average accuracies of 0.952 and 0.951 for DRIVE and CHASE_DB1 databases, respectively.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"66 1","pages":"38-52"},"PeriodicalIF":0.0,"publicationDate":"2020-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80229582","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}