Electronic Letters on Computer Vision and Image Analysis最新文献

{"title":"An ant colony based model to optimize parameters in industrial vision","authors":"L. Benchikhi, Mohamed Sadgal, Aziz Elfazziki, Fatimaezzahra Mansouri","doi":"10.5565/REV/ELCVIA.957","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.957","url":null,"abstract":"Industrial vision constitutes an efficient way to resolve quality control problems. It proposes a wide variety of relevant operators to accomplish controlling tasks in vision systems. However, the installation of these systems awaits for a precise parameter tuning, which remains a very difficult exercise. The manual parameter adjustment can take a lot of time, if precision is expected, by revising many operators. In order to save time and get more precision, a solution is to automate this task by using optimization approaches (mathematical models, population models, learning models...). This paper proposes an Ant Colony Optimization (ACO) based model. The process considers each ant as a potential solution, and then by an interacting mechanism, ants converge to the optimal solution. The proposed model is illustrated by some image processing applications giving very promising results. Compared to other approaches, the proposed one is very hopeful.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"50 1","pages":"33-53"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85748776","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 Real-Time Gradient-based Eye Detection and Tracking Using Transform Domain and PSO-Based Feature Selection","authors":"N. Salehi, Maryam Keyvanara, A. Monadjemi","doi":"10.5565/REV/ELCVIA.811","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.811","url":null,"abstract":"Despite numerous research on eye detection and tracking, this field of study remains challenging due to the individuality of eyes, occlusion, and variability in scale, location, and light conditions. This paper combines a techniques of feature extraction and a feature selection method to achieve a significant increase in eye recognition. Subspace methods may improve detection efficiency and accuracy of eye centers detection using dimensionality reduction. In this study, HoG descriptor is used to lay the ground for BPSO based feature selection. Histogram of Oriented Gradient (HoG) features are used for efficient extraction of pose, translation and illumination invariant features. HoG descriptors uses the fact that local object appearance and shape within an image can be described by the distribution of intensity gradients or edge directions. The method upholds invariance to geometric and photometric transformations. The performance of presented method is evaluated using several benchmark datasets, namely, BioID and RS-DMV. Experimental results obtained by applying the proposed algorithm on BioID dataset show that the proposed system outperforms other eye recognition systems. A significant increase in the recognition rate is achieved when using the combination of HoG descriptor, BPSO, and SVM for feature extraction, feature selection and training phase respectively. The Recognition rate for BioID dataset was 99.6% and the detection time was 15.24 msec for every single frame.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"10 1","pages":"15-32"},"PeriodicalIF":0.0,"publicationDate":"2017-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74467529","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":"Detection of retinal blood vessels from ophthalmoscope images using morphological approach","authors":"Jyotiprava Dash, N. Bhoi","doi":"10.5565/REV/ELCVIA.913","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.913","url":null,"abstract":"Accurate segmentation of retinal blood vessels is an essential task for diagnosis of various pathological disorders. In this paper, a novel method has been introduced for segmenting retinal blood vessels which involves pre-processing, segmentation and post-processing. The pre-processing stage enhanced the image using contrast limited adaptive histogram equalization and 2D Gabor wavelet. The enhanced image is segmented using geodesic operators and a final segmentation output is obtained by applying a post-processing stage that involves hole filling and removal of isolated pixels. The performance of the proposed method is evaluated on the publicly available Digital retinal images for vessel extraction (DRIVE) and High-resolution fundus (HRF) databases using five different measurements and experimental analysis shows that the proposed method reach an average accuracy of 0.9541 on DRIVE database and 0.9568, 0.9478 and 0.9613 on HRF database with healthy, diabetic retinopathy (DR) and glaucomatous images respectively.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"137 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2017-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73495097","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":"Design and Stability Analysis of Multi-Objective Ensemble Classifiers","authors":"Z. Pourtaheri, S. Zahiri, S. M. Razavi","doi":"10.5565/REV/ELCVIA.929","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.929","url":null,"abstract":"Some important topics, which affects directly on the performance of the designed ensemble classifier, inflict a complex search space with high dimensions on the researcher. So, heuristic algorithms can be applied to find best solutions because of their capability of efficient search in the solution space. Due to the stochastic nature of heuristic algorithms, it's necessary to perform stability analysis of heuristic ensemble classifiers. In this paper, Multi-Objective Inclined Planes Optimization (MOIPO) algorithm, as a novel multi-objective technique, is used to design ensemble classifiers and the performance of created ensemble is compared with ensemble designed by Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Experimental results confirm the supremacy of MOIPO for designing ensemble classifiers. So, in the next step, for the first time, the stability of this ensemble classifier is analyzed by using statistical method and suitable model for stability analysis is specified.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"229 1","pages":"32-47"},"PeriodicalIF":0.0,"publicationDate":"2017-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80220270","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 block-based background model for moving object detection","authors":"O. Elharrouss, A. Abbad, Driss Moujahid, J. Riffi, H. Tairi","doi":"10.5565/REV/ELCVIA.855","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.855","url":null,"abstract":"Detecting the moving objects in a video sequence using a stationary camera is an important task for many computer vision applications. This paper proposes a background subtraction approach. As first step, the background is initialized using the block-based analysis before being updated in each incoming frame. Our background frame is generated by collecting the blocks background candidates. The block candidate selection is based on probability density function (pdf) computation. After that, the absolute difference between the background frame and each frame of sequence is computed. A noise filter is applied using the Structure/Texture decomposition in order to minimize the noise caused by background subtraction operation. The binary motion mask is formed using an adaptive threshold that was deduced from the weighted mean and variance calculation. To assure the correspondence between the current frame and the background frame, an adaptation of background model in each incoming frame is realized. After comparing results obtained from the proposed method to other existing ones, it was shown that our approach attains a higher degree of efficacy","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"85 1","pages":"17-31"},"PeriodicalIF":0.0,"publicationDate":"2017-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85837166","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 critical appraisal on wavelet based features from brain MR images for efficient characterization of ischemic stroke injuries","authors":"R. Karthik, R. Menaka","doi":"10.5565/REV/ELCVIA.804","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.804","url":null,"abstract":"Ischemic stroke is a severe neuro disorder typically characterized by a block inside a blood vessel supplying blood to the brain. It remains the third leading cause for death, after heart attack and cancer. Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) were the vital major imaging techniques used for diagnosing this disorder. While the CT imaging can be used at the primary stage, MRI proves to be a standard aid for progressive diagnostic planning in the treatment of stroke injuries. Developing a fully automatic approach for lesion segmentation is a challenging issue due to the complex nature of the lesions structures. This research basically aims at examining the properties of such complex structures. It analyses the characteristics of the normal brain tissues and abnormal lesion structures using a three-level wavelet decomposition procedure. Four different wavelet functions namely daubechies, symlet, coiflet and de-meyer were applied to the different datasets and the resulting observations were examined based on their feature statistics obtained. Experiments indicate the feature statistics obtained from daubechies and de-meyer wavelets were able to clearly distinguish between the typical brain tissues and abnormal lesion structures.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"87 1","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2016-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83437250","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":"Hierarchical visual content modelling and query based on trees","authors":"A. Setyanto","doi":"10.5565/REV/ELCVIA.952","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.952","url":null,"abstract":"In recent years, such vast archives of video information have become available that human annotation of content is no longer feasible; automation of video content analysis is therefore highly desirable. The recognition of semantic content in images is a problem that relies on prior knowledge and learnt information and that, to date, has only been partially solved. Salient analysis, on the other hand, is statistically based and highlights regions that are distinct from their surroundings, while also being scalable and repeatable. The arrangement of salient information into hierarchical tree structures in the spatial and temporal domains forms an important step to bridge the semantic salient gap. Salient regions are identified using region analysis, rank ordered and documented in a tree for further analysis. A structure of this kind contains all the information in the original video and forms an intermediary between video processing and video understanding, transforming video analysis to a syntactic database analysis problem. This contribution demonstrates the formulation of spatio-temporal salient trees the syntax to index them, and provides an interface for higher level cognition in machine vision.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"45 1","pages":"40-42"},"PeriodicalIF":0.0,"publicationDate":"2016-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82794861","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":"Memory organization for invariant object recognition and categorization","authors":"Guillermo S. Donatti","doi":"10.5565/REV/ELCVIA.954","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.954","url":null,"abstract":"Using distributed representations of objects enables artificial systems to be more versatile regarding inter- and intra-category variability, improving the appearance-based modeling of visual object understanding. They are built on the hypothesis that object models are structured dynamically using relatively invariant patches of information arranged in visual dictionaries, which can be shared across objects from the same category. However, implementing distributed representations efficiently to support the complexity of invariant object recognition and categorization remains a research problem of outstanding significance for the biological, the psychological, and the computational approach to understanding visual perception. The present work focuses on solutions driven by top-down object knowledge. It is motivated by the idea that, equipped with sensors and processing mechanisms from the neural pathways serving visual perception, biological systems are able to define efficient measures of similarities between properties observed in objects and use these relationships to form natural clusters of object parts that share equivalent ones. Based on the comparison of stimulus-response signatures from these object-to-memory mappings, biological systems are able to identify objects and their kinds. The present work combines biologically inspired mathematical models to develop memory frameworks for artificial systems, where these invariant patches are represented with regular-shaped graphs, whose nodes are labeled with elementary features that capture texture information from object images. It also applies unsupervised clustering techniques to these graph image features to corroborate the existence of natural clusters within their data distribution and determine their composition. The properties of such computational theory include self-organization and intelligent matching of these graph image features based on the similarity and co-occurrence of their captured texture information. The performance to model invariant object recognition and categorization of feature-based artificial systems equipped with each of the developed memory frameworks is validated applying standard methodologies to well-known image libraries found in literature. Additionally, these artificial systems are cross-compared with state-of-the-art alternative solutions. In conclusion, the findings of the present work convey implications for strategies and experimental paradigms to analyze human object memory as well as technical applications for robotics and computer vision.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"68 1","pages":"33-36"},"PeriodicalIF":0.0,"publicationDate":"2016-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80954156","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":"Speech Recognition Supported by Lip Analysis","authors":"W. Butt","doi":"10.5565/REV/ELCVIA.953","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.953","url":null,"abstract":"Computers have become more pervasive than ever with a wide range of devices and multiple ways of interaction. Traditional ways of human computer interaction using keyboards, mice and display monitors are being replaced by more natural modes such as speech, touch, and gesture. The continuous progress of technology brings to an irreversible change of paradigms of interaction between human and machine. They are now used in daily life in many devices that have revolutionized the way users interact with machines. In fact new PCs, tablets and smartphones are moving increasingly toward a direction that will bring in a short time to have interaction paradigms so advanced that will be completely transparent to users. The various modes of human-machine interaction, through voice recognition are without doubt one of the most considered. A number of researchers have revealed that a speech reading system is beneficial complement to an audio speech recognition system by using of visual cues of the speakers, such as face in noisy environment. However, robust and precise extraction of visual features is a challenging problem in object recognition, due to high variation in pose, lighting and facial makeup. Most of the existing approaches use constraints such as the use of reflective marker on subjects lips, lip movements recorded with a fixed camera position (head mounted camera) and lip segmentation in organized illumination conditions. Furthermore, there is no common consensus about the visual features selection and their significance for a particular phoneme. Speech is the natural procedure of communication. Therefore speech would be an apparently preferred option for human computer interaction. In the past years, development in technology, combined with a significant reduction in cost, has led to the pervasive use of automated speech recognition in variety of systems such as telephony, human-computer interaction and robotics. Visual speech cues are prospective source of speech information and they are apparently not affected in noisy acoustic environmental condition and cross talking between speakers. Visual information of a speaker is the key component of Speech Recognition system such as outside area of mouth, mouth gestures and facial expressions. The major problem to develop robust speech recognition system is to find the precise visual feature extraction method. Sometime hearer observes improper from speaker because of the incompatible effect of visual features. These visual features have great role in the lip reading process. These interpretations gave a motivation for developing a computer speech recognition system. Butt et al. / Electronic Letters on Computer Vision and Image Analysis 15(2):30-32, 2016 31 I propose a speech recognition system using face detection, lip extraction and tracking with some preprocessing techniques to overwhelmed the pose/lighting variation problems. The proposed approach is useful for face/lip detection and tracking in seq","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"70 1","pages":"30-32"},"PeriodicalIF":0.0,"publicationDate":"2016-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86226913","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 confidence framework for the assessment of optical flow performance","authors":"Patricia Márquez-Valle","doi":"10.5565/REV/ELCVIA.949","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.949","url":null,"abstract":"Optical Flow (OF) is the input of a wide range of decision support systems such as car driver assistance, UAV guiding or medical diagnose. In these real situations, the absence of ground truth forces to assess OF quality using quantities computed from either sequences or the computed optical flow itself. These quantities are generally known as Confidence Measures, CM. Even if we have a proper confidence measure we still need a way to evaluate its ability to discard pixels with an OF prone to have a large error. Current approaches only provide a descriptive evaluation of the CM performance but such approaches are not capable to fairly compare different confidence measures and optical flow algorithms. Thus, it is of prime importance to define a framework and a general road map for the evaluation of optical flow performance.  This thesis provides a framework able to decide which pairs ”optical flow - confidence measure” (OF-CM) are best suited for optical flow error bounding given a confidence level determined by a decision support system. To design this framework we cover the following points: 1) Descriptive scores. As a first step, we summarize and analyze the sources of inaccuracies in the output of optical flow algorithms. Second, we present several descriptive plots that visually assess CM capabilities for OF error bounding. In addition to the descriptive plots, given a plot representing OF-CM capabilities to bound the error, we provide a numeric score that categorizes the plot according to its decreasing profile, that is, a score assessing CM performance. 2) Statistical framework. We provide a comparison framework that assesses the best suited OF-CM pair for error bounding that uses a two stage cascade process. First of all we assess the predictive value of the confidence measures by means of a descriptive plot. Then, for a sample of descriptive plots computed over training frames, we obtain a generic curve that will be used for sequences with no ground truth. As a second step, we evaluate the obtained general curve and its capabilities to really reflect the predictive value of a confidence measure using the variability across train frames by means of ANOVA. The presented framework has shown its potential in the application on clinical decision support systems. In particular, we have analyzed the impact of the different image artifacts such as noise and decay to the output of optical flow in a cardiac diagnose system and we have improved the navigation inside the bronchial tree on bronchoscopy.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"49 1","pages":"4-6"},"PeriodicalIF":0.0,"publicationDate":"2016-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84896838","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}