Electronic Letters on Computer Vision and Image Analysis最新文献

{"title":"Robust Object Tracking in Infrared Video via Particle Filters","authors":"E. Comas, Adrián Stacul, C. Delrieux","doi":"10.5565/REV/ELCVIA.1185","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.1185","url":null,"abstract":"In this paper we investigate the effectiveness of particle filters for object tracking in infrared videos. Once the user identifies the target object to be followed in position and size, its most representative feature points are obtained by means of the SURF algorithm. A particle filter is initialized with these feature points, and the location of the object within the video frames is determined by the average value of the particles that have a greater similarity with the target. Two different field tests were carried out to study the filter behaviour in comparison with previously used methods in the bibliography. The first one was tracking an unmanned aerial vehicle (UAV) in the open. The second one was to identify a heliport in a noisy infrared zenithal video take. In the first test, the UAV was followed by another positioning system simultaneously, thus allowing the comparison of both systems, and the evaluation in the improvement introduced by the particle algorithm.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46898538","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":"Transition region based approach for skin lesion segmentation","authors":"Priyadarsan Parida, Ranjita Rout","doi":"10.5565/rev/elcvia.1177","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1177","url":null,"abstract":"Skin melanoma is a skin disease that affects nearly 40% of people globally. Manual detection of the area is a time-consuming process and requires expert knowledge. The application of computer vision techniques can simplify this. In this article, a novel unsupervised transition region based approach for skin lesion segmentation for melanoma detection is proposed. The method starts with Gaussian blurring of the green channel dermoscopic image. Further, the transition region is extracted using local variance features and a global thresholding operation. It achieves the region of interest (binary mask) using various morphological operations. Finally, the melanoma regions are segregated from normal skin regions using the binary mask. The proposed method is tested using DermQuest dataset along with ISIC 2017 dataset and it achieves better results as compared to other state of art methods in effectively segmenting the melanoma regions from the normal skin regions .","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49111557","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 computer vision system for marbling meat segmentation","authors":"G. F. C. Campos, J. L. Seixas, A. P. A. Barbon, A. S. Felinto, A. Bridi, Sylvio Barbon Junior","doi":"10.5565/rev/elcvia.777","DOIUrl":"https://doi.org/10.5565/rev/elcvia.777","url":null,"abstract":"In this study, we developed a robust automatic computer vision system for marbling meat segmentation. Our approach can segment muscle fat in various marbled meat samples using images acquired with different quality devices in an uncontrolled environment, where there was external ambient light and artificial light; thus, professionals can apply this method without specialized knowledge in terms of sample treatments or equipment, as well as without disruption to normal procedures, thereby obtaining a robust solution. The proposed approach for marbling segmentation is based on data clustering and dynamic thresholding. Experiments were performed using two datasets that comprised 82 images of 41 longissimus dorsi muscles acquired by different sampling devices. The experimental results showed that the computer vision system performed well with over 98% accuracy and a low number of false positives, regardless of the acquisition device employed.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"1 1","pages":"15-27"},"PeriodicalIF":0.0,"publicationDate":"2020-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88814549","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":"Single sensor multi-spectral imaging","authors":"Xavier Soria Poma","doi":"10.5565/rev/elcvia.1194","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1194","url":null,"abstract":"This dissertation presents the benefits of using a multispectral Single Sensor Camera (SSC) that, simultaneously acquire images in the visible and near-infrared (NIR) bands. The principal benefits while addressing problems related to image bands in the spectral range of 400 to 1100 nanometers, there are cost reductions in the hardware setup because only one SSC is needed instead of two; moreover, the cameras’ calibration and images alignment are not required anymore. Concerning to the NIR spectrum, even though this band is close to the visible band and shares many properties, the sensor sensitivity is material dependent due to different behavior of absorption/reflectance capturing a given scene compared to visible channels. Many works in literature have proven the benefits of working with NIR to enhance RGB images (e.g., image enhancement, dehazing, etc.). In spite of the advantage of using SSC (e.g., low latency), there are some drawbacks to be solved. One of these drawbacks corresponds to the nature of the silicon-based sensor, which in addition to capturing the RGB image when the infrared cut off filter is not installed it also acquires NIR information into the visible image. This phenomenon is called RGB and NIR crosstalking. This thesis firstly faces this problem in challenging images and then it shows the benefit of using multispectral images in the edge detection task. Then, three methods based on CNN have been proposed for edge detection. While the first one is based on the most used model, holistically-nested edge detection (HED) termed as multispectral HED (MS-HED), the other two have been proposed observing the drawbacks of MS-HED. These two novel architectures have been designed from scratch; after the first architecture is validated in the visible domain a slight redesign is proposed to tackle the multispectral domain. A dataset is collected to face this problem with SSCs. Even though edge detection is confronted in the multispectral domain, its qualitative and quantitative evaluation demonstrates the generalization in other datasets used for edge detection, improving state-of-the-art results. One of the main properties of this proposal is to show that the edge detection problem can be tackled by just training the proposed architecture one-time while validating it in other datasets.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42626427","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":"Fully Convolutional Networks for Text Understanding in Scene Images","authors":"Dena Bazazian","doi":"10.5565/rev/elcvia.1187","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1187","url":null,"abstract":"Text understanding in scene images has gained plenty of attention in the computer vision community and it is an important task in many applications as text carries semantically  rich  information  about  scene  content  and  context.   For  instance, reading text in a scene can be applied to autonomous driving, scene understanding or assisting visually impaired people. The general aim of scene text understanding is to localize and recognize text in scene images. Text regions are first localized in the original image by a trained detector model and afterwards fed into a recognition module. The tasks of localization and recognition are highly correlated since an inaccurate localization can affect the recognition task. The main purpose of this thesis is to devise efficient methods for scene text understanding. We investigate how the latest results on deep learning can advance text understanding pipelines. Recently, Fully Convolutional Networks (FCNs) and derived methods have achieved a significant performance on semantic segmentation and pixel level classification tasks. Therefore, we took benefit of the strengths of FCN approaches in order to detect and recognize text in natural scenes images.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"80 1","pages":"6-10"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79273568","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":"Automatic reactivity characterisation of char particles from pulverised coal combustion using computer vision","authors":"D. Chaves","doi":"10.5565/rev/elcvia.1191","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1191","url":null,"abstract":"Char morphologies produced during pulverised coal combustion may determine coal reactivity which affects the combustion efficiency and the emissions of CO2 in power plants. Commonly, char samples are characterised manually, but this process is subjective and time-consuming. This work proposes methods to automate the char reactivity characterisation using microscopy images and computer vision techniques. These methods are summarised in three contributions: the localisation of char particles based on candidate regions and deep learning methods; the classification of particles into char reactivity groups using morphological and texture features; and the integration in a system of the two previous proposals to characterise char sample reactivity. The proposed system successfully estimate char reactivity in a fast and accurate way.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"5 1","pages":"16-17"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90186904","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 Development of a Computer Vision Algorithm and Tool for Currency Recognition in Indian Vernacular Languages for Visually Challenged People","authors":"V. Raval","doi":"10.5565/rev/elcvia.1186","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1186","url":null,"abstract":"The God created this universe with all living and non-living entities. Human is one of the best among His creations. For human beings, eyes are the best gift of the God to see all His creations. As of now, human beings are considered as the only developed creatures among the God’s creations and have developed themselves from Stone Age to the Super Computing Era. As the human civilizations grew up, the day-to-day transactions moved from the barter system to the currency, the banknotes. Today, every country has its currency in terms of coins and paper notes. Each of the currency of individual country has its unique features, colors, denominations and international value. The life moves on this currency only. We, all, having been given two beautiful eyes could recognize the currency easily, but the same is not easy for the blind people. Though the denomination of a currency can easily be recognized to differentiate between counterfeit currencies from the real one is a Holy Grail. Especially for the blind people, it is a herculean task like finding a needle from a haystack. Since money is the cause of any cheating, if the person is blind, the chances of him being cheated are more. There are many tools available all over the world for the currencies of other developed countries. But, in India, there are no specific robust and handy tools that can help the blind people to recognize the Indian currencies in their mother tongue. For that reason, the main motive of this work is to develop and test a robust computer vision algorithm(s) to identify the Indian currency, mainly paper-based currency, in Indian regional languages. To go ahead with this research, along with the other image matching techniques, the ORB (Oriented FAST Rotated BRIEF) has been used as a feature detector. The reason behind the use of the ORB is the trade-off in the performance of the ORB. In its category, the ORB has been proved less accurate than its siblings the SIFT (Scale-Invariant Feature Transform) and the SURF (Speeded-Up Robust Features) in terms of feature detection and hence accuracy. However, the ORB is faster in terms of execution time than the others. As the SIFT and the SURF are patented technologies and ORB is the free and open source, this work attempts to improve the performance of the ORB in terms of recognition accuracy. In this direction, first, for preprocessing, the time performance of GrabCut algorithm has been improved (An algorithm which is used to remove the background from the images) for Android-based devices, named as cGrab-Cut. The output of this algorithm can be used for further processing of the image. For feature detection, two hybrid approaches have been developed to improve the performance of the ORB, named as HORB – A Histogram based ORB and ACORB – An ACO based ORB. In order to provide the best performance for image classification, this work lastly proposed, developed and tested two classifiers: a three-stage hybrid classifier, the HORBoVF which i","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"10 1","pages":"4-5"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82830211","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":"Development of transition region based methods for image segmentation","authors":"Priyadarsan Parida","doi":"10.5565/rev/elcvia.1176","DOIUrl":"https://doi.org/10.5565/rev/elcvia.1176","url":null,"abstract":"In this thesis, some transition region based segmentation approaches have developed to perform image segmentation for grayscale and colour images. In computer vision and image understanding applications, image segmentation is an important pre-processing step. The main goal of the segmentation process is the separation of foreground region from background region. Based on the output of the segmentation result, segmentation can be categorized as global segmentation or local segmentation. The global segmentation aims for complete separation of the object from the background while the local segmentation divides the image into constituent regions. For achieving segmentation, a number of algorithms are developed by various researchers. The segmentation approaches are application specific and do not work well for both grayscale and colour image segmentation. For any image consisting of foreground and background, some transition regions exist between the foreground and background regions. Effective extraction of transition region leads to a better segmentation result. Therefore, the doctoral thesis intends to efficient and effective transition region approaches for image segmentation for both grayscale and colour images. The performance of the segmentation is qualitatively measured visually by looking at the ground truths as well as the segmentation masks generated from different segmentation approaches or by comparing the original image and the segmented result. The quantitative performance of segmentation results is compared via five performance measures as misclassification error (ME), false positive rate (FPR), false negative rate (FNR), Jaccard index (JI) and segmentation accuracy (SA). The doctoral research work is focused on the development of transition region approaches both in spatial and wavelet domain for image segmentation. The algorithms developed are categorized as (A) Grayscale transition region based segmentation approaches (B) Colour image transition region based segmentation approaches The details are summarized below. (A) Grayscale transition region based segmentation approaches: Five transition region based approaches are developed for grayscale image segmentation (i) Proposed method 1, (ii) Proposed method 2, (iii) Proposed method 3, (iv) Proposed method 4 and (iv) Proposed method 5. (i) Proposed method 1: The Proposed method 1 extracts transition region using local variance and global thresholding considered from the variance features. The method utilizes edge linking and morphological operations for object extraction. The method is intended for segmentation of single and multiple objects from the image. The method does not perform well when the object and background gray level intensities are overlapping in nature. (ii) Proposed method 2: The Proposed method 2 utilizes 2-dimensional Gabor filter and global thresholding considered from the Gabor features for transition region extraction. Further, it uses edge linking and morphologi","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":"33 1","pages":"1-3"},"PeriodicalIF":0.0,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72652307","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":"Automated Leaf Alignment and Partial Shape Feature Extraction for Plant Leaf Classification","authors":"L. Hamid, S. Al-Haddad","doi":"10.5565/REV/ELCVIA.1143","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.1143","url":null,"abstract":"The last few decades have witnessed various approaches to automate the process of plant classification using the characteristics of the leaf. Several approaches have been proposed, and the majority focused on global shape features. However, one challenge that faces this task is the high interclass similarity amongst the leaves of different species in terms of the global shape. Furthermore, there always has been an obstacle against full automation as several approaches require user intervention to align the leaf. Therefore, a new set of Quartile Features (QF) is proposed in this paper to describe the partial shape of the leaf, in addition to an automated alignment approach to automate the system. The QF are extracted from the horizontal and vertical leaf quartiles to describe the partial shape of the leaf and the relations among its parts. The well-known Flavia dataset has been selected for the evaluation of the proposed system. The experimental results indicate the ability of the proposed alignment algorithm to align leaves with different shapes and maintain a correct classification accuracy regardless of the orientation of the input leaf samples. Furthermore, the proposed QF indicated promising results by increasing the accuracy of the classification by a range of approximately 26% to 30% when combined with Hu’s Moment Invariants, using k-fold cross-validation technique.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.5565/REV/ELCVIA.1143","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48620770","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":"Selection of Wavelet Basis Function for Image Compression – A Review","authors":"J. Sunkara","doi":"10.5565/REV/ELCVIA.1094","DOIUrl":"https://doi.org/10.5565/REV/ELCVIA.1094","url":null,"abstract":"Wavelets are being suggested as a platform for various tasks in image processing. The advantage of wavelets lie in its time frequency resolution. The use of different basis functions in the form of different wavelets made the wavelet analysis as a destination for many applications. The performance of a particular technique depends on the wavelet coefficients arrived after applying the wavelet transform. The coefficients for a specific input signal depends on the basis functions used in the wavelet transform. Hence in this paper toward this end, different basis functions and their features are presented. As the image compression task depends on wavelet transform to large extent from few decades, the selection of basis function for image compression should be taken with care. In this paper, the factors influencing the performance of image compression are presented.","PeriodicalId":38711,"journal":{"name":"Electronic Letters on Computer Vision and Image Analysis","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49506222","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}