M. Pâques, F. Rossant, N. Lermé, Chahira Miloudi, C. Kulcsár, J. Sahel, Kevin Loquin, L. Mugnier, I. Bloch, E. Koch
{"title":"Adaptive optics imaging of retinal microstructures: Image processing for medical applications","authors":"M. Pâques, F. Rossant, N. Lermé, Chahira Miloudi, C. Kulcsár, J. Sahel, Kevin Loquin, L. Mugnier, I. Bloch, E. Koch","doi":"10.1109/IWCIM.2014.7008813","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008813","url":null,"abstract":"Adaptive optics (AO) fundus imaging is an optoelectronic technique allowing an improvement of an order of magnitude of lateral resolution of retinal images. Currently, its main applications in ophthalmology span from photoreceptor to retinal pigment epithelial cells and vessels, each of them being affected by specific diseases. Technological and image processing improvements are expanding the scope of its medical applications. Here we will review some of the current and envisioned applications of AO in clinical practice.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127991117","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}
Zbigniew Zdziarski, C. Bourges, Joseph Mitchell, Pierre Houdyer, Dave Johnson, Rozenn Dahyot
{"title":"On summarising the ‘here and now’ of social videos for smart mobile browsing","authors":"Zbigniew Zdziarski, C. Bourges, Joseph Mitchell, Pierre Houdyer, Dave Johnson, Rozenn Dahyot","doi":"10.1109/IWCIM.2014.7008797","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008797","url":null,"abstract":"The amount of media that is being uploaded to social sites (such as Twitter, Facebook and Instagram) is providing a wealth of visual data (images and videos) augmented with additional information such as keywords, timestamps and GPS coordinates. Tapastreet1 provides access in real-time to this visual content by harvesting social networks for visual media associated with particular locations, time and hashtags [1]. Browsing efficiently through harvested videos requires smart processing to give users a quick overview of their content in particular when using mobile platforms with limited bandwidth. This paper aims at presenting an architecture for testing several strategies for processing summaries of videos collected on social networks to tackle this issue.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"156 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134191477","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":"Texture fidelity benchmark","authors":"M. Haindl, M. Kudelka","doi":"10.1109/IWCIM.2014.7008812","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008812","url":null,"abstract":"Automatic texture quality evaluation is important but still unsolved difficult problem. While several generative mathematical texture models were developed, their reliable qualitative evaluation is for now possible only using impractical and expensive visual psycho-physics which hampers their further progress. We present the texture fidelity benchmark created to help the validation of texture fidelity criteria being developed. The benchmark is a web based service (http://tfa.utia.cas.cz) designed for performance evaluation, mutual comparison, and ranking of various texture fidelity measures. The benchmark supports rapid verification and development of new fidelity criteria approaches and contains seven color, variable texture quality, series together with their grey-scale counterparts.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129265995","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}
Dimitri J. Walger, T. Breckon, A. Gaszczak, T. Popham
{"title":"A comparison of features for regression-based driver head pose estimation under varying illumination conditions","authors":"Dimitri J. Walger, T. Breckon, A. Gaszczak, T. Popham","doi":"10.1109/IWCIM.2014.7008805","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008805","url":null,"abstract":"Head pose estimation provides key information about driver activity and awareness. Prior comparative studies are limited to temporally consistent illumination conditions under the assumption of brightness constancy. By contrast the illumination conditions inside a moving vehicle vary considerably with environmental conditions. In this study we present a base comparison of three features for head pose estimation, via support vector machine regression, based on Histogram of Oriented Gradient (HOG) features, Gabor filter responses and Active Shape Model (ASM) landmark features. These, reputedly illumination invariant, are presented through a common face localization framework from which we estimate driver head pose in two degrees-of-freedom and compare against a baseline approach for recovering head pose via weak perspective geometry. Evaluation is performed over a number of invehicle sequences, exhibiting uncontrolled illumination variation, in addition to ground truth data-sets, with controlled illumination changes, upon which we achieve a minimal ~12° and ~15° mean error in pitch and yaw respectively via ASM landmark features.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"15 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123537194","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}
Cem Emre Akbas, Alican Bozkurt, M. T. Arslan, Huseyin Aslanoglu, A. Çetin
{"title":"L1 norm based multiplication-free cosine similarity measures for big data analysis","authors":"Cem Emre Akbas, Alican Bozkurt, M. T. Arslan, Huseyin Aslanoglu, A. Çetin","doi":"10.1109/IWCIM.2014.7008798","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008798","url":null,"abstract":"The cosine similarity measure is widely used in big data analysis to compare vectors. In this article a new set of vector similarity measures are proposed. New vector similarity measures are based on a multiplication-free operator which requires only additions and sign operations. A vector `product' using the multiplication-free operator is also defined. The new vector product induces the ℓ1-norm. As a result, new cosine measure-like similarity measures are normalized by the ℓ1-norms of the vectors. They can be computed using the MapReduce framework. Simulation examples are presented.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115387735","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}
A. Stoian, Marin Ferecatu, J. Benois-Pineau, M. Crucianu
{"title":"Fast cascaded action localization in video using frame alignment","authors":"A. Stoian, Marin Ferecatu, J. Benois-Pineau, M. Crucianu","doi":"10.1109/IWCIM.2014.7008792","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008792","url":null,"abstract":"Locating human actions in videos is challenging because of the complexity and variability of human motions, as well as of the amount of video data to be searched. We propose a method that detects and locates a set of actions in a video database by taking into account their temporal structure at the frame level. While other methods aggregate frames into action parts, we leverage the complementarity between aggregation and frame level comparison of sequences. Combining these two techniques in a cascade, we aim to address large scale retrieval. Evaluation on popular datasets show state of the art results, as well as efficient detection and low storage requirements.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121219379","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":"LHI-tree: An efficient disk-based image search application","authors":"L. Havasi, D. Varga, T. Szirányi","doi":"10.1109/IWCIM.2014.7008794","DOIUrl":"https://doi.org/10.1109/IWCIM.2014.7008794","url":null,"abstract":"Querying of nearest neighbour (NN) elements on large data collections is an important task for several information or content retrieval tasks. In the paper Local Hash-indexing tree (LHI-tree) is introduced, which is a disk-based index scheme that uses RAM for quick space partition localization and hard disks for the hash indexing. When large collections are considered, such hybrid data structure should be used to implement an effective indexing service. The proposed structure can produce list of approximate nearest neighbours. We compare LHI-tree to FLANN (Fast Library for Approximate Nearest Neighbors), an effective and frequently instanced implementation of ANN search. We show that they produce similar lists of retrieved images (although FLANN works only on RAM). In case of huge multimedia database a disk based indexing and retrieval method has a significant advantage against a vector based system running in RAM data. For the visual content indexing we built an image descriptor composed of four different information representations: edge histogram, entropy histogram, pattern histogram and dominant component colour characteristics. The paper will mention the content based retrieval of Hungarian Wikipedia images.","PeriodicalId":337627,"journal":{"name":"2014 International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129205162","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}