Pub Date : 2011-08-28DOI: 10.1109/SIBGRAPI-T.2011.11
M. Marengoni, Denise Stringhini
This paper presents some more advanced topics in image processing and computer vision, such as Principal Components Analysis, Matching Techniques, Machine Learning Techniques, Tracking and Optical Flow and Parallel Computer Vision using CUDA. These concepts will be presented using the open CV library, which is a free computer vision library for C/C++ programmers available for Windows, Linux Mac OS and Android platforms. These topics will be covered considering not only theoretical aspects but practical examples will be presented in order to understand how and when to use each of them.
{"title":"High Level Computer Vision Using OpenCV","authors":"M. Marengoni, Denise Stringhini","doi":"10.1109/SIBGRAPI-T.2011.11","DOIUrl":"https://doi.org/10.1109/SIBGRAPI-T.2011.11","url":null,"abstract":"This paper presents some more advanced topics in image processing and computer vision, such as Principal Components Analysis, Matching Techniques, Machine Learning Techniques, Tracking and Optical Flow and Parallel Computer Vision using CUDA. These concepts will be presented using the open CV library, which is a free computer vision library for C/C++ programmers available for Windows, Linux Mac OS and Android platforms. These topics will be covered considering not only theoretical aspects but practical examples will be presented in order to understand how and when to use each of them.","PeriodicalId":131363,"journal":{"name":"2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials","volume":"63 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123313057","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}
Pub Date : 2011-08-28DOI: 10.1109/SIBGRAPI-T.2011.10
G. Giraldi, Edson C. Kitani, E. Del-Moral-Hernandez, C. Thomaz
Face recognition is a multidisciplinary field that involves subjects in neuroscience, computer science and statistical learning. Some recent research in neuroscience has indicated that the ability of our memory relies on the capability of orthogonalizing (pattern separation) and completing (pattern prototyping) partial patterns in order to encode, store and recall information. From a computational viewpoint, pattern separation can be cast in the subspace learning area while pattern prototyping is closer to manifold learning methods. So, subspace (or manifold) learning techniques have a close biological inspiration and reasonability in terms of computational methods to possibly exploring and understanding the human behavior of recognizing faces. Therefore, the aim of this paper is threefold. Firstly, we review some theoretical aspects about perceptual and cognitive processes related to the mechanisms of pattern separation and pattern prototyping. Then, the paper presents the basic idea of manifold learning and its relationship with subspace learning with focus on the dimensionality reduction problem. Finally, we present the Discriminant Principal Component Analysis (DPCA) and the Self-Organized Manifold Mapping (SOMM) algorithm to exemplify respectively pattern separation and completion techniques. We show experimental results to demonstrate the effectiveness of DPCA and SOMM algorithms on well-framed face image analysis.
{"title":"Discriminant Component Analysis and Self-Organized Manifold Mapping for Exploring and Understanding Image Face Spaces","authors":"G. Giraldi, Edson C. Kitani, E. Del-Moral-Hernandez, C. Thomaz","doi":"10.1109/SIBGRAPI-T.2011.10","DOIUrl":"https://doi.org/10.1109/SIBGRAPI-T.2011.10","url":null,"abstract":"Face recognition is a multidisciplinary field that involves subjects in neuroscience, computer science and statistical learning. Some recent research in neuroscience has indicated that the ability of our memory relies on the capability of orthogonalizing (pattern separation) and completing (pattern prototyping) partial patterns in order to encode, store and recall information. From a computational viewpoint, pattern separation can be cast in the subspace learning area while pattern prototyping is closer to manifold learning methods. So, subspace (or manifold) learning techniques have a close biological inspiration and reasonability in terms of computational methods to possibly exploring and understanding the human behavior of recognizing faces. Therefore, the aim of this paper is threefold. Firstly, we review some theoretical aspects about perceptual and cognitive processes related to the mechanisms of pattern separation and pattern prototyping. Then, the paper presents the basic idea of manifold learning and its relationship with subspace learning with focus on the dimensionality reduction problem. Finally, we present the Discriminant Principal Component Analysis (DPCA) and the Self-Organized Manifold Mapping (SOMM) algorithm to exemplify respectively pattern separation and completion techniques. We show experimental results to demonstrate the effectiveness of DPCA and SOMM algorithms on well-framed face image analysis.","PeriodicalId":131363,"journal":{"name":"2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115962709","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}
Pub Date : 2011-08-28DOI: 10.1109/SIBGRAPI-T.2011.13
Horacio E. Fortunato, M. M. O. Neto
Computational photography tries to expand the concept of traditional photography (a static two dimensional projection of a scene) using state-of-the-art technology. While this can be achieved by combining information from multiple conventional pictures, a more interesting challenge consists in encoding and recovering additional information from one (or more) image(s). Since a photograph results from the convolution of scene radiance with the camera's aperture (integrated over the exposure time), researchers have designed apertures with certain desirable spectral properties to facilitate the deconvolution process and, consequently, the recovery of scene information. Images captured using these so-called coded apertures can be deconvolved to create all-in-focus images, and to estimate scene depth, among other things. Images of moving objects acquired using a coded exposure (obtained by switching between a fully-closed and a fully-opened aperture, according to a predefined pattern) can be deconvolved to reduce motion blur. The notion of encoding information during image acquisition opens up new and exciting possibilities, which researchers have just begun to explore. This article provides a gentle introduction to coded photography, focusing on the fundamental concepts and essential mathematical tools.
{"title":"A gentle introduction to coded computational photography","authors":"Horacio E. Fortunato, M. M. O. Neto","doi":"10.1109/SIBGRAPI-T.2011.13","DOIUrl":"https://doi.org/10.1109/SIBGRAPI-T.2011.13","url":null,"abstract":"Computational photography tries to expand the concept of traditional photography (a static two dimensional projection of a scene) using state-of-the-art technology. While this can be achieved by combining information from multiple conventional pictures, a more interesting challenge consists in encoding and recovering additional information from one (or more) image(s). Since a photograph results from the convolution of scene radiance with the camera's aperture (integrated over the exposure time), researchers have designed apertures with certain desirable spectral properties to facilitate the deconvolution process and, consequently, the recovery of scene information. Images captured using these so-called coded apertures can be deconvolved to create all-in-focus images, and to estimate scene depth, among other things. Images of moving objects acquired using a coded exposure (obtained by switching between a fully-closed and a fully-opened aperture, according to a predefined pattern) can be deconvolved to reduce motion blur. The notion of encoding information during image acquisition opens up new and exciting possibilities, which researchers have just begun to explore. This article provides a gentle introduction to coded photography, focusing on the fundamental concepts and essential mathematical tools.","PeriodicalId":131363,"journal":{"name":"2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130704434","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}
Pub Date : 2011-08-28DOI: 10.1109/SIBGRAPI-T.2011.12
L. Rittner, A. Saúde, Alexandre G. Silva, R. C. Machado, M. Bento, R. Lotufo
Adessowiki is a collaborative environment for teaching and research in image processing. Adessowiki is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the page is rendered, incorporating the results as figures, texts and tables on the document. The integrated collaborative environment of Adessowiki, containing documentation, programming code and execution results is able to create several possibilities of applications. This paper presents some of the applications where Adessowiki has been used, such as Scientific Writing and Virtual Learning Environment.
{"title":"Adessowiki: Collaborative Scientific Programming Environment","authors":"L. Rittner, A. Saúde, Alexandre G. Silva, R. C. Machado, M. Bento, R. Lotufo","doi":"10.1109/SIBGRAPI-T.2011.12","DOIUrl":"https://doi.org/10.1109/SIBGRAPI-T.2011.12","url":null,"abstract":"Adessowiki is a collaborative environment for teaching and research in image processing. Adessowiki is composed of a collection of collaborative web pages in the form of a wiki. The articles of this wiki can embed programming code that will be executed on the server when the page is rendered, incorporating the results as figures, texts and tables on the document. The integrated collaborative environment of Adessowiki, containing documentation, programming code and execution results is able to create several possibilities of applications. This paper presents some of the applications where Adessowiki has been used, such as Scientific Writing and Virtual Learning Environment.","PeriodicalId":131363,"journal":{"name":"2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125632039","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}
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