2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials最新文献

High Level Computer Vision Using OpenCV 使用OpenCV的高级计算机视觉

2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials Pub Date : 2011-08-28 DOI: 10.1109/SIBGRAPI-T.2011.11

M. Marengoni, Denise Stringhini

引用次数: 49

Discriminant Component Analysis and Self-Organized Manifold Mapping for Exploring and Understanding Image Face Spaces 判别成分分析和自组织流形映射用于探索和理解图像面空间

2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials Pub Date : 2011-08-28 DOI: 10.1109/SIBGRAPI-T.2011.10

G. Giraldi, Edson C. Kitani, E. Del-Moral-Hernandez, C. Thomaz

{"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}

引用次数: 1

A gentle introduction to coded computational photography 对编码计算摄影的简单介绍

2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials Pub Date : 2011-08-28 DOI: 10.1109/SIBGRAPI-T.2011.13

Horacio E. Fortunato, M. M. O. Neto

{"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}

引用次数: 5

Adessowiki: Collaborative Scientific Programming Environment Adessowiki:协作科学编程环境

2011 24th SIBGRAPI Conference on Graphics, Patterns, and Images Tutorials Pub Date : 2011-08-28 DOI: 10.1109/SIBGRAPI-T.2011.12

L. Rittner, A. Saúde, Alexandre G. Silva, R. C. Machado, M. Bento, R. Lotufo

引用次数: 2