Use of colour for hand-filled form analysis and recognition

Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system

Action Recognition in Videos: from Motion Capture Labs to the Web

This paper presents a survey of human action recognition approaches based on visual data recorded from a single video camera. We propose an organizing framework which puts in evidence the evolution of the area, with techniques moving from heavily constrained motion capture scenarios towards more challenging, realistic, "in the wild" videos. The proposed organization is based on the representation used as input for the recognition task, emphasizing the hypothesis assumed and thus, the constraints imposed on the type of video that each technique is able to address. Expliciting the hypothesis and constraints makes the framework particularly useful to select a method, given an application. Another advantage of the proposed organization is that it allows categorizing newest approaches seamlessly with traditional ones, while providing an insightful perspective of the evolution of the action recognition task up to now. That perspective is the basis for the discussion in the end of the paper, where we also present the main open issues in the area.Comment: Preprint submitted to CVIU, survey paper, 46 pages, 2 figures, 4 table

Identifying person re-occurrences for personal photo management applications

Automatic identification of "who" is present in individual digital images within a photo management system using only content-based analysis is an extremely difficult problem. The authors present a system which enables identification of person reoccurrences within a personal photo management application by combining image content-based analysis tools with context data from image capture. This combined system employs automatic face detection and body-patch matching techniques, which collectively facilitate identifying person re-occurrences within images grouped into events based on context data. The authors introduce a face detection approach combining a histogram-based skin detection model and a modified BDF face detection method to detect multiple frontal faces in colour images. Corresponding body patches are then automatically segmented relative to the size, location and orientation of the detected faces in the image. The authors investigate the suitability of using different colour descriptors, including MPEG-7 colour descriptors, color coherent vectors (CCV) and color correlograms for effective body-patch matching. The system has been successfully integrated into the MediAssist platform, a prototype Web-based system for personal photo management, and runs on over 13000 personal photos

Recuperação por conteudo em grandes coleções de imagens heterogeneas

Orientador: Alexandre Xavier FalcãoTese (doutorado) - Universidade Estadual de Campinas, Instituto de Matematica, Estatistica e Computação CientificaResumo: A recuperação de imagens por conteúdo (CBIR) é uma área que vem recebendo crescente atenção por parte da comunidade científica por causa do crescimento exponencial do número de imagens que vêm sendo disponibilizadas, principalmente na WWW. À medida que cresce o volume de imagens armazenadas, Cresce também o interesse por sistemas capazes de recuperar eficientemente essas imagens a partir do seu conteúdo visual. Nosso trabalho concentrou-se em técnicas que pudessem ser aplicadas em grandes coleções de imagens heterogêneas. Nesse tipo de coleção, não se pode assumir nenhum tipo de conhecimento sobre o conteúdo semântico e ou visual das imagens, e o custo de utilizar técnicas semi-automáticas (com intervenção humana) é alto em virtude do volume e da heterogeneidade das imagens que precisam ser analisadas. Nós nos concentramos na informação de cor presente nas imagens, e enfocamos os três tópicos que consideramos mais importantes para se realizar a recuperação de imagens baseada em cor: (1) como analisar e extrair informação de cor das imagens de forma automática e eficiente; (2) como representar essa informação de forma compacta e efetiva; e (3) como comparar eficientemente as características visuais que descrevem duas imagens. As principais contribuições do nosso trabalho foram dois algoritmos para a análise automática do conteúdo visual das imagens (CBC e BIC), duas funções de distância para a comparação das informações extraídas das imagens (MiCRoM e dLog) e urna representação alternativa para abordagens que decompõem e representam imagens a partir de células de tamanho fixo (CCIf)Abstract: Content-based image retrieval (CBIR) is an area that has received increasing attention from the scientific community due to the exponential growing of available images, mainly at the WWW.This has spurred great interest for systems that are able to efficiently retrieve images according to their visual content. Our work has focused in techniques suitable for broad image domains. ln a broad image domain, it is not possible to assume or use any a p1'ior'i knowledge about the visual content and/or semantic content of the images. Moreover, the cost of using semialitomatic image analysis techniques is prohibitive because of the heterogeneity and the amount of images that must be analyzed. We have directed our work to color-based image retrieval, and have focused on the three main issues that should be addressed in order to achieve color-based image retrieval: (1) how to analyze and describe images in an automatic and efficient way; (2) how to represent the image content in a compact and effective way; and (3) how to efficiently compare the visual features extracted from the images. The main contributions of our work are two algorithms to automatically analyze the visual content of the images (CBC and BIC), two distance functions to compare the visual features extracted from the images (MiCRoM and dLog), and an alteruative representation for CBIR approaches that decompose and represent images according to a grid of equalsized cells (CCH)DoutoradoDoutor em Ciência da Computaçã