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On Watershed Cuts 

Presenter: Jean Cousty (ESIEE)
Date: 04/07/2009

Abstract


The watershed transform is an efficient and popular tool for image segmentation. In this talk, we study the watersheds in edge-weighted graphs. We define the watershed cuts following the intuitive idea of drops of water flowing on a topographic surface.

In a first part, we establish the consistency of these watersheds: they can be equivalently defined by their catchment basins (through a steepest descent property) or by the dividing lines separating these catchment basins (through the drop of water principle). Then, we prove, through an equivalence theorem, their optimality in terms of minimum spanning forests.

In a second part, we present a thinning paradigm from which we derive three algorithmic watershed cut strategies: the first one is well suited to parallel implementations, the second one leads to a flexible linear-time sequential implementation whereas the third one links the watershed cuts and the popular flooding algorithms.

In the third part of the talk, we state that the watershed cuts preserve a notion of contrast, called connection value, on which are (implicitly) based several morphological region merging methods. This leads us to establish the links and differences between watershed cuts, minimum spanning forests and shortest-path forests.

Finally, we conclude the talk by showing illustrations of the proposed framework to the segmentation of grayscale images, artwork surfaces and diffusion tensor images.

A Semi Automatic Methodology for Segmentation of the Coronary Artery Tree from Angiography 

Presenter: Daniel da Silva Diogo Lara (UFMG)
Date: 04/07/2009

Abstract


Nowadays, medical diagnostics using images has a considerable importance in many areas of medicine. It promotes and makes easier the ways for acquisition, transmission and analysis of medical images. The use of digital images in the medical area is still growing up and new application modalities are always appearing. Coronary Artery Disease (CAD) is the narrowing or blockage of arteries that provide the heart muscle with blood. Coronary angiography remains to be an indispensable tool in clinics today for the diagnosis of CAD. A fundamental component of a semi automatic angiography analysis is vessel detection. Vessel detection is a recognition problem that is challenging due to the complex nature of vascular trees and to imaging imperfections. One inherent imperfection of angiography is the intensity inhomogeneity between the larger and smaller vessels. Another imperfection common among many angiographic methods is the leakage of contrast agent into the background tissue that reduces the contrast between vessels and tissue. This work presents a developing methodology for a semi automatic segmentation of the coronary artery tree from angiography.

An Approach for Photometric Validation in On-board Systems 

Presenter: Alexandre Wagner Chagas Faria (UFMG)
Date: 04/07/2009

Abstract


Visual Information still represents one of the most common ways of interaction between a machine and a human being. In order to facilitate this interaction, machines are equipped with luminous components that form an on-board environment. In vehicles, interaction with conductors is made through the reading of the cluster information, the radio operability, and any other luminous component. Hence, it is very important that the internal lighting of a vehicle is in good harmony with the customer. To achieve this harmony, in this work photometric characteristics of the components, such as intensity, color, and homogeneity, are studied and measured. The goal of this work is to develop a methodology, based on the human visual perception, to automatically identify and quantify non-homogeneous regions of the lighting distribution in on-board systems. This is going to be done through the analysis of lighting components in digital images.

Human Actions Recognition 

Presenter: Ana Paula Brandão Lopes (UFMG)
Date: 04/06/2009

Abstract


The ability to automatically recognize human actions directly from video information has many potential applications, like improving video content-based indexing and retrieval, identifying suspect behavior in surveillance scenarios, remotely monitoring elderly people or analyzing sports videos, for example. In this presentation, we provide an overview of the different representation approaches for human action recognition. Then, we provide some detail for a promising one, namely, bag of visual features (BOVF). Finally, we show some results we achieved with a BOVF implementation of ours in a standard human actions database.

FReBIR: Fuzzy Region-Based Image Retrieval 

Presenter: Sylvie Philipp-Foliguet (ENSEA)
Date: 04/06/2009

Abstract


FReBIR is a method of image indexing and retrieval which takes into account the relative positions of the regions within the image. Indexing is based on a fuzzy segmentation of the image. Fuzzy regions are then indexed by colour and texture. The image retrieval is based on inexact graph matching, taking into account both the similarity between regions and the spatial relation between them. We propose, on one hand a solution to reduce the combinatorial complexity of the graph matching, and on the other hand, several measures of similarity between graphs allowing the result images ranking. Similarity measures use kernel functions adapted to vectors, bags of features or graphs. SVM classifiers are used through relevance feedback loops to retrieve categories of images.

Applications concern image and 3D object retrieval. The method is adapted to partial queries, aiming for example at retrieving images containing a specific type of object.

NPDI

Núcleo de Processamento Digital de Imagens.
Departamento de Ciência da Computação.
Instituto de Ciências Exatas.
Universidade Federal de Minas Gerais

 

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Belo Horizonte - Minas Gerais - Brasil.

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