381 research outputs found
Video object tracking : contributions to object description and performance assessment
Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Universidade do Porto. Faculdade de Engenharia. 201
A Generative Model for Concurrent Image Retrieval and ROI Segmentation
This paper proposes a probabilistic generative model that concurrently tackles the problems of image retrieval and region-of-interest (ROI) segmentation. Specifically, the proposed model takes into account several properties of the matching process between two objects in different images, namely: objects undergoing a geometric transformation, typical spatial location of the region of interest, and visual similarity. In this manner, our approach improves the reliability of detected true matches between any pair of images. Furthermore, by taking advantage of the links to the ROI provided by the true matches, the proposed method is able to perform a suitable ROI segmentation. Finally, the proposed method is able to work when there is more than one ROI in the query image. Our experiments on two challenging image retrieval datasets proved that our approach clearly outperforms the most prevalent approach for geometrically constrained matching and compares favorably to most of the state-of-the-art methods. Furthermore, the proposed technique concurrently provided very good segmentations of the ROI. Furthermore, the capability of the proposed method to take into account several objects-of-interest was also tested on three experiments: two of them concerning image segmentation and object detection in multi-object image retrieval tasks, and another concerning multiview image retrieval. These experiments proved the ability of our approach to handle scenarios in which more than one object of interest is present in the query.This work has been partially supported by the project AFICUS, co-funded by the Spanish Ministry of Industry, Trade and Tourism, and the European Fund for Regional Development, with Ref.: TSI-020110-2009-103, and the National Grant TEC2011-26807 of the Spanish Ministry of Science and Innovation.Publicad
Object segmentation from low depth of field images and video sequences
This thesis addresses the problem of autonomous object segmentation. To do so
the proposed segementation method uses some prior information, namely that the
image to be segmented will have a low depth of field and that the object of interest
will be more in focus than the background. To differentiate the object from the
background scene, a multiscale wavelet based assessment is proposed. The focus
assessment is used to generate a focus intensity map, and a sparse fields level set
implementation of active contours is used to segment the object of interest. The
initial contour is generated using a grid based technique.
The method is extended to segment low depth of field video sequences with
each successive initialisation for the active contours generated from the binary dilation
of the previous frame's segmentation. Experimental results show good segmentations
can be achieved with a variety of different images, video sequences, and
objects, with no user interaction or input.
The method is applied to two different areas. In the first the segmentations
are used to automatically generate trimaps for use with matting algorithms. In the
second, the method is used as part of a shape from silhouettes 3D object reconstruction
system, replacing the need for a constrained background when generating
silhouettes. In addition, not using a thresholding to perform the silhouette segmentation
allows for objects with dark components or areas to be segmented accurately.
Some examples of 3D models generated using silhouettes are shown
Data-Driven Shape Analysis and Processing
Data-driven methods play an increasingly important role in discovering
geometric, structural, and semantic relationships between 3D shapes in
collections, and applying this analysis to support intelligent modeling,
editing, and visualization of geometric data. In contrast to traditional
approaches, a key feature of data-driven approaches is that they aggregate
information from a collection of shapes to improve the analysis and processing
of individual shapes. In addition, they are able to learn models that reason
about properties and relationships of shapes without relying on hard-coded
rules or explicitly programmed instructions. We provide an overview of the main
concepts and components of these techniques, and discuss their application to
shape classification, segmentation, matching, reconstruction, modeling and
exploration, as well as scene analysis and synthesis, through reviewing the
literature and relating the existing works with both qualitative and numerical
comparisons. We conclude our report with ideas that can inspire future research
in data-driven shape analysis and processing.Comment: 10 pages, 19 figure
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