17,973 research outputs found
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
A Novel Active Contour Model for Texture Segmentation
Texture is intuitively defined as a repeated arrangement of a basic pattern
or object in an image. There is no mathematical definition of a texture though.
The human visual system is able to identify and segment different textures in a
given image. Automating this task for a computer is far from trivial. There are
three major components of any texture segmentation algorithm: (a) The features
used to represent a texture, (b) the metric induced on this representation
space and (c) the clustering algorithm that runs over these features in order
to segment a given image into different textures. In this paper, we propose an
active contour based novel unsupervised algorithm for texture segmentation. We
use intensity covariance matrices of regions as the defining feature of
textures and find regions that have the most inter-region dissimilar covariance
matrices using active contours. Since covariance matrices are symmetric
positive definite, we use geodesic distance defined on the manifold of
symmetric positive definite matrices PD(n) as a measure of dissimlarity between
such matrices. We demonstrate performance of our algorithm on both artificial
and real texture images
A stochastic-variational model for soft Mumford-Shah segmentation
In contemporary image and vision analysis, stochastic approaches demonstrate
great flexibility in representing and modeling complex phenomena, while
variational-PDE methods gain enormous computational advantages over Monte-Carlo
or other stochastic algorithms. In combination, the two can lead to much more
powerful novel models and efficient algorithms. In the current work, we propose
a stochastic-variational model for soft (or fuzzy) Mumford-Shah segmentation of
mixture image patterns. Unlike the classical hard Mumford-Shah segmentation,
the new model allows each pixel to belong to each image pattern with some
probability. We show that soft segmentation leads to hard segmentation, and
hence is more general. The modeling procedure, mathematical analysis, and
computational implementation of the new model are explored in detail, and
numerical examples of synthetic and natural images are presented.Comment: 22 page
Gray Image extraction using Fuzzy Logic
Fuzzy systems concern fundamental methodology to represent and process
uncertainty and imprecision in the linguistic information. The fuzzy systems
that use fuzzy rules to represent the domain knowledge of the problem are known
as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and
subsequent extraction from a noise-affected background, with the help of
various soft computing methods, are relatively new and quite popular due to
various reasons. These methods include various Artificial Neural Network (ANN)
models (primarily supervised in nature), Genetic Algorithm (GA) based
techniques, intensity histogram based methods etc. providing an extraction
solution working in unsupervised mode happens to be even more interesting
problem. Literature suggests that effort in this respect appears to be quite
rudimentary. In the present article, we propose a fuzzy rule guided novel
technique that is functional devoid of any external intervention during
execution. Experimental results suggest that this approach is an efficient one
in comparison to different other techniques extensively addressed in
literature. In order to justify the supremacy of performance of our proposed
technique in respect of its competitors, we take recourse to effective metrics
like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise
Ratio (PSNR).Comment: 8 pages, 5 figures, Fuzzy Rule Base, Image Extraction, Fuzzy
Inference System (FIS), Membership Functions, Membership values,Image coding
and Processing, Soft Computing, Computer Vision Accepted and published in
IEEE. arXiv admin note: text overlap with arXiv:1206.363
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