The patchwork engine: image segmentation from shape symmetries

Tonder, Gert J. van; Ejima, Yoshimichi

doi:10.1016/s0893-6080(99)00105-7

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…A deeper understanding of texture perception and texton theory (van Tonder and Ejima, 2000a), as well as of illusory contour perception, has arisen from work in image segmentation on a model called the "patchwork engine," by (van Tonder and Ejima, 2000b). The engine is based on the HST described in Section 11.5, which generates a full symmetry set by modifying the boundary propagation model from Blum's MA to the more general model of geometric optics.…”

Section: Perception and Cognitionmentioning

confidence: 99%

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Medial Representations

Caianiello¹

2008

Computational Imaging and Vision

174

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Section: Perception and Cognitionmentioning

confidence: 99%

“…11.14 (Reproduced with permission from G. van Tonder) Top: Schematic explanation of the Patchwork Engine (van Tonder and Ejima, 2000b). Bottom: Forward-inverse HST output segmenting various types of edge maps (van Tonder and Ejima, 2003).…”

Section: Perception and Cognitionmentioning

confidence: 99%

Medial Representations

Caianiello¹

2008

Computational Imaging and Vision

174

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“…We join the views of Pavlidis (1980), van Tonder (2000, and Ullman (1989Ullman ( , 1991Ullman ( , 1999, by extending the concept of object description into its constituent parts to the MP-based description where the atoms are considered as shape fragments. We now extend this concept to shape recognition by considering the object itself as a constituent part of the image: the assumption we make relies on describing the target image in the template shape basis.…”

Section: Mp-based Shape Recognitionmentioning

confidence: 99%

Matching pursuit‐based shape representation and recognition using scale‐space

Mendels

Thiran

2006

Int J Imaging Syst Tech

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In this paper, we propose an analytical low-level representation of images, obtained by a decomposition process, namely the matching pursuit (MP) algorithm, as a new way of describing objects through a general continuous description using an affine invariant dictionary of basis function (BFs). This description is used to recognize multiple objects in images. In the learning phase, a template object is decomposed, and the extracted subset of BFs, called meta-atom, gives the description of the object. This description is then naturally extended into the linear scale-space using the definition of our BFs, and thus providing a more general representation of the object. We use this enhanced description as a predefined dictionary of the object to conduct an MP-based shape recognition task into the linear scale-space. The introduction of the scale-space approach improves the robustness of our method: we avoid local minima issues encountered when minimizing a nonconvex energy function. We show results for the detection of complex synthetic shapes, as well as real world (aerial and medical) images. V

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“…Recently particle swarm optimization (PSO) proposed by Kebnnedy and Eberhart [19] is a heuristics-based optimization approach simulating the movements of a bird flock finding food. Most of previous versions of the particle swarm are based on continuous space, where trajectories are the changes of position on some dimensions.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data

Liu

et al. 2012

BMC Genomics

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BackgroundMulti-objective optimization (MOO) involves optimization problems with multiple objectives. Generally, theose objectives is used to estimate very different aspects of the solutions, and these aspects are often in conflict with each other. MOO first gets a Pareto set, and then looks for both commonality and systematic variations across the set. For the large-scale data sets, heuristic search algorithms such as EA combined with MOO techniques are ideal. Newly DNA microarray technology may study the transcriptional response of a complete genome to different experimental conditions and yield a lot of large-scale datasets. Biclustering technique can simultaneously cluster rows and columns of a dataset, and hlep to extract more accurate information from those datasets. Biclustering need optimize several conflicting objectives, and can be solved with MOO methods. As a heuristics-based optimization approach, the particle swarm optimization (PSO) simulate the movements of a bird flock finding food. The shuffled frog-leaping algorithm (SFL) is a population-based cooperative search metaphor combining the benefits of the local search of PSO and the global shuffled of information of the complex evolution technique. SFL is used to solve the optimization problems of the large-scale datasets.ResultsThis paper integrates dynamic population strategy and shuffled frog-leaping algorithm into biclustering of microarray data, and proposes a novel multi-objective dynamic population shuffled frog-leaping biclustering (MODPSFLB) algorithm to mine maximum bicluesters from microarray data. Experimental results show that the proposed MODPSFLB algorithm can effectively find significant biological structures in terms of related biological processes, components and molecular functions.ConclusionsThe proposed MODPSFLB algorithm has good diversity and fast convergence of Pareto solutions and will become a powerful systematic functional analysis in genome research.

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The patchwork engine: image segmentation from shape symmetries

Cited by 16 publications

References 16 publications

Medial Representations

Medial Representations

Matching pursuit‐based shape representation and recognition using scale‐space

Multi-objective dynamic population shuffled frog-leaping biclustering of microarray data

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