Two new concepts for N/sub 16/ space

Zhang, Y.J.

doi:10.1109/97.782064

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…If the targets are cluttered, the boundary length and thus the prior energy tend to be large; if the targets are in blob shape, the boundary length and its corresponding energy tend to be small. (Note alternative 8 or even 16 connected neighbors [19] could be considered but would be slower.) The optimization of this prior energy encourages spatial cohesion.…”

Section: The Prior Modelmentioning

confidence: 99%

“…For image pixels with feature values between T l and T h , they are classified as target pixels only if they are quasi-connected with a region containing at least one pixel whose feature value is greater than the high threshold. The quasi-connected components differ from traditional connected components in that the computation is carried out using a generalized neighborhood similar to N 16 [19], but more efficient because it uses a reduced image. This multi-scale scheme improves the computation speed and the generalized neighborhood automatically fills small gaps.…”

Section: Deterministic Energy Minimizationmentioning

confidence: 99%

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Two thresholds are better than one

Zhang

Boult

Johnson

2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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The concept of the Bayesian optimal single threshold is a well established and widely used classification technique. In this paper, we prove that when spatial cohesion is assumed for targets, a better classification result than the "optimal" single threshold classification can be achieved. Under the assumption of spatial cohesion and certain prior knowledge about the target and background, the method can be further simplified as dual threshold classification. In core-dual threshold classification, spatial cohesion within the target core allows "continuation" linking values to fall between the two thresholds to the target core; classical Bayesian classification is employed beyond the dual thresholds. The core-dual threshold algorithm can be built into a Markov Random Field model (MRF). From this MRF model, the dual thresholds can be obtained and optimal classification can be achieved. In some practical applications, a simple method called symmetric subtraction may be employed to determine effective dual thresholds in real time. Given dual thresholds, the Quasi-Connected Component algorithm is shown to be a deterministic implementation of the MRF core-dual threshold model combining the dual thresholds, extended neighborhoods and efficient connected component computation.

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Section: The Prior Modelmentioning

confidence: 99%

Section: Deterministic Energy Minimizationmentioning

confidence: 99%

Two thresholds are better than one

Zhang

Boult

Johnson

2007

2007 IEEE Conference on Computer Vision and Pattern Recognition

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Fast and Robust 2D-Shape Extraction Using Discrete-Point Sampling and Centerline Grouping in Complex Images

Zhu

Wang

Liu

et al. 2013

IEEE Trans. on Image Process.

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This paper initially develops the discrete-point sampling operator's concept, model, and parameters that we have previously proposed, and makes its belt-shaped regions in a discrete-point sampling map more salient and appropriate for centerline extraction. The cross-sectional features of these belt-shaped regions are then analyzed and seven types of feature points are defined to facilitate descriptions of such features. Based on these feature points, a three-level detection system is proposed, including feature points, line segments, and centerlines, to extract centerlines from the belt-shaped regions. Eight basic types of centerlines and five types of relationships among the centerlines are defined by computational geometry algorithms, and Gestalt laws are used to cluster them into groupings. If some prior information about a desired shape is available, retrieval grouping may be carried out by a discrete-point sampling map, the purpose of which is to find centerlines by best matching with prior information. Discrete-point sampling effectually overcomes the influences of interference from noise, textures, and uneven illumination, and greatly reduces the difficulty of centerline extraction. Centerline clustered groupings and retrieval grouping can offer a strong anti-interference ability with nonlinear deformations such as articulation and occlusion. This method can extract large-scale complex shapes combined of lines and planes from complex images. The wheel location results of noise test and other shape extraction experiments show that our method has a strong capability to persist with nonlinear deformations.

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Two new concepts for N/sub 16/ space

Cited by 2 publications

References 4 publications

Two thresholds are better than one

Two thresholds are better than one

Fast and Robust 2D-Shape Extraction Using Discrete-Point Sampling and Centerline Grouping in Complex Images

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