• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.11A
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• pp.1946-1956
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• 2001

In this paper, we propose a novel 2-D partial matching algorithm based on model-based stochastic analysis of feature correspondences in a relation vector space, which is quite robust to shape variations as well as invariant to geometric transformations. We represent an object using the ARG (Attributed Relational Graph) model with features of a set of relation vectors. In addition, we statistically model the partial occlusion or noise as the distortion of the relation vector distribution in the relation vector space. Our partial matching algorithm consists of two-phases. First, a finite number of candidate sets areselected by using logical constraint embedding local and structural consistency Second, the feature loss detection is done iteratively by error detection and voting scheme thorough the error analysis of relation vector space. Experimental results on real images demonstrate that the proposed algorithm is quite robust to noise and localize target objects correctly even inseverely noisy and occluded scenes.

• Journal of KIISE:Databases
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• v.34 no.4
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• pp.304-314
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• 2007

Content Based Image Retrieval (CBIR) is to store and retrieve images using the feature description of image contents. In order to support more accurate image retrieval, it has become necessary to develop features that can effectively describe image contents. The commonly used low-level features, such as color, texture, and shape features may not be directly mapped to human visual perception. In addition, such features cannot effectively describe a single image that contains multiple objects of interest. As a result, the research on feature descriptions has shifted to focus on higher-level features, which support representations more similar to human visual perception like spatial relationships between objects. Nevertheless, the prior works on the representation of spatial relations still have shortcomings, particularly with respect to supporting rotational invariance, Rotational invariance is a key requirement for a feature description to provide robust and accurate retrieval of images. This paper proposes a high-level feature named 8AB (8 Angular Bin) that effectively describes the spatial relations of objects in an image while providing rotational invariance. With this representation, a similarity calculation and a retrieval technique are also proposed. In addition, this paper proposes a search-space pruning technique, which supports efficient image retrieval using the 8AB feature. The 8AB feature is incorporated into a CBIR system, and the experiments over both real and synthetic image sets show the effectiveness of 8AB as a high-level feature and the efficiency of the pruning technique.

• The Journal of Engineering Geology
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• v.32 no.1
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• pp.41-57
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• 2022

A workflow is presented to estimate the size of a representative elementary volume and 3-D hydraulic conductivity tensor based on fluid flow analysis for a discrete fracture network (DFN). A case study is considered for a Cretaceous granitic rock mass at Gijang in Busan, Korea. The intensity and size of joints were calibrated using the first invariant of the fracture tensor for the 2-D DFN of the study area. Effective hydraulic apertures were obtained by analyzing the results of field packer tests. The representative elementary volume of the 2-D DFN was determined to be 20 m square by investigating the variations in the directional hydraulic conductivity for blocks of different sizes. The directional hydraulic conductivities calculated from the 2-D DFN exhibited strong anisotropy related to the hydraulic behavior of the study area. The 3-D hydraulic conductivity tensor for the fractured rock mass of the study area was estimated from the directional block conductivities of the 2-D DFN blocks generated for various directions in 3-D. The orientations of the principal components of the 3-D hydraulic conductivity tensor were found to be identical to those of delineated joint sets in the study area.