• KSII Transactions on Internet and Information Systems (TIIS)
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• v.2 no.6
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• pp.280-298
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• 2008

For efficient content-based image retrieval, diverse visual features such as color, texture, and shape have been widely used. In the case of leaf images, further improvement can be achieved based on the following observations. Most plants have unique shape of leaves that consist of one or more blades. Hence, blade-based matching can be more efficient than whole shape-based matching since the number and shape of blades are very effective to filtering out dissimilar leaves. Guaranteeing rotational invariance is critical for matching accuracy. In this paper, we propose a new shape representation, indexing and matching scheme for leaf image retrieval. For leaf shape representation, we generated a distance curve that is a sequence of distances between the leaf’s center and all the contour points. For matching, we developed a blade-based matching algorithm called rotation invariant - partial dynamic time warping (RI-PDTW). To speed up the matching, we suggest two additional techniques: i) priority queue-based pruning of unnecessary blade sequences for rotational invariance, and ii) lower bound-based pruning of unnecessary partial dynamic time warping (PDTW) calculations. We implemented a prototype system on the GEMINI framework [1][2]. Using experimental results, we showed that our scheme achieves excellent performance compared to competitive schemes.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.4
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• pp.1-8
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• 1994

In this paper, we propose an efficient two-step pattern matching method which promises shape distortion-tolerant recognition of handwritten of handwritten Hangul syllables. In the first step, nonlinear shape normalization is carried out to compensate for global shape distortions in handwritten characters, then a preliminary classification based on simple pattern matching is performed. In the next step, nonlinear pattern matching which achieves best matching between input and reference pattern is carried out to compensate for local shape distortions, then detailed classification which determines the final result of classification is performed. As the performance of recognition systems based on pattern matching methods is greatly effected by the quality of reference patterns. we construct reference patterns by combining the proposed nonlinear pattern matching method with a well-known averaging techniques. Experimental results reveal that recognition performance is greatly improved by the proposed two-step pattern matching method and the reference pattern construction scheme.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.307-310
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• 2009

In this paper, we present a robust shape matching approach based on bottom-up segmentation. We show how over-segmentation results can be used to overcome both ambiguity of contour matching and occlusion. To measure the shape difference between a template and the object in the input, we use oriented chamfer matching. However, in contrast to previous work, we eliminate the affection of the background clutters before calculating the shape differences using over-segmentation results. By this method, we can increase the matching cost interval between true matching and false matching, which gives reliable results. Finally, our experiments also demonstrate that our method is robust despite the presence of occlusion.

• International Journal of CAD/CAM
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• v.7 no.1
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• pp.113-120
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• 2007

Sketch based query plays an important role in shape matching for 3D shape retrieval system. Some researchers suggest the sketch based query interfaces. It is more effective to capture the needs of users rather than query by example. In this paper, we propose a new 2D sketch based query interface with coarse filters for shape matching. Coarse filtering enables to eliminate unfavorable shapes from shapes in DB. For the purpose of coarse filtering, we use two filters with the topological and geometric patterns. For the validity of our method, we show the experimental results.

• Journal of the Korean Institute of Telematics and Electronics
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• v.27 no.12
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• pp.1817-1824
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• 1990

This paper describes a shape matching algorithm for occluded or distorted two-dimensional objects. In our approach, the shape matchin is viewed as a segment matching problem. A shape matching algorithm, based on both the stochastic labeling technique and the hypothesis generate-test paradigm, is proposed, and a simple technique which performs the stochastic labeling process in accordance with the definition of consisten labeling assignment without requiring an iterative updating process of probability valiues is also proposed. Several simulation results show that the proposed algorithm is very effective when occlusion, scaling or change of orientation has occurred in the object.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.1
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• pp.133-142
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• 1993

This paper prrsents shape matching of two-dimensional object. This shape matching is applied to two-dimensional simple c10sedcurves represented by polygons. A large number of shape matching procedures have proposed baseed on teh view that shape can be represented by a vector of numerical features, and that this representation can be matched using techniques from statical pattern recognition. The varieties of features that have been extracted from shapes and used to represent them are numerous. But all of these feature-based approches suffer from the shortcoming that the descriptor of a segment of a shape do not ordinarily bear any simple relations hip to the description for the entire shape. We solve the segment matching problem of shape matching, defined as the recognition of a piece of a shape as approximate match to a part of large shape, by using relaxation labeling technique.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.882-884
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• 2003

We present an application of digital video images for object tracking. In order to track a fixed object, which was shoot on a moving vehicle, this study develops a shape-based matching algorithm to implement the tracking task. Because the shape-based matching algorithm has scale and rotation invariant characteristics, therefore it can be used to calculate the similarity between two variant shapes. An experiment is performed to track the ship object in the open sea. The result shows that the proposed method can track the object in the video images even the shape change largely.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.243-253
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• 2015

In this paper, we develop the noninformative priors for the common shape parameter of several inverse Gaussian distributions. Specially, we want to develop noninformative priors which satisfy certain objective criterion. The probability matching priors and reference priors of the common shape parameter will be developed. It turns out that the second order matching prior does not exist. The reference priors satisfy the first order matching criterion, but Jeffrey's prior is not the first order matching prior. We showed that the proposed reference prior matches the target coverage probabilities in a frequentist sense through simulation study, and an example based on real data is given.

• Proceedings of the IEEK Conference
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• 2007.07a
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• pp.327-328
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• 2007

A contour shape matching based pixel motion estimation is proposed. The pixel motion information is very useful to compensate the motion artifact generated at the specific gray level contours in the moving image for subfield gray-scale display devices. In this motion estimation method, the gray level boundary contours are extracted from the input image. Then using contour shape matching, the most similar contour in next frame is found, and the contour is divided into segment unit. The pixel motion vector is estimated from the displacement of the each segment in the contour by segment matching. From this method, more precise motion vector can be estimated and this method is more robust to image motion with rotation or from illumination variations.

• The Journal of Korea Robotics Society
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• v.4 no.3
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.