• 전자공학회논문지 IE
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• v.45 no.2
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• pp.34-39
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• 2008

This study established the efficient system that retrieves the 3D model by using a statistical technique called the function of discriminant analysis. This method was suggested to search index, which was formed by the statistics of 128 feature vectors including those scope, minimum value, average, standard deviation, skewness and scale. All of these were sampled with Osada's D2 method and the statistics as a factor effecting a change turned the value of discriminant analytic function into that of index. Through the primary retrieval on the model of query, the class above the top 2% was drawn out by comparing the query with the index of previously saved class from the group of same models. This method was proved an efficient retrieval technique that saved its procedural time. It shortened the retrieval time for 3D model by 57% faster than the existing Osada's method, and the precision that similar models were found in the first place was recorded 0.362, which revealed it more efficient by 44.8%.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.57-60
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• 2001

In this paper, we propose a new 3D object retrieval system using the shape information of 2D silhouette images. 2D images at different view points are derived from a 3D model and linked to the model. Shape feature of 2D image is extracted by a region-based descriptor. In the experiment, we compare the results of the proposed system with those of the system using curvature scale space(CSS) to show the efficiency of our system.

• Journal of Broadcast Engineering
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• v.13 no.6
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• pp.828-837
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• 2008

We propose a method to select representative views of single objects and classes of objects for 3D object retrieval and classification. Our method is based on projected 2D shapes, or views, of the 3D objects, where the representative views are selected by applying affinity propagation to cluster uniformly sampled views. Affinity propagation assigns prototypes to each cluster during the clustering process, thereby providing a natural criterion to select views. We recursively apply affinity propagation to the selected views of objects classified as single classes to obtain representative views of classes of objects. By enabling classification as well as retrieval, effective management of large scale databases for retrieval can be enhanced, since we can avoid exhaustive search over all objects by first classifying the object. We demonstrate the effectiveness of the proposed method for both retrieval and classification by experimental results based on the Princeton benchmark database [16].

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1188-1193
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• 2013

In content based image retrieval (CBIR) system, the images are represented based upon its feature such as color, texture, shape, and spatial relationship etc. In this paper, we propose a MRI Image Retrieval using wavelet transform with mahalanobis distance measurement. Wavelet transformation can also be easily extended to 2-D (image) or 3-D (volume) data by successively applying 1-D transformation on different dimensions. The proposed algorithm has tested using wavelet transform and performance analysis have done with HH and $H^*$ elimination methods. The retrieval image is the relevance between a query image and any database image, the relevance similarity is ranked according to the closest similar measures computed by the mahalanobis distance measurement. An adaptive similarity synthesis approach based on a linear combination of individual feature level similarities are analyzed and presented in this paper. The feature weights are calculated by considering both the precision and recall rate of the top retrieved relevant images as predicted by our enhanced technique. Hence, to produce effective results the weights are dynamically updated for robust searching process. The experimental results show that the proposed algorithm is easily identifies target object and reduces the influence of background in the image and thus improves the performance of MRI image retrieval.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10C
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• pp.1007-1016
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• 2005

This paper presents a feature extraction method for shape based retrieval of 3D models. Since the feature descriptor of 3D model should be invariant to translation, rotation and scaling, it is necessary to preprocess the 3D models to represent them in a canonical coordinate system. We use the PCA(Principal Component Analysis) method to preprocess the 3D models. Also, we apply that to make a MBR(Minimum Boundary Rectangle) and a circumsphere. The proposed algorithm is as follows. We generate a circumsphere around 3D models, where radius equals 1(r=1) and locate each model in the center of the circumsphere. We produce the concentric spheres with a different radius($r_i=i/n,\;i=1,2,{\ldots},n$). After looking for meshes intersected with the concentric spheres, we compute the curvature of the meshes. We use these curvatures as the model descriptor. Experimental results numerically show the performance improvement of proposed algorithm from min. 0.1 to max. 0.6 in comparison with conventional methods by ANMRR, although our method uses .relatively small bins. This paper uses $R{^*}-tree$ as the indexing.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.2 s.308
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• pp.72-80
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• 2006

In this paper, we propose a new descriptor for 3D model retrieval based on shape information. The proposed method consists of two steps including ray casting method and spherical harmonic function, considering geometric properties of model. In the ray casting method, an adaptive sampling is performed for external shape information. By increasing shape information included in the descriptor, we improve the discriminating power of the proposed descriptor. The coefficients of spherical harmonic function are adaptively calculated, considering geometric frequency characteristics. This makes the descriptor more compact and concise without decreasing the retrieval performance. By combining two methods, we achieve more improved retrieval results.

• 한국HCI학회:학술대회논문집
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• 2009.02a
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• pp.669-672
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• 2009

The techniques extract shape descriptors from 3D models and use these descriptors for indices for comparing shape similarities. Most similarity search techniques focus on comparisons of each individual 3D model from databases. However, our similarity search technique can compare not only each individual 3D model, but also partial shape similarities. The partial shape matching technique extends the user's query request by finding similar parts of 3D models and finding 3D models which contain similar parts. We have implemented an experimental partial shape-matching search system for 3D pagoda models, and preliminary experiments show that the system successfully retrieves similar 3D model parts efficiently.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.2
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• pp.429-435
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• 2010

Three dimensional (3-D) object detection and pose estimation from a single view query image has been an important issue in various fields such as medical applications, robot vision, and manufacturing automation. However, most of the existing methods are not appropriate in a real time environment since object detection and pose estimation requires extensive information and computation. In this paper, we present a fast 3-D object detection and pose estimation scheme based on surrounding camera view-changed images of objects. Our scheme has two parts. First, we detect images similar to the query image from the database based on the shape feature, and calculate candidate poses. Second, we perform accurate pose estimation for the candidate poses using the scale invariant feature transform (SIFT) method. We earned out extensive experiments on our prototype system and achieved excellent performance, and we report some of the results.

• The KIPS Transactions:PartD
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• v.15D no.3
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• pp.399-410
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• 2008

This paper suggests a new retrieval method using weighted vector sum to resolve a problem of traditional location-based retrieval method, nearest neighbor (NN) query, and NN query using direction. The proposed method filters out data with the radius, and then the remained retrieval area is filtered by a direction information compounded of a user's moving direction, a pre-fixed interesting direction, and a pre-fixed retrieval angle. The moving direction is computed from a vector or a weighted vector sum of several vectors using a weight to adopt several cases. The retrieval angle can be set from traditional $360^{\circ}$ to any degree you want. The retrieval data for this method can be a still and moving image recorded shooting location, and also several type of media like text, web, picture offering to customer with location of company or resort. The suggested method guarantees more accurate retrieval than traditional location-based retrieval methods because that the method selects data within the radius and then removes data of useless areas like passed areas or an area of different direction. Moreover, this method is more flexible and includes the direction based NN.

• 전자공학회논문지 IE
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• v.46 no.1
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• pp.37-43
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• 2009

In this paper, we propose a new algorithm for 3D model retrieval based on spherical coordinate system. We obtains sample points in a polygons on 3D model. We convert a point in cartesian coordinates(x, y, z) to it in spherical coordinate. 3D shape features are achieved by adopting distribution of zenith of sample point in spherical coordinate. We used Osada's method for obtaining sample points on 3D model and the PCA method for the pose standardization 3D model. Princeton university's benchmark data was used for this research. Experimental results numerically show the precision improvement of proposed algorithm 12.6% in comparison with Vranic's depth buffer-based feature vector algorithm.