• Journal of the Korea Computer Graphics Society
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• v.8 no.2
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• pp.9-14
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• 2002

In cases of densely occluded urban scenes, it is effective to determine the visibility of scenes, since only small parts of the scene are visible from a given cell. In this paper, we introduce a new visibility preprocessing method that efficiently computes potentially visible objects for volumetric cells. The proposed method deals with general 3D polygonal models and invisible objects jointly blocked by multiple occluders. The proposed approach decomposes volume visibility into a set of point visibilities, and then computes point visibility using hardware visibility queries, in particular HP_occlusion_test and NV_occlusion_query. We carry out experiments on various large-scale scenes, and show the performance of our algorithm.

• The KIPS Transactions:PartA
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• v.15A no.1
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• pp.1-8
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• 2008

An occlusion culling method, one of visibility culling methods, excludes invisible objects or triangles which are covered by other objects. As it reduces computation quantity, occlusion culling is an effective method to handle complex scenes in real-time. But an existing common occlusion culling method, such as hardware occlusion query method, sends objects' data twice to GPU and this causes processing overheads once for occlusion culling test and the other is for rendering. And another existing hardware occlusion culling method, VCBP, can test objects' visibility quickly, but it neither test bounding volume nor return test result to application stage. In this paper, we propose a single pass occlusion culling method which uses temporal and spatial coherency, with effective occlusion culling hardware architecture. In our approach, the hardware performs occlusion culling test rapidly with cache on the rasterization stage where triangles are transformed into fragments. At the same time, hardware sends each primitive's visibility information to application stage. As a result, the application stage reduces data transmission quantity by excluding covered objects using the visibility information on previous frame and hierarchical spatial tree. Our proposed method improved maximum 44%, minimum 14% compared with S&W method based on hardware occlusion query. And the performance is increased 25% and 17% respectively, compared to maximum and minimum performance of CHC method which is based on occlusion culling method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3172-3193
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• 2018

Recently, several studies have shown that linear representation based approaches are very effective and efficient for image classification. One of these linear-representation-based approaches is the Collaborative representation (CR) method. The existing algorithms based on CR have two major problems that degrade their classification performance. First problem arises due to the limited number of available training samples. The large variations, caused by illumintion and expression changes, among query and training samples leads to poor classification performance. Second problem occurs when an image is partially noised (contiguous occlusion), as some part of the given image become corrupt the classification performance also degrades. We aim to extend the collaborative representation framework under limited training samples face recognition problem. Our proposed solution will generate virtual samples and intra-class variations from training data to model the variations effectively between query and training samples. For robust classification, the image patches have been utilized to compute representation to address partial occlusion as it leads to more accurate classification results. The proposed method computes representation based on local regions in the images as opposed to CR, which computes representation based on global solution involving entire images. Furthermore, the proposed solution also integrates the locality structure into CR, using Euclidian distance between the query and training samples. Intuitively, if the query sample can be represented by selecting its nearest neighbours, lie on a same linear subspace then the resulting representation will be more discriminate and accurately classify the query sample. Hence our proposed framework model the limited sample face recognition problem into sufficient training samples problem using virtual samples and intra-class variations, generated from training samples that will result in improved classification accuracy as evident from experimental results. Moreover, it compute representation based on local image patches for robust classification and is expected to greatly increase the classification performance for face recognition task.

• Journal of the HCI Society of Korea
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• v.1 no.1
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• pp.45-52
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm propose the depth peeling method which renders the minimal surface of objects, not whole surface, to find colliding. The Depth peeling method utilizes the state-of-the-art functionalities of GPU such as framebuffer object, vertexbuffer object, and occlusion query. Combining these functions, multi-pass rendering and context switch can be done with low overhead. Therefore proposed approach has less rendering times and rendering overhead than previous image-space collision detection. The algorithm can handle deformable objects and complex objects, and its precision is governed by the resolution of the render-target-texture. The experimental results show the feasibility of GPU-based collision detection and its performance gain in real-time applications such as 3D games.

• 한국HCI학회:학술대회논문집
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• 2006.02a
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• pp.812-817
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• 2006

This paper presents an image-space algorithm to real-time collision detection, which is run completely by GPU. For a single object or for multiple objects with no collision, the front and back faces appear alternately along the view direction. However, such alternation is violated when objects collide. Based on these observations, the algorithm has been devised, and the implementation utilizes the state-of-the-art functionalities of GPU such as framebuffer objects(FBO), vertex buffer object(VBO) and occlusion query. The experimental results show the feasibility of GPU-intensive collision detection and its performance gain in real-time applications such as 3D games.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.245-254
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• 2008

Recently, many vision-based navigation methods have been introduced as an intelligent robot application. However, many of these methods mainly focus on finding an image in the database corresponding to a query image. Thus, if the environment changes, for example, objects moving in the environment, a robot is unlikely to find consistent corresponding points with one of the database images. To solve these problems, we propose a novel navigation strategy which uses fast motion estimation and a practical scene recognition scheme preparing the kidnapping problem, which is defined as the problem of re-localizing a mobile robot after it is undergone an unknown motion or visual occlusion. This algorithm is based on motion estimation by a camera to plan the next movement of a robot and an efficient outlier rejection algorithm for scene recognition. Experimental results demonstrate the capability of the vision-based autonomous navigation against dynamic environments.

• Journal of the Korea Computer Graphics Society
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• v.15 no.4
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• pp.1-11
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• 2009

In this paper, we present a prism-based mesh culling method to improve effectiveness of continuous collision detection which is a major bottleneck in a simulation using polygonal mesh models. A prism is defined based on two matching triangles between a sequence of times m a polygonal model. In order to detect potential colliding set(PCS) of prism between two polygonal models in a unit time, we apply the visibility test based on the occlusion query to two sets of prisms which are defined from two polygonal models in a unit time. Moreover, we execute the narrow band culling based on SAT(Separating Axis Test) to define potential colliding prism pairs from PCS of prisms extracted as a result of the visibility test. In the SAT, we examine one axis to be perpendicular to a plane which divides a 3D space into two half spaces to include each prism. In the experiments, we applied the proposed culling method to pairs of polygonal models with the different size and compared the number of potential colliding prism pairs with the number of all possible prism pairs of two polygonal models. We also compared effectiveness and performance of the visibility test-based method with those of the SAT-based method as the second narrow band culling. In an experiment using two models to consist of 2916 and 2731 polygons, respectively, we got potential colliding prism pairs with 99 % of culling rate.