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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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The KIPS Transactions:PartB
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Korea Information Processing Society
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Volume & Issues
Volume 17B, Issue 6 - Dec 2010
Volume 17B, Issue 5 - Oct 2010
Volume 17B, Issue 4 - Aug 2010
Volume 17B, Issue 3 - Jun 2010
Volume 17B, Issue 2 - Apr 2010
Volume 17B, Issue 1 - Feb 2010
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Panoramic Image Composition Algorithm through Scaling and Rotation Invariant Features
Kwon, Ki-Won ; Lee, Hae-Yeoun ; Oh, Duk-Hwan ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 333~344
DOI : 10.3745/KIPSTB.2010.17B.5.333
This paper addresses the way to compose paronamic images from images taken the same objects. With the spread of digital camera, the panoramic image has been studied to generate with its interest. In this paper, we propose a panoramic image generation method using scaling and rotation invariant features. First, feature points are extracted from input images and matched with a RANSAC algorithm. Then, after the perspective model is estimated, the input image is registered with this model. Since the SURF feature extraction algorithm is adapted, the proposed method is robust against geometric distortions such as scaling and rotation. Also, the improvement of computational cost is achieved. In the experiment, the SURF feature in the proposed method is compared with features from Harris corner detector or the SIFT algorithm. The proposed method is tested by generating panoramic images using
images. Results show that it takes 0.4 second in average for computation and is more efficient than other schemes.
Fast Natural Feature Tracking Using Optical Flow
Bae, Byung-Jo ; Park, Jong-Seung ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 345~354
DOI : 10.3745/KIPSTB.2010.17B.5.345
Visual tracking techniques for Augmented Reality are classified as either a marker tracking approach or a natural feature tracking approach. Marker-based tracking algorithms can be efficiently implemented sufficient to work in real-time on mobile devices. On the other hand, natural feature tracking methods require a lot of computationally expensive procedures. Most previous natural feature tracking methods include heavy feature extraction and pattern matching procedures for each of the input image frame. It is difficult to implement real-time augmented reality applications including the capability of natural feature tracking on low performance devices. The required computational time cost is also in proportion to the number of patterns to be matched. To speed up the natural feature tracking process, we propose a novel fast tracking method based on optical flow. We implemented the proposed method on mobile devices to run in real-time and be appropriately used with mobile augmented reality applications. Moreover, during tracking, we keep up the total number of feature points by inserting new feature points proportional to the number of vanished feature points. Experimental results showed that the proposed method reduces the computational cost and also stabilizes the camera pose estimation results.
Robust AAM-based Face Tracking with Occlusion Using SIFT Features
Eom, Sung-Eun ; Jang, Jun-Su ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 355~362
DOI : 10.3745/KIPSTB.2010.17B.5.355
Face tracking is to estimate the motion of a non-rigid face together with a rigid head in 3D, and plays important roles in higher levels such as face/facial expression/emotion recognition. In this paper, we propose an AAM-based face tracking algorithm. AAM has been widely used to segment and track deformable objects, but there are still many difficulties. Particularly, it often tends to diverge or converge into local minima when a target object is self-occluded, partially or completely occluded. To address this problem, we utilize the scale invariant feature transform (SIFT). SIFT is an effective method for self and partial occlusion because it is able to find correspondence between feature points under partial loss. And it enables an AAM to continue to track without re-initialization in complete occlusions thanks to the good performance of global matching. We also register and use the SIFT features extracted from multi-view face images during tracking to effectively track a face across large pose changes. Our proposed algorithm is validated by comparing other algorithms under the above 3 kinds of occlusions.
Automatic Method for Extracting Homogeneity Threshold and Segmenting Homogeneous Regions in Image
Han, Gi-Tae ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 363~374
DOI : 10.3745/KIPSTB.2010.17B.5.363
In this paper, we propose the method for extracting Homogeneity Threshold(
) and for segmenting homogeneous regions by USRG(Unseeded Region Growing) with
is a criterion to distinguish homogeneity in neighbor pixels and is computed automatically from the original image by proposed method. Theoretical background for proposed method is based on the Otsu`s single level threshold method. The method is used to divide a small local part of original image int o two classes and the sum(
) of standard deviations for the classes to satisfy special conditions for distinguishing as different regions from each other is used to compute
. To find validity for proposed method, we compare the original image with the image that is regenerated with only the segmented homogeneous regions and show up the fact that the difference between two images is not exist visually and also present the steps to regenerate the image in order the size of segmented homogeneous regions and in order the intensity that includes pixels. Also, we show up the validity of proposed method with various results that is segmented using the homogeneity thresholds(
) that is added a coefficient
for adjusting scope of
. We expect that the proposed method can be applied in various fields such as visualization and animation of natural image, anatomy and biology and so on.
Active Contour Model for Boundary Detection of Multiple Objects
Jang, Jong-Whan ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 375~380
DOI : 10.3745/KIPSTB.2010.17B.5.375
Most of previous algorithms of object boundary extraction have been studied for extracting the boundary of single object. However, multiple objects are much common in the real image. The proposed algorithm of extracting the boundary of each of multiple objects has two steps. In the first step, we propose the fast method using the outer and inner products; the initial contour including multiple objects is split and connected and each of new contours includes only one object. In the second step, an improved active contour model is studied to extract the boundary of each object included each of contours. Experimental results with various test images have shown that our algorithm produces much better results than the previous algorithms.
Gradient Descent Approach for Value-Based Weighting
Lee, Chang-Hwan ; Bae, Joo-Hyun ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 381~388
DOI : 10.3745/KIPSTB.2010.17B.5.381
Naive Bayesian learning has been widely used in many data mining applications, and it performs surprisingly well on many applications. However, due to the assumption that all attributes are equally important in naive Bayesian learning, the posterior probabilities estimated by naive Bayesian are sometimes poor. In this paper, we propose more fine-grained weighting methods, called value weighting, in the context of naive Bayesian learning. While the current weighting methods assign a weight to each attribute, we assign a weight to each attribute value. We investigate how the proposed value weighting effects the performance of naive Bayesian learning. We develop new methods, using gradient descent method, for both value weighting and feature weighting in the context of naive Bayesian. The performance of the proposed methods has been compared with the attribute weighting method and general Naive bayesian, and the value weighting method showed better in most cases.
An Ant Colony Optimization Heuristic to solve the VRP with Time Window
Hong, Myung-Duk ; Yu, Young-Hoon ; Jo, Geun-Sik ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 389~398
DOI : 10.3745/KIPSTB.2010.17B.5.389
The Vehicle Routing and Scheduling Problem with Time Windows(VRSPTW) is to establish a delivery route of minimum cost satisfying the time constraints and capacity demands of many customers. The VRSPTW takes a long time to generate a solution because this is a NP-hard problem. To generate the nearest optimal solution within a reasonable time, we propose the heuristic by using an ACO(Ant Colony Optimization) with multi-cost functions. The multi-cost functions can generate a feasible initial-route by applying various weight values, such as distance, demand, angle and time window, to the cost factors when each ant evaluates the cost to move to the next customer node. Our experimental results show that our heuristic can generate the nearest optimal solution more efficiently than Solomon I1 heuristic or Hybrid heuristic applied by the opportunity time.
A Method for Precision Improvement Based on Core Query Clusters and Term Proximity
Jang, Kye-Hun ; Lee, Kyung-Soon ;
The KIPS Transactions:PartB, volume 17B, issue 5, 2010, Pages 399~404
DOI : 10.3745/KIPSTB.2010.17B.5.399
In this paper, we propose a method for precision improvement based on core clusters and term proximity. The method is composed by three steps. The initial retrieval documents are clustered based on query term combination, which occurred in the document. Core clusters are selected by using proximity between query terms. Then, the documents in core clusters are reranked based on context information of query. On TREC AP test collection, experimental results in precision at the top documents(P@100) show that the proposed method improved 11.2% over the language model.