• Journal of information and communication convergence engineering
• /
• v.11 no.4
• /
• pp.258-267
• /
• 2013

New incremental learning algorithm using extended data expression, based on probabilistic compounding, is presented in this paper. Incremental learning algorithm generates an ensemble of weak classifiers and compounds these classifiers to a strong classifier, using a weighted majority voting, to improve classification performance. We introduce new probabilistic weighted majority voting founded on extended data expression. In this case class distribution of the output is used to compound classifiers. UChoo, a decision tree classifier for extended data expression, is used as a base classifier, as it allows obtaining extended output expression that defines class distribution of the output. Extended data expression and UChoo classifier are powerful techniques in classification and rule refinement problem. In this paper extended data expression is applied to obtain probabilistic results with probabilistic majority voting. To show performance advantages, new algorithm is compared with Learn++, an incremental ensemble-based algorithm.

• Journal of Broadcast Engineering
• /
• v.19 no.2
• /
• pp.140-147
• /
• 2014

In this paper, we propose a method for real-time depth image refinement. In order to improve the quality of the depth map acquired from Kinect camera, we employ constant memory and texture memory which are suitable for a 2D image processing in the graphics processing unit (GPU). In addition, we applied the joint bilateral filter (JBF) in parallel to accelerate the overall execution. To enhance the quality of the depth image, we applied the JBF hierarchically using the compute unified device architecture (CUDA). Finally, we obtain the refined depth image. Experimental results showed that the proposed real-time depth image refinement algorithm improved the subjective quality of the depth image and the computational time was 260 frames per second.

• Journal of KIISE:Databases
• /
• v.30 no.3
• /
• pp.286-295
• /
• 2003

The shape of query windows for spatial selection queries is a rectangle in many cases. However, it can be issued for spatial selection queries with not only rectangular query widow, but also polygonal query window. Moreover, as the applications like GIS can manage much more spatial data, they can support the more various applications. Therefore it is valuable for considering about the query processing method suitable for not only rectangle query window, but also general polygonal one. It is the general state-of-the-art approach to use the plane- sweep technique as the computation algorithm in the refinement step as the spatial join queries do. However, from the observation on the characteristics of spatial data and query windows, we can find in many cases that the shape of query window is much simpler than that of spatial data. From these observations, we suggest a new refinement process approach which is suitable for this situation. Our experiments show that, if the number of vertices composing the query window is less than about 20, the new approach we suggest is superior to the state-of-the-art approach by about 20% in general cases.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.6
• /
• pp.2669-2688
• /
• 2016

An efficient and effective unsupervised single moving object detection framework is presented in this paper. Given the sparsely labelled trajectory points, we adopt a coarse-to-fine strategy to detect and segment the foreground from the background. The superpixel level coarse segmentation reduces the complexity of subsequent processing, and the pixel level refinement improves the segmentation accuracy. A distance measurement is devised in the coarse segmentation stage to measure the similarities between generated superpixels, which can then be used for clustering. Moreover, a Quadmap is introduced to facilitate the refinement in the fine segmentation stage. According to the experiments, our algorithm is effective and efficient, and favorable results can be achieved compared with state-of-the-art methods.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1996.10a
• /
• pp.168-175
• /
• 1996

This paper deals with the variable-node element for fluid flow and the adaptive h-version mesh refinement algorithm. The transient element has been formulated by the Galerkin approach in which the pressure term is replaced with the penalty function. The present element having variable mid-side node and is suitable for constructing a locally refined mesh avoiding the use of the highly distorted elements. A modified Gauss quadrature is needed to integrate the element matrices to solve the trouble associated with the discontinuity of derivatives of shape functions. Several numerical examples show that the proposed element can be effectively used in the h-version adapt ive mesh refinement

• 한국전산유체공학회:학술대회논문집
• /
• 1998.11a
• /
• pp.127-132
• /
• 1998

The present study is aimed to investigate flow characteristics of Two dimensional backward-facing step by numerical approach. A convection conservative difference scheme based upon SOLA algorithm is used for the solution of the two-dimensional incompressible Navier-Stokes equations to simulate the laminar, transitional and turbulent flow conditions at which the experimental data can be available for the backward-facing step. The twenty kinds of Reynolds number are used for the calculations. In an effort to demonstrate that the reported solutions are dependent on the mesh refinement, computations are performed on seven different meshes of uniformly increasing refinement. Also to investigate the result of inflow dependence, two kinds of the inflow profile are chosen for the laminar flow. As criterion of benchmarking the result of numerical simulation, reattachment length is used for the selected Reynolds numbers.

• Proceedings of the Korean Society for Language and Information Conference
• /
• 2007.11a
• /
• pp.430-439
• /
• 2007

In this paper, we use non-negative matrix factorization (NMF) to refine the document clustering results. NMF is a dimensional reduction method and effective for document clustering, because a term-document matrix is high-dimensional and sparse. The initial matrix of the NMF algorithm is regarded as a clustering result, therefore we can use NMF as a refinement method. First we perform min-max cut (Mcut), which is a powerful spectral clustering method, and then refine the result via NMF. Finally we should obtain an accurate clustering result. However, NMF often fails to improve the given clustering result. To overcome this problem, we use the Mcut object function to stop the iteration of NMF.

• Korean Journal of Remote Sensing
• /
• v.39 no.1
• /
• pp.111-127
• /
• 2023

In visual slam and 3D data modeling, the Iterative Closest Point method is a primary fundamental algorithm, and many technical fields have used this method. However, it relies on search methods that take a high search time. This paper solves this problem by applying an effective point cloud refinement method. And this paper also accelerates the point cloud registration process with an indexing scheme using the spatial decomposition method. Through some experiments, the results of this paper show that the proposed point cloud refinement method helped to produce better performance.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.14 no.11
• /
• pp.4355-4371
• /
• 2020

Phase retrieval, recovering a signal from phaseless measurements, is generally considered to be an NP-hard problem. This paper adopts an amplitude-based nonconvex optimization cost function to develop a new stochastic gradient algorithm, named stochastic reweighted phase retrieval (SRPR). SRPR is a stochastic gradient iteration algorithm, which runs in two stages: First, we use a truncated sample stochastic variance reduction algorithm to initialize the objective function. The second stage is the gradient refinement stage, which uses continuous updating of the amplitude-based stochastic weighted gradient algorithm to improve the initial estimate. Because of the stochastic method, each iteration of the two stages of SRPR involves only one equation. Therefore, SRPR is simple, scalable, and fast. Compared with the state-of-the-art phase retrieval algorithm, simulation results show that SRPR has a faster convergence speed and fewer magnitude-only measurements required to reconstruct the signal, under the real- or complex- cases.

• Proceedings of the IEEK Conference
• /
• 2002.07a
• /
• pp.141-144
• /
• 2002

In this paper, we propose a new object tracking algorithm using deformed template and Level-Set theory, which is robust against background variation, object flexibility and occlusion. The proposed tracking algorithm consists of two steps. The first step is an estimation of object shape and location, on the assumption that the transformation of object can be approximately modeled by the affine transform. The second step is a refinement of the object shape to fit into the real object accurately, by using the potential energy map and the modified Level Set speed function. Experimental results show that the proposed algorithm can track non-rigid objects with large variation in the backgrounds.