• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.29 no.8C
• /
• pp.1159-1174
• /
• 2004

In this paper, we present an efficient algorithm for region analysis of business card images acquired in a PDA by using DCT and information pixel density. The proposed method consists of three parts: region segmentation, information region classification, and text region classification. In the region segmentation, an input business card image is partitioned into 8 f8 blocks and the blocks are classified into information and background blocks using the normalized DCT energy in their low frequency bands. The input image is then segmented into information and background regions by region labeling on the classified blocks. In the information region classification, each information region is classified into picture region or text region by using a ratio of the DCT energy of horizontal and vertical edge components to that in low frequency band and a density of information pixels, that are black pixels in its binarized region. In the text region classification, each text region is classified into large character region or small character region by using the density of information pixels and an averaged horizontal and vertical run-lengths of information pixels. Experimental results show that the proposed method yields good performance of region segmentation, information region classification, and text region classification for test images of several types of business cards acquired by a PDA under various surrounding conditions. In addition, the error rates of the proposed region segmentation are about 2.2-10.1% lower than those of the conventional region segmentation methods. It is also shown that the error rates of the proposed information region classification is about 1.7% lower than that of the conventional information region classification method.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.22 no.4
• /
• pp.771-781
• /
• 1997

In order to solve the problem of uncovered background region due to the region-due to the region-based motion estimation, this paper presents a new method which generates the uncovered region memory using motion estimation and shows the application of the algorithm for very low bit rate video coding. The proposed algorithm can be briefly described as follows it detects the changed region by using the information of FD(frame difference) and segmentation, and then as for only that region the backward motion estimation without transmission of shape information is done. Therefore, from only motion information the uncovered background region memory is generated and updated. The contents stored in the uncovered background region memory are referred whenever the uncovered region comes into existence. The regions with large prediction error are transformed and coded by using DCT. As results of simulation, the proposed algorithm shows the superior improvement in the subjective and objective image quality due to the remarkable reduction of transmission bits for prediction error.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.6
• /
• pp.2619-2643
• /
• 2018

In this paper, a new clustering algorithm based on the connected region generation (CRG-clustering) is proposed. It is an effective and robust approach to clustering on the basis of the connectivity of the points and their neighbors. In the new algorithm, a connected region generating (CRG) algorithm is developed to obtain the connected regions and an isolated point set. Each connected region corresponds to a homogeneous cluster and this ensures the separability of an arbitrary data set theoretically. Then, a region expansion strategy and a consensus criterion are used to deal with the points in the isolated point set. Experimental results on the synthetic datasets and the real world datasets show that the proposed algorithm has high performance and is insensitive to noise.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.10 no.9
• /
• pp.4386-4404
• /
• 2016

Most existing salient object detection algorithms commonly employed segmentation techniques to eliminate background noise and reduce computation by treating each segment as a processing unit. However, individual small segments provide little information about global contents. Such schemes have limited capability on modeling global perceptual phenomena. In this paper, a novel salient object detection algorithm is proposed based on region merging. An adaptive-based merging scheme is developed to reassemble regions based on their color dissimilarities. The merging strategy can be described as that a region R is merged with its adjacent region Q if Q has the lowest dissimilarity with Q among all Q's adjacent regions. To guide the merging process, superpixels that located at the boundary of the image are treated as the seeds. However, it is possible for a boundary in the input image to be occupied by the foreground object. To avoid this case, we optimize the boundary influences by locating and eliminating erroneous boundaries before the region merging. We show that even though three simple region saliency measurements are adopted for each region, encouraging performance can be obtained. Experiments on four benchmark datasets including MSRA-B, SOD, SED and iCoSeg show the proposed method results in uniform object enhancement and achieve state-of-the-art performance by comparing with nine existing methods.

• Proceedings of the IEEK Conference
• /
• 2000.06d
• /
• pp.163-166
• /
• 2000

Real-time traffic detection scheme based on Computer Vision is capable of efficient traffic control using automatically computed traffic information and obstacle detection in moving automobiles. Traffic information is extracted by segmenting vehicle region from road images, in traffic detection system. In this paper, we propose the advanced segmentation of vehicle from road images using multiple local region information. Because multiple local region overlapped in the same lane is processed sequentially from small, the traffic detection error can be corrected.

• Proceedings of the IEEK Conference
• /
• 2003.11a
• /
• pp.449-452
• /
• 2003

This paper propose a extracting method of the region for image using segmentation and edge information. First propose algorithm extract information using canny edge detector and the image was divided by watershed segmentation. And it extract the mage with edge information by merging region. Finally we compare the proposed method with levelset method. In the result proposed method not only extract the image with accurate region but also reduce operation time.

• Journal of Korea Multimedia Society
• /
• v.19 no.6
• /
• pp.979-989
• /
• 2016

Traversable region detection is an essential step for advanced driver assistance systems and self-driving car systems, and it has been conducted by detecting lanes from input images. The performance can be unreliable, however, when the light condition is poor or there exist no lanes on the roads. To solve this problem, this paper proposes an algorithm which utilizes the information about the number of lanes and texture analysis. The proposed algorithm first specifies road region candidates by utilizing the number of lanes information. Among road region candidates, the road region is determined as the region in which texture is homogeneous and texture discontinuities occur around its boundaries. Traversable region is finally detected by dividing the estimated road region with the number of lanes information. This paper combines the proposed algorithm with a lane detection-based method to construct a system, and simulation results show that the system detects traversable region even on the road with poor light conditions or no lanes.

• ETRI Journal
• /
• v.37 no.5
• /
• pp.1023-1031
• /
• 2015

In infrared (IR) images, near targets have a transient distribution at the boundary region, as opposed to a steady one at the inner region. Based on this fact, this paper proposes a novel IR target extraction method that uses both a weighted information entropy (WIE) and an adaptive opening filter to extract near finely shaped targets in IR images. Firstly, the boundary region of a target is detected using a local variance WIE of an original image. Next, a coarse target region is estimated via a labeling process used on the boundary region of the target. From the estimated coarse target region, a fine target shape is extracted by means of an opening filter having an adaptive structure element. The size of the structure element is decided in accordance with the width information of the target boundary and mean WIE values of windows of varying size. Our experimental results show that the proposed method obtains a better extraction performance than existing algorithms.

• Proceedings of the Korean Information Science Society Conference
• /
• 1999.10c
• /
• pp.203-205
• /
• 1999

분산 가상환경(DVE)이 대규모가 되어감에 따라, 고려해야 할 중요한 사항중의 하나는 확장성이다. DVE에 확장성을 지원하기 위한 여러 접근 방법 중의 하나는 region 분할 방법이다. 대부분의 DVE 시스템에서는 region은 분리되어 있으며 이웃한 region의 사용자들 사이의 상호작용이 지원된다. 하지만 사용자들은 항상 자신이 관심을 가지고 있는 이웃 region들 안의 모든 사용자 상태 정보를 알아야 한다. 이것은 region간 상호작용을 하는 사용자들에게 통신에 따른 부하가 많이 걸리게 되며 따라서 시스템 확장성이 떨어지게 된다. 본 연구에서는 region 관리자가 현재 region안의 사용자들에 대한 관심도가 높은 이웃 region안의 사용자들의 일부만 선택한다. 이를 통해 region안의 사용자들은 이웃 region으로부터 모든 갱신 메시지를 받지 않아도 된다. 본 연구를 통해 DVE 시스템은 확장성 있는 region안에서의 상호작용뿐만 아니라 region간 상호작용을 지원할 수 있다.

• Proceedings of the IEEK Conference
• /
• 2001.09a
• /
• pp.139-142
• /
• 2001

This paper presents a highly fast and accurate facial region extraction method by using the skin-color-reference map and motion information. First, we construct the robust skin-color-reference map and eliminate the background in image by this map. Additionally, we use the motion information for accurate and fast detection of facial region in image sequences. Then we further apply region growing in the remaining areas with the aid of proposed criteria. The simulation results show the improvement in execution time and accurate detection.