• 한국정보처리학회논문지
• /
• 제7권3호
• /
• pp.1013-1020
• /
• 2000

In this paper, we propose an algorithm which segments hierarchically blocks of image using discrete cosine transform(DCT) and execute finite-state vector quantization (FSVQ) for each block. Using DCT coefficient feature, image is segmented hierarchically to large smooth block and small edge block, then the block hierarchy informations are transmitted. The codebooks are respectively constructed for each hierarchical blocks, the encoder transmits codeword index using FSVQ for reducing encoded bit with hierarchical segmentation. Compared with side match VQ(SMVQ) and hierarchical FSVQ(HFSVQ) algorithm, about Zelda and Boat image, the new algorithm shows better picture quality with 1.97dB and 2.85 dB difference as to SMVQ, 1.78dB and 1.85dB diffences as to HFSVQ respectively.

• 전자공학회논문지B
• /
• 제33B권4호
• /
• pp.111-123
• /
• 1996

In this paper, a new edge-based segmentation algorithm for range image using pseudo reflectance images (PRIs) is proposed. A model of pseudo reflectance which is useful in analyzing three dimensional scene and objects is introduced and then three PRIs are generated by the model. For generating three PRIs, bels and jain's differential window operator is selected and three different light source directions are determined. Three edge images are extracted from each PRI and a fused (logical ORing) edge image is constructed for the benefit of enhanced edge formation. The final segmentation results of the proposed algoritm are obtained after the processing of thinning, labeling and correcting erroeneous regions with the fused edge image. The good performance of edge detection and segmentation is confirmed via computer simulation with synthetic and real range images.

• 응용통계연구
• /
• 제24권1호
• /
• pp.177-183
• /
• 2011

Hidden Markov random eld(HMRF) is one of the most common model for image segmentation which is an important preprocessing in many imaging devices. The HMRF has unknown hyper-parameters on Markov random field to be estimated in segmenting testing images. However, in practice, due to computational complexity, it is often assumed to be a fixed constant. In this paper, we numerically show that the segmentation results very depending on the fixed hyper-parameter, and, if the parameter is misspecified, they further depend on the choice of the class-labelling algorithm. In contrast, the HMRF with estimated hyper-parameter provides consistent segmentation results regardless of the choice of class labelling and the estimation method. Thus, we recommend practitioners estimate the hyper-parameter even though it is computationally complex.

• 한국컴퓨터산업학회논문지
• /
• 제4권12호
• /
• pp.923-928
• /
• 2003

잡음의 혼입, 추가적인 데이터의 삽입에 의해 많은 catchments basins 이 보다 더 부분 분할되어 local minima 가 증가하는 경우 훼손된 Watershed 영상은 관련된 객체 윤곽들이 소실되는 over segmentation 이 발생한다 제안된 Watershed 영상 분할 알고리즘은 반복적으로 유사 gray level 분포를 지니는 인접 영역을 병합, 영상 복원하여 기존의 알고리즘보다 우수한 효과를 나타낼 것으로 기대된다.

• 대한의용생체공학회:학술대회논문집
• /
• 대한의용생체공학회 1997년도 춘계학술대회
• /
• pp.95-100
• /
• 1997

In this paper, we propose a coding method to improve compression efficiency for MR image. This can be achieved by combining coding and segmentation scheme which removes noisy background region, which is meaningless for diagnosis, in MR image. The wavelet coder encodes only diagnostically significant foreground regions refering to segmentation map. Our proposed algorithm provides about 15% of bitrate reduction when compared with the same coder which is not combined with segmentation scheme. And the proposed scheme shows better reconstructed image Qualify than JPEG at the same compression ratio.

• 대한전자공학회:학술대회논문집
• /
• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
• /
• pp.231-233
• /
• 2009

The object recognition is one of the major computer vision fields. The object recognition using features(SIFT) is finding common features in input images and query images. But the object recognition using feature methods has suffered of difficulties due to heavy calculations when resizing input images and query images. In this paper, we focused on speed up finding features in the images. we proposed method using block segmentation and histogram. Block segmentation used diving input image and than histogram decided correlation between each 1]lock and query image. This paper has confirmed that tile matching time reduced for object recognition since reducing block.

• Journal of Information Processing Systems
• /
• 제12권4호
• /
• pp.661-680
• /
• 2016

The vehicle license plate recognition (VLPR) system analyzes and monitors the speed of vehicles, theft of vehicles, the violation of traffic rules, illegal parking, etc., on the motorway. The VLPR consists of three major parts: license plate detection (LPD), license plate character segmentation (LPCS), and license plate character recognition (LPCR). This paper presents an efficient method for the LPCS and LPCR of Korean vehicle license plates (LPs). LP tilt adjustment is a very important process in LPCS. Radon transformation is used to correct the tilt adjustment of LP. The global threshold segmentation method is used for segmented LP characters from two different types of Korean LPs, which are a single row LP (SRLP) and double row LP (DRLP). The cross-correlation matching method is used for LPCR. Our experimental results show that the proposed methods for LPCS and LPCR can be easily implemented, and they achieved 99.35% and 99.85% segmentation and recognition accuracy rates, respectively for Korean LPs.

• 대한의용생체공학회:의공학회지
• /
• 제29권5호
• /
• pp.348-354
• /
• 2008

Segmenting vessels in lower extremity CT images is very difficult because of gray level variation, connection to bones, and their small sizes. Instead of segmenting vessels, we propose an approach that segments bones and subtracts them from the original CT images. The subtracted images can contain not only connected vessel structures but also isolated vessels, which are very difficult to detect using conventional vessel segmentation methods. The proposed method initially grows a 3-dimensional (3D) volume with a seeded region growing (SRG) using an adaptive threshold and then detects junctions and forked branches. The forked branches are classified into either bone branches or vessel branches based on appearance, shape, size change, and moving velocity of the branch. The final volume is re-grown by collecting connected bone branches. The algorithm has produced promising results for segmenting bone structures in several tens of vessel-enhanced CT image data sets of lower extremities.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제12권9호
• /
• pp.4375-4388
• /
• 2018

To solve the problem of transmission errors in stereoscopic images, this paper proposes a novel error concealment (EC) method using superpixel segmentation and adaptive disparity selection (SSADS). Our algorithm consists of two steps. The first step is disparity estimation for each pixel in a reference image. In this step, the numbers of superpixel segmentation labels of stereoscopic images are used as a new constraint for disparity matching to reduce the effect of mismatching. The second step is disparity selection for a lost block. In this step, a strategy based on boundary smoothness is proposed to adaptively select the optimal disparity which is used for error concealment. Experimental results demonstrate that compared with other methods, the proposed method has significant advantages in both objective and subjective quality assessment.

• 대한의용생체공학회:의공학회지
• /
• 제21권2호
• /
• pp.145-156
• /
• 2000

본 논문에서는 중앙시상 두뇌 자기공명영상 분할결과를 이용한 3차원 시상 두뇌 자기공명영상의 자동분할기법을 제안한다. 제안된 알고리즘에서는 먼저 3차원 시상 두뇌 자기공명영상의 중앙영상을 분할하고, 분할된 중앙두뇌 자기공명영상을 인접하는 영상에 마스크로 적용한다. 이 때 마스크 적용으로 인하여 인접하는 영상이 절단되는 문제가 발생할 수 있다. 이러한 문제를 해결하기 위하여 절단 영역의 경계점을 검출한 후, 절단 영역에 대한 경로 재설정을 통해 절단 영역을 복원한다. 이러한 경로 재설정을 위해 connectivity-based threshold segmentation algorithm을 사용하였다. 실험결과 제안된 알고리즘의 유용성을 확인할 수 있었다.