• International Journal of Control, Automation, and Systems
• /
• v.4 no.5
• /
• pp.637-644
• /
• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Proceedings of the Korea Contents Association Conference
• /
• 2004.11a
• /
• pp.505-510
• /
• 2004

This paper suggests a new CT-image segmentation algorithm. This algorithm uses morphological filters and the watershed algorithms. The proposed CT-image segmentation algorithm consists of six parts: preprocessing, image simplification, feature extraction, decision making, region merging, and postprocessing. By combining spatial and temporal information, we can get more accurate segmentation results. The simulation results illustrate not only the segmentation results of the conventional scheme but also the results of the proposed scheme; this comparison illustrates the efficacy of the proposed technique. Furthermore, we compare the various medical images of the structuring elements. Indeed, to illustrate the improvement of coding efficiency in postprocessing, we use differential chain coding for the shape coding of results.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 1998.06b
• /
• pp.29-33
• /
• 1998

In this paper, we propose a hierarchical segmentation method for tracking a semantic video object using a watershed algorithm based on morphological filtering. In the proposed method, each hierarchy consists of three steps: First, markers are extracted on the simplified current frame. Second, region growing by a modified watershed algorithm is performed for segmentation. Finally, the segmented regions are classified into 3 categories, i.e., inside, outside, and uncertain regions according to region probability values, which are acquired by the probability map calculated from a estimated motion field. Then, for the remaining uncertain regions, the above three steps are repeated at lower hierarchies with less simplified frames until every region is decided to a certain region. The proposed algorithm provides prospective results in video sequences such as Miss America, Clair, and Akiyo.

• Journal of Korea Game Society
• /
• v.17 no.6
• /
• pp.139-152
• /
• 2017

In this paper, we propose a ground area detection technique based on region segmentation in the spherical image. We modified the Watershed planar image segmentation method to segment spherical images. After regions are segmented, the ground area is detected by comparing colors and textures of pixels of the assumed ground region with the pixels of other regions. The ground detection technique for planar images cannot be used for spherical images due to the spherical distortion. Considering the spherical distortion, we designed the ground shape detection algorithm to detect the ground area in the spherical images. Our experimental results show that the proposed technique properly detects ground areas both for the flat ground and the obstacle-filled ground environments.

• Journal of the Korean Society of Visualization
• /
• v.19 no.1
• /
• pp.28-35
• /
• 2021

In this study, an image processing method for the measurement of two-phase bubbly flow is developed. Shadowgraphy images obtained by high-speed camera are used for analysis. Some bubbles are generated as single unit and others are overlapped or clustered. Single bubbles can be easily analyzed using parameters such as bubble shape, centroid, and area. But overlapped bubbles are difficult to transform clustered bubbles into segmented bubbles. Several approaches were proposed for the bubble segmentation such as Hough transform, connection point method and watershed. These methods are not enough for bubble segmentation. In order to obtain the size distribution of bubbles, we present a method of splitting overlapping bubbles using watershed and approximating them to ellipse. There is only 5% error difference between manual and automatic analysis. Furthermore, the error can be reduced down to 1.2% when a correction factor is used. The ellipse fitting algorithm developed in this study can be used to measure bubble parameters accurately by reflecting the shape of the bubbles.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.40 no.1
• /
• pp.51-62
• /
• 2003

A new video segmentation algorithm for segmentation-based video coding is proposed. The method uses a new criterion based on similarities in both motion and brightness. Brightness and motion information are incorporated in a single segmentation procedure. The actual segmentation is accomplished using a region-growing technique based on the watershed algorithm. In addition, a tracking technique is used in subsequent frames to achieve a coherent segmentation through time. Simulation results show that the proposed method is effective in determining object boundaries not easily found using the statistic criterion alone.

• Proceedings of the KSME Conference
• /
• 2001.06b
• /
• pp.670-675
• /
• 2001

This paper is to propose image segmentation method based on chamfer algorithm. First, we get original image from CCD camera and transform it into gray image. Second, we extract maximum gray value of background and reconstruct and eliminate the background using surface fitting method and bilinear interpolation. Third, we subtract the reconstructed background from gray image to remove noises in gray image. Fourth, we transform the subtracted image into binary image using Otsu's optimal thresholding method. Fifth, we use morphological filters such as areaopen, opening, filling filter etc. to remove noises and isolated points. Sixth, we use chamfer distance or Euclidean distance to this filtered image. Finally, we use watershed algorithm and count microorganisms in image by labeling. To prove the effectiveness, we apply the proposed algorithm to one of Ammonia-oxidizing bacteria, Acinetobacter sp. It is shown that both Euclidean algorithm and chamfer algorithm show over-segmentation. But Chamfer algorithm shows less over-segmentation than Euclidean algorithm.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.43 no.9 s.351
• /
• pp.44-52
• /
• 2006

Recently developed MPEG-4 Part 2 compression standard provides a novel capability to handle arbitrary video objects. To support this capability, an efficient object segmentation technique is required. This paper proposes a real-time algorithm for foreground object segmentation in video sequences. The proposed algorithm consists of two steps: the first step that segments a video frame into multiple sub-regions using Spatio-Temporal Watershed Transform and the second step in which a foreground object segment is extracted from the sub-regions generated in the first step. For real-time processing, the algorithm is partitioned into hardware and software parts so that computationally expensive parts are off-loaded from a processor and executed by hardware accelerators. Simulation results show that the proposed implementation can handle QCIF-size video at 15 fps and extracts an accurate foreground object.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.10
• /
• pp.35-41
• /
• 2012

We propose a new marker driven Watershed algorithm for automated segmentation of clustered cell from microscopy image with less over segmentation. The Watershed Transform is able to segment extremely complex objects which are highly touched and overlapped each other. The success of the Watershed Transform depends essentially on the finding markers for each of the objects of interest. For extracting of markers positioning around center of each cell we used radial symmetry and iterative voting algorithms. With synthetic and real images, we quantitatively demonstrate the performance of our method and achieved better results than the other compared methods.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.5 no.3
• /
• pp.309-314
• /
• 2010

In this paper, an intelligent security camera over internet is proposed. Among ISC methods, watersheds based methods produce a good performance in segmentation accuracy. But traditional watershed transform has been suffered from over-segmentation due to small local minima included in gradient image that is input to the watershed transform. And a zone face candidates of detection using skin-color model. last step, face to check at face of candidate location using SVM method. It is extract of wavelet transform coefficient to the zone face candidated. Therefore, it is likely that it is applicable to read world problem, such as object tracking, surveillance, and human computer interface application etc.