• Journal of Korea Multimedia Society
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• v.9 no.5
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• pp.529-541
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• 2006

In this paper, we propose a semi-automatic segmentation method which can be used to generate video object plane (VOP) for object based coding scheme and multimedia authoring environment. Semi-automatic segmentation can be considered as a user-assisted segmentation technique. A user can initially mark objects of interest around the object boundaries and then the selected objects are continuously separated from the un selected areas through time evolution in the image sequences. The proposed segmentation method consists of two processing steps: partially manual intra-frame segmentation and fully automatic inter-frame segmentation. The intra-frame segmentation incorporates user-assistance to define the meaningful complete visual object of interest to be segmentation and decides precise object boundary. The inter-frame segmentation involves boundary and region tracking to obtain temporal coherence of moving object based on the object boundary information of previous frame. The proposed method shows stable and efficient results that could be suitable for many digital video applications such as multimedia contents authoring, content based coding and indexing. Based on this result, we have developed objects based video editing system with several convenient editing functions.

• International Journal of Contents
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• v.9 no.1
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• pp.6-10
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• 2013

Foreground estimation in object segmentation has been an important issue for last few decades. In this paper we propose a GrabCut based automatic foreground estimation method using block clustering. GrabCut is one of popular algorithms for image segmentation in 2D image. However GrabCut is semi-automatic algorithm. So it requires the user input a rough boundary for foreground and background. Typically, the user draws a rectangle around the object of interest manually. The goal of proposed method is to generate an initial rectangle automatically. In order to create initial rectangle, we use Gabor filter and Saliency map and then we use 4 features (amount of area, variance, amount of class with boundary area, amount of class with saliency map) to categorize foreground and background. From the experimental results, our proposed algorithm can achieve satisfactory accuracy in object segmentation without any prior information by the user.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.3
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• pp.125-132
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• 2012

This paper proposes an automatic object segmentation and background composition method for video communication over consumer mobile phones. The object regions were extracted based on the motion and color variance of the first two frames. To combine the motion and variance information, the Euclidean distance between the motion boundary pixel and the neighboring color variance edge pixels was calculated, and the nearest edge pixel was labeled to the object boundary. The labeling results were refined using the morphology for a more accurate and natural-looking boundary. The grow-cut segmentation algorithm begins in the expanded label map, where the inner and outer boundary belongs to the foreground and background, respectively. The segmented object region and a new background image stored a priori in the mobile phone was then composed. In the background composition process, the background motion was measured using the optical-flow, and the final result was synthesized by accurately locating the object region according to the motion information. This study can be considered an extended, improved version of the existing background composition algorithm by considering motion information in a video. The proposed segmentation algorithm reduces the computational complexity significantly by choosing the minimum resolution at each segmentation step. The experimental results showed that the proposed algorithm can generate a fast, accurate and natural-looking background composition.

• Proceedings of the IEEK Conference
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• 2000.07a
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• pp.429-432
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• 2000

The new MPEG-4 video coding standard enables content-based functionalities. In order to support the philosophy of the MPEG-4 visual standard, each frame of video sequences should be represented in terms of video object planes (VOP’s). In other words, video objects to be encoded in still pictures or video sequences should be prepared before the encoding process starts. Therefore, it requires a prior decomposition of sequences into VOP’s so that each VOP represents a moving object. A parallel processing system is required an automatic segmentation to be processed in real-time, because an automatic segmentation is time consuming. This paper addresses the parallel processing: system for an automatic segmentation for separating moving object from the background in image sequences. The proposed parallel processing system comprises of processing elements (PE’s) and a multi-access memory system (MAMS). Multi-access memory system is a memory controller to perform parallel memory access with the variety of types: horizontal, vertical, and block access way. In order to realize these ways, a multi-access memory system consists of a memory module selection module, data routing modules, and an address calculation and routing module. The proposed system is simulated and evaluated by the CADENCE Verilog-XL hardware simulation package.

• Journal of KIISE:Computer Systems and Theory
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• v.31 no.5_6
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• pp.305-320
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• 2004

Moving object segmentation is a fundamental function for content-based application. Moving object edges are produced by matching the detected moving edges with the current frame edges. But we can often experience the object edge disconnectedness due to coincidence of similarity between the object and background colors or the decrease of movement of moving object. The edge disconnectedness is a serious problem because it degrades the object visual quality so conspicuously That it sometimes makes it inadequate to perform content-based coding. We have solved this problem by developing a robust and comprehensive edge linking algorithm. And we also developed an automatic moving object segmentation algorithm. These algorithms can produce the completely linked moving object edge boundary and the accurate moving object segmentation. These algorithms can process CIF 30 frames/sec in a PC. These algorithms can be used for the MPEG-4 content-based coding.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.17-31
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• 2002

The new MPEG-4 video coding standard enables content-based functionalities. Image segmentation is an indispensable process for it. This paper addresses an effective segmentation of non-rigid objects. Non-rigid objects are deformable objects with fuzzy, blurred and indefinite boundaries. So it is difficult to segment deformable objects precisely. In order to solve this problem, we propose an effective segmentation of non-rigid objects using watershed algorithms in still pictures. And we propose an automatic segmentation through intra-frame and inter-frame segmentation process in video sequences. Automatic segmentation preforms boundary-based and region-based segmentation to extract precise object boundaries.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1999.06a
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• pp.165.1-170
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• 1999

Object segmentation fro natural video scenes has recently become one of very active research to pics due to the object-based video coding standard MPEG-4. Object detection and isolation is also useful for object-based indexing and search of video content, which is a goal of the emerging new standard, MPEG-7. In this paper, an automatic segmentation method of moving objects in image sequence is presented which is applicable to multimedia content authoring for MPEG-4, and two different segmentation approaches suitable for surveillance applications are addressed in raw data domain and compressed bitstream domains. We also propose an object-based video description scheme based on object segmentation for video indexing purposes.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.283-289
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• 2006

The present image segmentation is what user want to segment image and has been studied for technology in composition of segment object with other images. In this paper, we propose a method of novel semi-automatic image segmentation using gradual region merging and genetic algorithm. Proposed algorithm is edge detection of object using genetic algorithm after selecting object which user want. We segment region of object which user want to based on detection edge using watershed algorithm. We separated background and object in indefinite region using gradual region merge from Segment object. And, we have applicable value which user want by making interface based on GUI for efficient perform of algorithm development. In the experiments, we analyzed various images for proving superiority of the proposed method.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2010-2014
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• 2005

Object detection and parameter estimation in point cloud data is a relevant subject to robotics, reverse engineering, computer vision, and sport mechanics. In this paper a software is presented for fully-automatic object detection and parameter estimation in unordered, incomplete and error-contaminated point cloud with a large number of data points. The software consists of three algorithmic modules each for object identification, point segmentation, and model fitting. The newly developed algorithms for orthogonal distance fitting (ODF) play a fundamental role in each of the three modules. The ODF algorithms estimate the model parameters by minimizing the square sum of the shortest distances between the model feature and the measurement points. Curvature analysis of the local quadric surfaces fitted to small patches of point cloud provides the necessary seed information for automatic model selection, point segmentation, and model fitting. The performance of the software on a variety of point cloud data will be demonstrated live.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.204-206
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• 2003

In order to utilize remote sensed images effectively, a lot of image classification methods are suggested for many years. But, the accuracy of traditional methods based on pixel-based classification is not high in general. In this study, object oriented classification based on image segmentation is used to classify Landsat images. A necessary prerequisite for object oriented image classification is successful image segmentation. Object oriented image classification, which is based on fuzzy logic, allows the integration of a broad spectrum of different object features, such as spectral values , shape and texture. Landsat images are divided into urban, agriculture, forest, grassland, wetland, barren and water in sochon-gun, Chungcheongnam-do using object oriented classification algorithms in this paper. Preliminary results will help to perform an automatic image classification in the future.