• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.4
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• pp.806-816
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• 2009

Character recognition system consists of four step; text localization, text segmentation, character segmentation, and recognition. The character segmentation is very important and difficult because of noise, illumination, and so on. For high recognition rates of the system, it is necessary to take good performance of character segmentation algorithm. Many algorithms for character segmentation have been developed up to now, and many people have been recently making researches in segmentation of touching or overlapping character. Most of algorithms cannot apply to the text regions of management number marked on the slab in steel image, because the text regions are irregular such as touching character by strong illumination and by trouble of nozzle in marking machine, and loss of character. It is difficult to gain high success rate in various cases. This paper describes a new algorithm of character segmentation to recognize slab management number marked on the slab in the steel image. It is very important that pre-processing step is to convert gray image to binary image without loss of character and touching character. In this binary image, non-touching characters are simply separated by using vertical projection profile. For separating touching characters, after we use combined profile to find candidate points of boundary, decide real character boundary by using method based on recognition. In recognition step, we remove noise of character images, then recognize respective character images. In this paper, the proposed algorithm is effective for character segmentation and recognition of various text regions on the slab in steel image.

• Proceedings of the Korea Information Processing Society Conference
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• 2004.11a
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• pp.891-894
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• 2004

This paper proposes an improved image segmentation algorithm by the watershed algorithm based on the local adaptive threshold on local minima search and the fixing threshold on label allocation. The previous watershed algorithm generates the problem of over-segmentation. The over-segmentation makes the boundary in the inaccuracy region by occurring around the object. In order to solve those problems we quantize the input color image by the vector quantization, remove noise and find the gradient image. We sorted local minima applying the local adaptive threshold on local minima search of the input color image. The simulation results show that the proposed algorithm controls over-segmentation and makes the fine boundary around segmented region applying the fixing threshold based on sorted local minima on label allocation.

• Journal of Auto-vehicle Safety Association
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• v.14 no.2
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• pp.51-56
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• 2022

This paper presents an Ground Segmentation algorithm to eliminate unnecessary Lidar Point Cloud Data (PCD) in an autonomous driving system. We consider Random Sample Consensus (Ransac) Algorithm to process lidar ground data. Ransac designates inlier and outlier to erase ground point cloud and classified PCD into two parts. Test results show removal of PCD from ground area by distinguishing inlier and outlier. The paper validates ground rejection algorithm in real time calculating the number of objects recognized by ground data compared to lidar raw data and ground segmented data based on the z-axis. Ground Segmentation is simulated by Robot Operating System (ROS) and an analysis of autonomous driving data is constructed by Matlab. The proposed algorithm can enhance performance of autonomous driving as misrecognizing circumstances are reduced.

• Proceedings of the IEEK Conference
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• 2003.11a
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• pp.39-42
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• 2003

In this paper, we propose a background segmentation method in low resolution color image. A segmentation algorithm is based on color and edge information. In edge image, adaptive and local thresholds are applied to suppress paint boundaries. Through our experiments, the proposed algorithm efficiently segments background from objects.

• Proceedings of the IEEK Conference
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• 1999.06a
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• pp.619-622
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• 1999

This paper describes a new semi-automatic segmentation algorithm based on color information. Semi-automatic segmentation mainly consists of intra-frame segmentation and inter-frame segmentation. While intra-frame segmentation extracts video objects of interest from boundary information provided by the user and intensity information of the image, inter-frame segmentation partitions the image into the video objects and background by tracking the motion of video objects. For inter-frame segmentation, color information (Y, Cb and Cr) of the current frame can be used efficiently in order to find the exact boundary of the video objects. In this paper we propose a new region growing algorithm which can maximize the ability of region differentiation, while preserving features of each color component.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.709-721
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• 2009

This paper discusses an efficient image segmentation using morphological watershed algorithm that is robust to noise. Morphological image segmentation consists of four steps: image simplification, computation of gradient image and watershed algorithm and region merging. Conventional watershed segmentation exhibits a serious weakness for over-segmentation of images. In this paper we present a morphological edge detection methods for detecting edges under noisy condition and apply our watershed algorithm to the resulting gradient images and merge regions using Kolmogorov-Smirnov test for eliminating irrelevant regions in the resulting segmented images. Experimental results are analyzed in both qualitative analysis through visual inspection and quantitative analysis with percentage error as well as computational time needed to segment images. The proposed algorithm can efficiently improve segmentation accuracy and significantly reduce the speed of computational time.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.368-374
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• 2010

Image segmentation is the basic step in the field of the image processing for pattern recognition, environment recognition, and context analysis. The Otsu's automatic threshold selection, which determines the optimal threshold value to maximize the between class scatter using the distribution information of the normalized histogram of a image, is the famous method among the various image segmentation methods. For the automatic threshold selection proposed by Otsu, it is difficult to determine the optimal threshold value by considering the sub-region characteristic of the image because the Otsu's algorithm analyzes the global histogram of a image. In this paper, to alleviate this difficulty of Otsu's image segmentation algorithm and to improve image segmentation capability, the original image is divided into several sub-images by using context fuzzy c-means algorithm. The proposed fuzzy Otsu threshold algorithm is applied to the divided sub-images and the several threshold values are obtained.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.211-230
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• 2022

In image segmentation, for the condition that objects (targets) and background in an image are intertwined or their common boundaries are vague as well as their textures are similar, and the targets in images are greatly variable, the deep learning might be difficult to use. Hence, a new method based on graph theory and guided feathering is proposed. First, it uses a guided feathering algorithm to initially separate the objects from background roughly, then, the image is separated into two different images: foreground image and background image, subsequently, the two images are segmented accurately by using the improved graph-based algorithm respectively, and finally, the two segmented images are merged together as the final segmentation result. For the graph-based new algorithm, it is improved based on MST in three main aspects: (1) the differences between the functions of intra-regional and inter-regional; (2) the function of edge weight; and (3) re-merge mechanism after segmentation in graph mapping. Compared to the traditional algorithms such as region merging, ordinary MST and thresholding, the studied algorithm has the better segmentation accuracy and effect, therefore it has the significant superiority.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1995.06a
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• pp.143-148
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• 1995

In this paper, we describe a fast 3D video segmentation method using mathematical morphology. The proposed 3D video segmentation algorithm is composed of intra-frame segmentation step and inter-frame segmentation step. In the intra-frame segmentation step, the first frame is segmented using the fast hierarchical segmentation method. Then, in the inter-frame segmentation step, the next frames are segmented using markers that are extracted from the difference of previous segmentation result and simplified present image. Experimental results show that the proposed method has more fast structure and is suitable for video segmentation.