Generalized Hough Transform Using Regions with Homogeneous Color
International Journal of Computer Vision
Unsupervised video object segmentation and tracking based on new edge features
Pattern Recognition Letters
An Efficient Parameterless Quadrilateral-Based Image Segmentation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Region-based image retrieval using an object ontology and relevance feedback
EURASIP Journal on Applied Signal Processing
Morphological preprocessing method to thresholding degraded word images
Pattern Recognition Letters
Image segmentation method using thresholds automatically determined from picture contents
Journal on Image and Video Processing
Selecting an appropriate segmentation method automatically using ANN classifier
IEA/AIE'07 Proceedings of the 20th international conference on Industrial, engineering, and other applications of applied intelligent systems
Intelligent segmentation method for real-time defect inspection system
Computers in Industry
Intelligent object extraction algorithm based on foreground/background classification
EUC'05 Proceedings of the 2005 international conference on Embedded and Ubiquitous Computing
IRED gun: infrared LED tracking system for game interface
PCM'05 Proceedings of the 6th Pacific-Rim conference on Advances in Multimedia Information Processing - Volume Part II
Fast granular analysis based on watershed in microscopic mineral images
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
3-D face modeling from two views and grid light
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Fast training of SVM via morphological clustering for color image segmentation
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
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Mathematical morphology is very attractive for automatic image segmentation because it efficiently deals with geometrical descriptions such as size, area, shape, or connectivity that can be considered as segmentation-oriented features. This paper presents an image-segmentation system based on some well-known strategies. The segmentation process is divided into three basic steps, namely: simplification, marker extraction, and boundary decision. Simplification, which makes use of area morphology, removes unnecessary information from the image to make it easy to segment. Marker extraction identifies the presence of homogeneous regions. A new marker extraction design is proposed in this paper. It is based on both luminance and color information. The goal of boundary decision is to precisely locate the boundary of regions detected by the marker extraction. This decision is based on a region-growing algorithm which is a modified watershed algorithm. A new color distance is also defined for this algorithm. In both marker extraction and boundary decision, color measurement is used to replace grayscale measurement and L*a*b* color space is used to replace the more straightforward spaces such as the RGB color space and YUV color space