A note on the gradient of a multi-image
Computer Vision, Graphics, and Image Processing - Lectures notes in computer science, Vol. 201 (G. Goos and J. Hartmanis, Eds.)
An improved seeded region growing algorithm
Pattern Recognition Letters
Color image processing and applications
Color image processing and applications
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
An Efficient Parameterless Quadrilateral-Based Image Segmentation Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Region growing: a new approach
IEEE Transactions on Image Processing
Automatic image segmentation by integrating color-edge extraction and seeded region growing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Combined morphological-spectral unsupervised image segmentation
IEEE Transactions on Image Processing
Classification-Driven Watershed Segmentation
IEEE Transactions on Image Processing
Object extraction from T2 weighted brain MR image using histogram based gradient calculation
Pattern Recognition Letters
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A method for effective segmentation of small objects in color images is presented. It can be used jointly with region growing algorithms. Segmentation of small objects in color images is a difficult problem because their boundaries are close to each other. The proposed algorithm accurately determines the location of the boundary points of closely located small objects and finds the skeletons (seed regions) of those objects. The method makes use of conditions obtained by analyzing the change of color characteristics of the edge pixels along the direction that is orthogonal to the boundaries of adjacent objects. These conditions are generalized for the case of the well-known class of color images having misregistration artifacts. If high-quality seed regions are available, the final segmentation can be performed using one of the region growing methods. The segmentation algorithm based on the proposed method was tested using a large number of color images, and it proved to be very efficient.