Human image understanding: recent research and a theory
Papers from the second workshop Vol. 13 on Human and Machine Vision II
Computer Vision and Image Understanding
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Region-Based Fuzzy Feature Matching Approach to Content-Based Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Unsupervised Segmentation of Color Images Based on k -means Clustering in the Chromaticity Plane
CBAIVL '99 Proceedings of the IEEE Workshop on Content-Based Access of Image and Video Libraries
ICCV '95 Proceedings of the Fifth International Conference on Computer Vision
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Phase based feature detector consistent with human visual system characteristics
Pattern Recognition Letters
A variational formulation for segmenting desired objects in color images
Image and Vision Computing
Retargeting Images and Video for Preserving Information Saliency
IEEE Computer Graphics and Applications
Development of expert system for extraction of the objects of interest
Expert Systems with Applications: An International Journal
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Color-texture image segmentation by combining region and photometric invariant edge information
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Snakes, shapes, and gradient vector flow
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
Segmenting a low-depth-of-field image using morphological filters and region merging
IEEE Transactions on Image Processing
| Hi-index | 12.05 |
Segmenting center of interests (COIs) from pictures is an important but highly challenging problem for researchers in computer vision and image processing. The capability of understanding the meanings of pictures by computers can lead to breakthroughs in a wide range of applications including Web image search and online picture-sharing communities. In this paper, a two-level strategy is presented, which consists of a rough segmentation stage and a fine segmentation stage. In the first level, a picture is partitioned into four regions by using a block clustering method based on color and texture features, and the COI within the picture is distinguished from the background according to the principles of photographic composition. This stage aims to determine the approximate region of the target. In the second level, a novel active contour model is established based on shape information and vector method, where the image energy is defined by a hue gradient and the external energy is generated from either a triangular inner force or a supplementary force. This stage tries to extract the boundary of the target accurately. Experimental results on photos downloaded from the Internet show the feasibility and effectiveness of the proposed method.