Watershed merging method for color images

Watershed transformation can be applied to color as well as to gray-scale images. A problem arises when dealing with color images. It is caused by the fact that pixels in such images are vectors that describe all color components whereas the watershed transformation requires a scalar height function as its input. There are multiple gradient magnitude definitions for color images that allow for the needed conversion. As in the case of gray-scale images, the image after watershed transformation is heavily over-segmented. One can blur the image before calculating the gradient magnitude, threshold the gradient image or merge the resulting watersheds. Unfortunately, the result is still over-segmented.A solution presented in this paper complements those mentioned above. It uses hierarchical cluster analysis methods for joining similar classes of the over-segmented image into a given number of clusters. After the image has been preprocessed and segmented, the over-segmentation is reduced by means of the cluster analysis. The attribute values for each watershed in each color component are calculated and clustering is performed. The resulting similarity hierarchy allows for the simple selection of the number of clusters in the final segmentation.Several clustering methods, including complete linkage and Ward's methods with different sets of components, have been tested. Selected results are presented

Watershed merging method for color images

Watershed transformation can be applied to color as well as to gray-scale images. A problem arises when dealing with color images. It is caused by the fact that pixels in such images are vectors that describe all color components whereas the watershed transformation requires a scalar height function as its input. There are multiple gradient magnitude definitions for color images that allow for the needed conversion. As in the case of gray-scale images, the image after watershed transformation is heavily over-segmented. One can blur the image before calculating the gradient magnitude, threshold the gradient image or merge the resulting watersheds. Unfortunately, the result is still over-segmented.A solution presented in this paper complements those mentioned above. It uses hierarchical cluster analysis methods for joining similar classes of the over-segmented image into a given number of clusters. After the image has been preprocessed and segmented, the over-segmentation is reduced by means of the cluster analysis. The attribute values for each watershed in each color component are calculated and clustering is performed. The resulting similarity hierarchy allows for the simple selection of the number of clusters in the final segmentation.Several clustering methods, including complete linkage and Ward's methods with different sets of components, have been tested. Selected results are presented

Visual-hint Boundary to Segment Algorithm for Image Segmentation

Image segmentation has been a very active research topic in image analysis area. Currently, most of the image segmentation algorithms are designed based on the idea that images are partitioned into a set of regions preserving homogeneous intra-regions and inhomogeneous inter-regions. However, human visual intuition does not always follow this pattern. A new image segmentation method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is more consistent with human perceptions. VHBS abides by two visual hint rules based on human perceptions: (i) the global scale boundaries tend to be the real boundaries of the objects; (ii) two adjacent regions with quite different colors or textures tend to result in the real boundaries between them. It has been demonstrated by experiments that, compared with traditional image segmentation method, VHBS has better performance and also preserves higher computational efficiency.Comment: 45 page

Application of the Ring Theory in the Segmentation of Digital Images

Ring theory is one of the branches of the abstract algebra that has been broadly used in images. However, ring theory has not been very related with image segmentation. In this paper, we propose a new index of similarity among images using Zn rings and the entropy function. This new index was applied as a new stopping criterion to the Mean Shift Iterative Algorithm with the goal to reach a better segmentation. An analysis on the performance of the algorithm with this new stopping criterion is carried out. The obtained results proved that the new index is a suitable tool to compare images.Comment: Very interesting new index to compute the similarity among images. arXiv admin note: substantial text overlap with arXiv:1306.262