The two issues of art image creation and data hiding are integrated into one and solved by a single approach in this study. An automatic method for generating a new type of computer art, called stained glass image, which imitates the stained-glass window picture, is proposed. The method is based on the use of a tree structure for region growing to construct the art image. Also proposed is a data hiding method which utilizes a general feature of the tree structure, namely, number of tree nodes, to encode the data to be embedded. The method can be modified for uses in three information protection applications, namely, covert communication, watermarking, and image authentication. Besides the artistic stego-image content which may distract the hacker's attention to the hidden data, data security is also considered by randomizing both the input data and the seed locations for region growing, yielding a stego-image which is robust against the hacker's attacks. Good experimental results proving the feasibility of the proposed methods are also included.

In this paper we propose a novel method to inpaint highlights and to remove the specularity in the image with specular objects by the color line projection. Color line projection is the method that a color with a surface reflection component is projected near the diffuse color line by following the direction of the specular color line. We use two captured images using different exposure time so that the clue of the original color in a highlight area is searched from two images since the color at the highlight region is distorted and saturated to the illumination color. In the first step of the proposed procedure, the region corresponding to the highlight is generated and the clue of the original highlight color is acquired. In the next step, the color line is generated by the restricted region growing method around the highlight region, and the color line is divided into the diffuse color line and the specular color line. In the final step, pixels near the specular color line are projected onto near the diffuse color line by the color line projection, in which the modified random function is applied to realistically inpaint the highlight. One of advantages in our method is to find the highlight region and the clue of the original color of the highlight with ease. It also efficiently estimates the surface reflection component which is utilized to remove specularity and to inpaint the highlight. The proposed method performs the highlight inpainting and the specular removal simultaneously once the color line is generated. In addition, color line projection with the modified random function can make the result more realistic. We show experimental results from the real images and make a synthesis of the real image and the image modified by the proposed method.

A novel approach for segmentation of grayscale images, which are color scene originally, is proposed. Many algorithms have been elaborated for a grayscale image segmentation. All those approaches have been discussed in a luminance space, because it has been considered that grayscale images do not have any color information. However, a luminance value has color information as a set of corresponding colors. In this paper, an inverse mapping of luminance values is carried out to CIELAB color space, and the image segmentation for grayscale images is performed based on a distance in the color space. The proposed scheme is applied to a region growing segmentation and the performance is verified.

This investigation proposes a fast wavelet-based color segmentation (FWCS) technique and a modified directional region-growing (DRG) technique for semantic image segmentation. The FWCS is a subsequent combination of progressive color truncation and histogram-based color extraction processes for segmenting color regions in images. By exploring specialized centroids of segmented fragments as initial growing seeds, the proposed DRG operates a directional 1-D region growing on pairs of color segmented regions based on those centroids. When the two examined regions are positively confirmed by DRG, the proposed framework subsequently computes the texture features extracted from these two regions to further check their relation using texture similarity testing (TST). If any pair of regions passes double checking with both DRG and TST, they are identified as associated regions. If two associated regions/areas are connective, they are unified to a union area enclosed by a single contour. On the contrary, the proposed framework merely acknowledges a linking relation between those associated regions/areas highlighted with any linking mark. Particularly, by the systematic integration of all proposed processes, the critical issue to decide the ending level of wavelet decomposition in various images can be efficiently solved in FWCS by a quasi-linear high-frequency analysis model newly proposed. The simulations conducted here demonstrate that the proposed segmentation framework can achieve a quasi-semantic segmentation without priori a high-level knowledge.

A new method to extract retinal blood vessels from a colour fundus image is described. Digital colour fundus images are contrast enhanced in order to obtain sharp edges. The green bands are selected and transformed to correlation coefficient images by using two sets of Gaussian kernel patches of distinct scales of resolution. Blood vessels are then extracted by means of a new algorithm, directional recursive region growing segmentation or D-RRGS. The segmentation results have been compared with clinically-generated ground truth and evaluated in terms of sensitivity and specificity. The results are encouraging and will be used for further application such as blood vessel diameter measurement.

The segmentation of images into regions that have some common properties is a fundamental problem in low level computer vision. In this paper, the region growing method to segmentation is studied. In the study, a coarse to fine processing strategy is adopted to identify the homogeneity of the subregion of an image. The pixels in the image are checked by a nested triple-layer neighborhood system based hypothesis test. The pixels can then be classified into single pixels or grain pixels with different size and coarseness. Instead of using the global threshold to the region growing, local thresholds are determined adaptively for each pixel in the image. The strength of the proposed method lies in the fact that the thresholds are computed automatically. Experiments for synthetic and natural images show the efficiency of our method.