In this paper, we propose a new algorithm to remove shadow from a single color image. We first project log-log chromaticity coordinates onto an angle of illuminationinvariant direction to obtain an illumination independent grayscale image. Then color information is added back by comparing intensities in the original and illuminationinvariant grayscale images, and taking both global tonality and pixel-wise color into account. Finally, the result color image without shadow is recovered. The proposed method simplifies the color restoration procedure and therefore requires much less computation. Experimental results show that the new method can produce shadow-free images with same quality as those shown in previous work.

In content-based image retrieval, the representation of local properties in an image is one of the most active research issues. This paper introduces a salient region detector based on wavelet transform. The detector can extract the visually meaningful regions on an image and reflect local characteristics. An annular segmentation algorithm based on the distribution of salient regions is designed. It takes not only local image features into account, but also the spatial distribution information of the salient regions. Then, Color moments and the introduced visual perception texture features of the regions around the salient points were computed as a features vector used for indexing the image. We tested the proposed scheme using a wide range image samples from the Corel Image Library, the experimental results indicating that the method has produced promising results.

We introduce a simple texture synthesis method based on Support Vector Machines (SVM). Although SVM has been effectively used for various pattern recognition tasks, there is no report available on directly applying SVM for texture synthesis. The advantage of using SVM is that the sample can be simply modeled by a linear model and is not required during the synthesis stage. In addition, the method can be further extended to synthesize 3D surface texture or Bidirectional Texture Functions. Our experimental results show that the method can successfully model and synthesize semi or highly structured textures, which can be difficult subjects for previous texture synthesis methods based on parametric models.

This paper proposes a hybrid model for deformable template which combines alignable and non-alignable sketches. These sketches are subject to slight or considerable translations in different images. For slight translations, Wu et al [13] proposed active basis model to capture them, where each sketch is allowed to shift in position and orientation. For larger translations of sketches, [13] assumed that they follow the same distribution as sketches of natural image ensembles, which need not be explicitly modeled. But in fact, for a specified object class, the unaligned sketches follow a totally different distribution from those of natural images. We summarize these sketches by their means in the foreground mask. We treat the mean value in each direction as independent features and fit their marginal distributions on object ensemble and natural image ensemble using Gaussian distribution. The marginal distributions are combined with Active Basis into a joint probability ratio to distinguish foreground object from natural background. Experiments are conducted on 14 object classes, most of which show considerable improvement in ROC.

This paper presents a new approach to measure texture directions and estimate illumination tilt angle of 3D surface textures by using mojette transform. Feature vectors are generated from variances of 72 mojette transform projections with different projection angles. The measured texture directions are compared with human perceptual judgement. Furthermore, we estimate illumination tilt angles by minimizing the Euclidean distance of the feature vector between the test image and the training sets. Experimental results show the effectiveness and accuracy of our proposed approach.