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基于数字栅格地图的数据特性,运用小波变换工具,结合人类视觉系统特征,提出一种自适应的数字栅格地图可见水印算法。首先,对可见水印信息进行扩展等预处理;然后通过计算数字栅格地图的视觉重要区域来选择水印嵌入位置,水印嵌入位置由人类视觉系统特征和地图数据特性共同决定;最后,将可见水印自适应地嵌入地图所选择嵌入区域的小波域中。对提出的水印算法进行了试验分析,结果表明,该算法不仅具有良好的抗差性,同时还较好地保持了可见水印和原地图的视觉特征,以一种更积极有效的方式保护了数字栅格地图的版权。

To enhance color images more effectively, a novel strategy is presented in this paper. We firstly translate the image to be enhanced from RGB space into HIS space, secondly keep its H component unchanged, and thirdly stretch its S component exponentially, and at last process its I component in the following manner： couple both the gray value and the spatial information into an inner activity item of corresponding neuron, integrate the human visual system into a dynamic component of corresponding neuron, and compare the inner activity item with dynamic component to obtain the enhanced image. Experiments demonstrate the effectiveness and validity of our strategy.

Object recognition, which consists of classification and detection, has two important attributes for robustness： 1） closeness： detection windows should be as close to object locations as possible, and 2） adaptiveness： object matching should be adaptive to object variations within an object class. It is difficult to satisfy both attributes using traditional methods which consider classification and detection separately; thus recent studies propose to combine them based on confidence contextualization and foreground modeling. However, these combinations neglect feature saliency and object structure, and biological evidence suggests that the feature saliency and object structure can be important in guiding the recognition from low level to high level. In fact, object recognition originates in the mechanism of ＂what＂ and ＂where＂ pathways in human visual systems. More importantly, these pathways have feedback to each other and exchange useful information, which may improve closeness and adaptiveness. Inspired by the visual feedback, we propose a robust object recognition framework by designing a computational visual feedback model （VFM） between classification and detection. In the ＂what＂ feedback, the feature saliency from classification is exploited to rectify detection windows for better closeness; while in the ＂where＂ feedback, object parts from detection are used to match object structure for better adaptiveness. Experimental results show that the ＂what＂ and ＂where＂ feedback is effective to improve closeness and adaptiveness for object recognition, and encouraging improvements are obtained on the challenging PASCAL VOC 2007 dataset.

A shadow detection method using pulse couple neural network inspired by the characters of human visual system is proposed. More precisely, lateral inhibition of human vision and coefficient of variation are combined together to improve the pulse couple neural network. Shadow detection is considered to be a shadow region segmentation problem. Experiment shows that the presented method is consistent with human vision compared to shadow detection methods based on HSV and pulse couple neural network (PCNN) by both subjective and objective assessments.

In this paper, a saliency weighted visual feature similarity (SWVFS) metric is proposed for full reference image quality assessment (IQA). Instead of traditional spatial pooling strategies, a visual saliency-based approach is employed for better compliance with properties of the human visual system, where the saliency allocation is closely related to the activity of posterior parietal cortex and the pluvial nuclei of the thalamus. Assuming that the saliency map actually represents the contribution of locally computed visual distortions to the overall image quality, the gradient similarity and the textural congruency are merged into the final image quality indicator. The gradient and texture comparison play complementary roles in characterizing the local image distortion. Extensive experiments conducted on seven publicly available image databases show that the performance of SWVFS is competitive with the state-of-the-art IQA algorithms.

Traditional secret sharing （SS） schemes can reconstruct the secret precisely,but have high com-putation complexity.Visual secret sharing （VSS） schemes use human visual system to reconstruct the secret without cryptographic computation,but have pixel expansion and loss of contrast.Wang et al.proposed a （2,n）-SS scheme for binary images based on Boolean operation,which has low computation complexity,no pixel expansion and the contrast is 1/2.In this paper,we first construct an r runs （2,n）-SS scheme to improve the contrast of Wang et al.＇s binary （2,n）-SS scheme.Then we present two approaches to construct r runs （2,n）-SS schemes for grayscale image and color image.The two approaches are both based on Boolean operation,while one approach uses halftone technology and the other uses bit level processing.These proposed schemes have low computation complexity and almost ideal contrast.

We propose a complete parameterization presentation of the 3-channel bivariate non-separable orthogonal FIR filter, and describe the sufficient condition of generating continuous wavelet bases. Given the results above, a non-separable, compactly supported, orthogonal, continuous parameterized bivariate wavelet bank is set up here.