In this paper, we present a novel multimodal emotion recognition technique that automatically learns decision-making parameters customized for each modality. Specifically, the process of decision-making is implemented in a multi-stage and collaborative fashion: Given a classifier for single modality, the classifier is regarded as a virtual expert since classification methods can make emotion recognition in accordance with certain expertise. Then, in the reputation equalization, the expert's classification capability is then quantitatively equalized to assure the reputation and/or confidence for each expert. To compromise decisions among experts, the final decision is obtained by calculating the weighted-sum of all the equalized reputation quantities, in such a way that the decision of one expert can be made in collaboration with that of the others. Moreover, to learn the proposed model parameters, the genetic algorithm is tailored and applied to alleviate the local minima problem during the process of finding an optimal solution. The experimental results have shown that the proposed collaborative decision-making model is effective in multimodal emotion recognition.

The process of compressing the high luminance values into the displayable range inevitably incurs the loss of image contrasts. Although the local adaptation process, such as the two-scale contrast reduction scheme, is capable of preserving details during the HDR compression process, it cannot be used to enhance the local contrasts of image contents. Moreover, the effect of noise artifacts cannot be eliminated when the detail manipulation is subsequently performed. We propose a new tone reproduction scheme, which incorporates the local contrast enhancement and the noise suppression processes, for the display of HDR images. Our experimental results show that the proposed scheme is indeed effective in enhancing local contrasts of image contents and suppressing noise artifacts during the increase of the visibility of HDR scenes.

We propose a compact 3-D object representation scheme that can greatly assist the search/retrieval process in a network environment. A 3-D mesh-based object is transformed into a new coordinate frame by using the Isomap (isometric feature mapping) method. During the transformation process, not only the structure of the salient parts of an object will be kept, but also the geometrical relationships will be preserved. From the viewpoint of cognitive psychology, the data distributed on the Isomap manifold can be regarded as a set of significant features of a 3-D mesh-based object. To perform efficient matching, we project the Isomap domain 3-D object onto two different 2-D maps, and the two 2-D feature descriptors are used as the basis to measure the degree of similarity between two 3-D mesh-based objects. Experiments demonstrate that the proposed method in retrieving similar 3-D models is very effective. Most importantly, the proposed 3-D mesh retrieval scheme is still valid even if a 3-D mesh undergoes a mesh simplification process.

Visual data comprise of multi-scale and inhomogeneous signals. In this paper, we exploit these characteristics and develop a compact data representation technique based on a hierarchical tensor-based transformation. In this technique, an original multi-dimensional dataset is transformed into a hierarchy of signals to expose its multi-scale structures. The signal at each level of the hierarchy is further divided into a number of smaller tensors to expose its spatially inhomogeneous structures. These smaller tensors are further transformed and pruned using a tensor approximation technique. Our hierarchical tensor approximation supports progressive transmission and partial decompression. Experimental results indicate that our technique can achieve higher compression ratios and quality than previous methods, including wavelet transforms, wavelet packet transforms, and single-level tensor approximation. We have successfully applied our technique to multiple tasks involving multi-dimensional visual data, including medical and scientific data visualization, data-driven rendering and texture synthesis.

In this paper, we propose a novel mesh-decomposition scheme called "visual salience-guided mesh decomposition". The concept of "part salience", which originated in cognitive psychology, asserts that the salience of a part can be determined by (at least) three factors: the protrusion, the boundary strength, and the relative size of the part. We try to convert these conceptual rules into real computational processes, and use them to guide a three-dimensional (3D) mesh decomposition process in such a way that the significant components can be precisely identified and efficiently extracted from a given 3D mesh. The proposed decomposition scheme not only identifies the parts' boundaries defined by the minima rule, but also labels each part with a quantitative degree of visual salience during the mesh decomposition process. The experimental results show that the proposed scheme is indeed effective and powerful in decomposing a 3D mesh into its significant components

In this paper, we incorporate a set of principles originated from cognitive psychology into the design of 3-D shape analysis and retrieval algorithms. Based on the "visual salience-guided mesh decomposition" we previously proposed, a 3-D mesh-based shape is first broken up into parts such that human visual perception on parts can be appropriately mimicked. Next, the decomposed parts are individually analyzed and quantified according to the properties of visual salience. To establish the indices of 3-D meshes for shape matching, spherical parameterization is adopted to map the decomposed parts onto the surface of a unit sphere. In this way, the dissimilarity degree between the query acquired from users and a model in database can be calculated. The experimental results have shown that the matching performance of the proposed scheme is indeed efficient and powerful.

In this paper, we incorporate a set of principles that originated in cognitive psychology into the design of 3D shape analysis and retrieval systems. Based on the "visual salience guided mesh decomposition" scheme we proposed previously, a 3D shape represented in mesh form is first broken into parts such that human visual perception of the parts can be appropriately mimicked. Next, the decomposed parts are individually analyzed and quantified according to the properties of visual salience. To establish the indices of 3D meshes for the subsequent retrieval process, spherical parameterization is adopted to map the decomposed parts onto the surface of a unit sphere. In this way, the degree of similarity between a query provided by a user and models in the database can be calculated. The experiment results show that the retrieval performance of the proposed scheme is indeed efficient and powerful.

Designing a powerful fragile watermarking technique for authenticating three-dimensional (3-D) polygonal meshes is a very difficult task. Yeo and Yeung were first to propose a fragile watermarking method to perform authentication of 3-D polygonal meshes. Although their method can authenticate the integrity of 3-D polygonal meshes, it cannot be used for localization of changes. In addition, it is unable to distinguish malicious attacks from incidental data processings. In this paper, we trade off the causality problem in Yeo and Yeung's method for a new fragile watermarking scheme. The proposed scheme can not only achieve localization of malicious modifications in visual inspection, but also is immune to certain incidental data processings (such as quantization of vertex coordinates and vertex reordering). During the process of watermark embedding, a local mesh parameterization approach is employed to perturb the coordinates of invalid vertices while cautiously maintaining the visual appearance of the original model. Since the proposed embedding method is independent of the order of vertices, the hidden watermark is immune to some attacks, such as vertex reordering. In addition, the proposed method can be used to perform region-based tampering detection. The experimental results have shown that the proposed fragile watermarking scheme is indeed powerful.

In this paper, we propose a novel mesh decomposition scheme called "visual salience-guided mesh decomposition". The concept of part salience is originated from cognitive psychology and it asserts the salience of a part can be determined by (at least) three factors: the protrusion, the boundary strength, and the relative size of a part. We try to convert these "conceptual" rules into "real" computational processes and then use them to "guide" a 3D mesh decomposition process. The experimental results have shown that the proposed scheme is indeed effective and powerful in decomposing a 3D mesh into significant components.