The conventional encryption techniques of visual cryptography are extended to web page. The key point is that Cascading Style Sheet blend modes can simulate stacking operators. The multiply blend mode can be regarded as Boolean OR operator, and the difference blend mode can be regarded as exclusive-OR (XOR) operator. Thus, the secrets can be revealed in a web browser by overlapping shares together without any additional computation. We named this scheme Web-VC. The decoding procedure of Web-VC is just like usual visual cryptography, while digital shares can be used in this case. Web-VC has strong ability of resisting compression and transcoding without losing the secret. Another contribution of this work is the method of XOR-based flip visual cryptography.

The K-means method is a well-known clustering algorithm with an extensive range of applications, such as biological classification, disease analysis, data mining, and image compression. However, the plain K-means method is not fast when the number of clusters or the number of data points becomes large. A modified K-means algorithm was presented by Fahim et al. (2006). The modified algorithm produced clusters whose mean square error was very similar to that of the plain K-means, but the execution time was shorter. In this study, we try to further increase its speed. There are two rules in our method: a selection rule, used to acquire a good candidate as the initial center to be checked, and an erasure rule, used to delete one or many unqualified centers each time a specified condition is satisfied. Our clustering results are identical to those of Fahim et al. (2006). However, our method further cuts computation time when the number of clusters increases. The mathematical reasoning used in our design is included.

The stego-images generated by many existing hiding techniques are not economic in size, and hence need compression. Unfortunately, compression usually destroys the secret content hidden inside. To solve this dilemma, some hiding methods based on compression code (rather than the image itself) are reported. This paper proposes a high-capacity and high-hiding-ratio "reversible" steganography method based on JPEG-compression code. In the proposed method, the JPEG compression code of an image is used as the cover media. An 8×8 hiding-capacity table is firstly evaluated, which is then utilized to modify the quantization table attached to the given JPEG code. The two quantization tables (modified and original) together can map the DCT coefficients of each block to some larger DCT coefficients, with secret data hidden inside these larger DCT coefficients. In the decoding process, after lossless extraction of the hidden secret data, the proposed method can also recover the original JPEG-compression code. Experimental results show that our method outperforms other JPEG-based hiding methods (reversible or not) regarding both hiding-ratio and stego-image's quality.

In secret image sharing, a polynomial interpolation technique heavy experiences a computation load when the secret image is retrieved later. To the contrary, fast approaches often need larger storage space due to pixel expansion property. This paper proposes a missing-allowable (k, n) scheme which is fast and with a reasonable pixel expansion rate (per). The scheme generates n extremely-noisy shadow images for the given secret color image A, and any k out of these n shadows can recover A loss-freely. In average, to decode a color pixel of A, the retrieval uses only three exclusion-OR operations among 24-bit numbers. Hence, the new method has very fast decoding speed, and its pixel expansion rate is always acceptable (0 < per < 2).

This paper modifies an elegant data hiding method of Chang et alpsilas [Pattern Recog. Lett. 2004, vol. 25, no. 11, 1253-1261]. The embedding employs a modified search-order coding (SOC) technique to hide binary secret data in the vector quantization (VQ) index file of a host image. The recovered host image is identical to the one recovered by traditional VQ; the compression rate is competitive to traditional VQpsilas (often better than traditional VQpsilas if we use t = 1 mode); while the hidden binary data is revealed in the decompression procedure as a bonus. The compression rate and hiding capacity are also better than those in Chang et alpsilas.

Visual cryptography (VC) was introduced by Naor and Shamir. In general, a (k, n)-threshold scheme shares a given secret image S among n so-called shadow images. People can "view" the secret image by simply stacking any k of these n shadows (k < n is a threshold value). In most of the reported VC schemes, the size of each shadow is greater than that of S. This paper, we use Hilbert curve and two queues to present a VC scheme with the following properties: (1). Each shadow image is as small as the input image S. (2). Many gracious (k, n) contrast-optimal algorithms can be directly and easily applied to our scheme. (3) Image quality is competitive.

This paper proposes a method for hiding an important image in a cover image whose size is limited. In this method, in order to save space, a modified search-order coding (MSOC) technique first transforms the important image, then, a randomization procedure permutes the transformed image to further increase the security. Finally, a modulus function embeds the permuted code in a cover image; notably, in the modules function, the modulus base used for a pixel is determined according to the variance of its neighboring pixels. Experimental results show that the images are of high quality. Comparisons with reported methods are provided.

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.

This study presents a user-friendly image-sharing method for easier management of the shadow images. The sharing of images among several branches (distributed disks) using the proposed method has several characteristics: 1) fast transmission among branches; 2) fault tolerance; 3) a secure storage system; 4) reduced chance of pirating of high-quality images; and 5) most importantly, the provision to each branch manager an easy-to-manage environment (because each shadow image looks like a shrunken version of the original image). The current approach still has the small-size and channel-independent properties of our previous work, namely, the size of each shadow image is only 1/r of that of the original image, and any r shadow images can be used for restoration (the restored image is independent of which r shadow images are used).

A new algorithm for accurate optical flow (OF) estimation using discrete wavelet approximation is proposed. The computation of OF depends on minimizing the image and smoothness constraints. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately approximate any function. OF vectors and image functions are represented by means of linear combinations of scaling basis functions. Based on such wavelet-based approximation, the leading coefficients of these basis functions carry global information about the approximated functions. The proposed method can successfully convert the problem of minimizing a constraint function into that of solving a linear system of a quadratic and convex function of scaling coefficients. Once all the corresponding coefficients are determined, the flow vectors can be obtained accordingly. Experiments have been conducted on both synthetic and real image sequences. In terms of accuracy, the results show that our approach outperforms the existing methods which adopted the same objective function as ours.

A new method for person identification based on facial motion is proposed. Facial motion is represented by a high-dimensional feature vector which is constructed by concatenating a sequence of motion flow fields. Under the proposed representation, each individual can be characterized as a feature vector which collects spatial and temporal information of a face simultaneously We then make use of this high-dimensional spatiotemporal feature vector to perform person identification. The experimental results are encouraging and exciting in terms of robustness under varying illumination conditions.

In this paper, a new algorithm for accurate optical flow estimation using discrete wavelet approximation is proposed. The proposed method takes advantages of the nature of wavelet theory, which can efficiently and accurately represent "things", to model optical flow vectors and image related functions. Each flow vector and image function are represented by linear combinations of wavelet basis functions. From such wavelet-based approximation, the leading coefficients of these basis functions carry the global information of the approximated "things". The proposed method can successfully convert the problem of minimizing a constraint function into that of solving a linear system of a quadratic and convex function of wavelet coefficients. Once all the corresponding coefficients are decided, the flow vectors can be determined accordingly. Experiments conducted on both synthetic and real image sequences show that our approach outperformed the existing methods in terms of accuracy.

A new fast approach to the nearest codeword search using a single kick-out condition is proposed. The nearest codeword found by the proposed approach is identical to the one found by the full search, although the processing time is much shorter. The principle is to bypass those codewords which satisfy the proposed kick-out condition without the actual (and time-consuming) computation of the distortions from the bypassed codewords to the query vector. Due to the efficiency and simplicity of the proposed condition, a considerable saving of the central processing unit time needed to encode a data set (using a given codebook) can be achieved. Moreover, the memory requirement is low. Comparisons with some previous works are included to show these two benefits.

By finding the maximum and minimum of {y/sub i/-mx/sub i/|1/spl les/i/spl les/N} for certain slopes m, we propose here a simple and fast parallel algorithm to obtain the convex hull of N arbitrarily given points on an image board, The mathematical theory needed is included, and computation time is 1 log N.

A new color image compression technique based on moment-preserving and block truncation coding is proposed. An input image is divided into nonoverlapping blocks and each block pixel is assigned one of two representative colors, which are computed with analytic formulas derived from preserving certain moments in the block. A bit map is then generated for each block to represent the pixels' colors. Different uniformity conditions in the representative colors are also identified and utilized to save code bits. Good average compression ratios up to about 13 can be achieved, as shown by experimental results.

This paper proposes a new clustering method based on the hierarchical use of the analytical two-class clustering tool introduced by Lin and Tsai.1 The method comprises two phases. In the first phase, called the splitting phase, the data set is hierarchically decomposed into some subsets. In the second phase, called the merging phase, the set-to-set distances between these subsets are checked so that some subsets can be merged back together to obtain better clustering results. We use the idea of the so-called dense cut to determine when to stop the splitting phase. We also use a trace-following technique for the so-called boundary data to reduce significantly the computational load involved in the merging phase. Two algorithms are provided, and many experiments are included to show that the data being processed are not required to be linearly separable, noiseless, or formed of spherical clusters.

A new color quantization method that uses the radius weighted mean cut (RWM-cut) is proposed. The method is hierarchically divisive, and each two-class partition only uses the centroid and the RWM. The processing speed is fast and the MSE quantization error is competitive. The algorithm can either be used alone or be used to create a good initial palette for the LBG algorithm.

A new color image compression technique based on moment-preserving and block truncation coding (BTC) is proposed. An input image is divided into non-overlapping blocks. And each block is coded by two representative colors, or equivalently, by two sets of tristimulus values (R/sub 1/, G/sub 1/, B/sub 1/) and (R/sub 2/, G/sub 2/, B/sub 2/). To compute the two representative colors of each block, some color moments of the block are preserved, and the two sets of desired tristimulus values are computed accordingly using analytic formulas. The compression ratio is 6 when the block size is 4/spl times/4. Furthermore, a simple method is applied to quantize the bit map into 64 predefined bit maps and requantize each color component from 8 bits into 4 bits. An average compression ratio of 14.00 can be achieved.>

We propose a new method to perform two-class clustering of 2-D data in a quick and automatic way by preserving certain features of the input data. The method is analytical, deterministic, unsupervised, automatic, and noniterative. The computation time is of order n if the data size is n, and hence much faster than any other method which requires the computation of an n-by-n dissimilarity matrix. Furthermore, the proposed method does not have the trouble of guessing initial values. This new approach is thus more suitable for fast automatic hierarchical clustering or any other fields requiring fast automatic two-class clustering of 2-D data. The method can be extended to cluster data in higher dimensional space. A 3-D example is included.>