Yuh-Rau Wang

A blind watermarking method using maximum wavelet coefficient quantization

This paper proposes a blind watermarking algorithm based on maximum wavelet coefficient quantization for copyright protection. The wavelet coefficients are grouped into different block size and blocks are randomly selected from different subbands. We add different energies to the maximum wavelet coefficient under the constraint that the maximum wavelet coefficient is always maximum in a block. The watermark is embedded the local maximum coefficient which can effectively resist attacks. Also, using the block-based watermarking, we can extract the watermark without using the original image or watermark. Experimental results show that the proposed method is quite robust under either non-geometry or geometry attacks.

A wavelet-tree-based watermarking method using distance vector of binary cluster

This paper proposes a wavelet-tree-based watermarking method using distance vector of binary cluster for copyright protection. In the proposed method, wavelet trees are classified into two clusters using the distance vector to denote binary watermark bits. The two smallest wavelet coefficients in a wavelet tree are used to reduce distortion of a watermarked image. The distance vector, which is obtained from the two smallest coefficients of a wavelet tree, is quantized to decrease image distortion. The trees are classified into two clusters so that they exhibit a sufficiently large statistical difference based on the distance vector, which difference is then used for subsequent watermark extraction. We compare the statistical difference and the distance vector of a wavelet tree to decide which watermark bit is embedded in the embedding process. The experimental results show that the watermarked image looks visually identical to the original and the watermark can be effectively extracted upon image processing attacks.

An intelligent watermarking method based on particle swarm optimization

Meerwald, Koidl, and Uhl (2009) pointed out that the method proposed in Lin et al. (2008) exists potential insecurity. This paper proposes an intelligent watermarking by invoking particle swarm optimization (PSO) technique in wavelet domain to overcome the revealed insecurity issue, furthermore resolve the conflict between imperceptibility and robustness of watermarking. In the proposed method, PSO is fused with the method proposed in Lin et al. (2008) (denoted SDWCQ) to avoid potentially insecurity in Lin et al. (2008). That is, the method of using the fixed block size in one subband and the permutation is unable to disguise which coefficients make up a block. The attacker can utilize the insecure property and analyze the significant difference between bipolar watermarks in Lin et al. (2008) to detect the embedded blocks, furthermore modify the significant difference, and result in unable to extract the watermark. In this paper, coefficients are randomly selected from different subbands to make up a block to promote the disguise. Performance analysis shows that the proposed algorithm obviously outperforms SDWCQ which does not use PSO.

A sliding window technique for efficient license plate localization based on discrete wavelet transform

Real-time license plate recognition (LPR) is an interesting but complicated research topic. Some previous works use discrete wavelet transform (DWT) to extract license plate (LP), however, most of them are not capable of dealing with complex environments such as the low-contrast source images and the dynamic-range problems. In this paper, we propose an algorithm for license plate localization (LPL) based on DWT. The LP can be extracted from different quality of source images under complex environments by using two frequency subbands. We first use the HL subband to search the features of LP and then verify the features by checking whether in the LH subband there exists a horizontal line around the feature or not. The proposed method can extract both front and back LPs of various vehicles. The experimental results show that the proposed method can achieve good LPL results with both short run-time and high accurate detection rate.

MTPSO algorithm for solving planar graph coloring problem

Research highlights? A modified turbulent particle swarm optimization (MTPSO) model is proposed to solve the planar graph. ? MTPSO combines walking one strategy, assessment strategy and turbulent strategy. ? MTPSO can solve the four-colors problem efficiently and accurately. In this paper, we proposed a modified turbulent particle swarm optimization (named MTPSO) model for solving planar graph coloring problem based on particle swarm optimization. The proposed model is consisting of the walking one strategy, assessment strategy and turbulent strategy. The proposed MTPSO model can solve the planar graph coloring problem using four-colors more efficiently and accurately. Compared to the results shown in Cui et al. (2008), not only the experimental results of the proposed model can get smaller average iterations but can get higher correction coloring rate when the number of nodes is greater than 30.

Efficient algorithms for the all nearest neighbor and closest pair problems on the linear array with a reconfigurable pipelined bus system

We present two O(1)-time algorithms for solving the 2D all nearest neighbor (2D/spl I.bar/ANN) problem, the 2D closest pair (2D/spl I.bar/CP) problem, the 3D all nearest neighbor (3D/spl I.bar/ANN) problem and the 3D-closest pair (3D/spl I.bar/CP) problem of n points on the linear array with a reconfigurable pipelined bus system (LARPBS) from the computational geometry perspective. The first O(1) time algorithm, which invokes the ANN properties (introduced in this paper) only once, can solve the 2D/spl I.bar/ANN and 2D/spl I.bar/CP problems of n points on an LARPBS of size 1/2n/sup 5/3+c/, and the 3D/spl I.bar/ANN and 3D/spl I.bar/CP problems pf n points on an LARPBS of size 1/2n/sup 7/4+c/, where 0 < /spl epsi/ = 1/2/sup c+1/-1 /spl Lt/ 1, c is a constant and positive integer. The second O(1) time algorithm, which recursively invokes the ANN properties k times, can solve the kD/spl I.bar/ANN, and kD/spl I.bar/CP problems of n points on an LARPBS of size 1/2n/sup 3/2+c/, where k = 2 or 3, 0 < /spl epsi/ = 1/2/sup n+1/-1 /spl Lt/ 1, and c is a constant and positive integer. To the best of our knowledge, all results derived above are the best O(1) time ANN algorithms known.

The m-pancycle-connectivity of a WK-Recursive network

In this paper, we study the m-pancycle-connectivity of a WK-Recursive network. We show that a WK-Recursive network with amplitude W and level L is strictly (5x2^L^-^1-2)-pancycle-connected for W>=3. That is, each pair of vertices in a WK-recursive network with amplitude greater than or equal to 3 resides in a common cycle of every length ranging from 5x2^L^-^1-2 to N, where N is the size of the interconnection network; and the value 5x2^L^-^1-2 reaches the lower bound of the problem.

An O(1) time algorithm for the 3D Euclidean distance transform on the CRCW PRAM model

We develop a parallel algorithm for the 2D Euclidean distance transform (2D/spl I.bar/EDT, for short) of a binary image of size N /spl times/ N in O(1) time using N/sup 2+/spl delta/+/spl epsi// CRCW processors and a parallel algorithm for the 3D Euclidean distance transform (3D/spl I.bar/EDT, for short) of a binary image of size N /spl times/ N /spl times/ N in O(1) time using N/sup 3+/spl delta/+/spl epsi// CRCW processors, where /spl delta/=1/, /spl epsi/=1/(2/sup c+1/-1), h, and are constants and positive integers. Our 2D/spl I.bar/EDT (3D/spl I.bar/EDT) parallel algorithm can be used to build up Voronoi diagram and Voronoi polygons (polyhedra) in a 2D (3D) binary image also. All of these parallel algorithms can be performed in O(1) time using N/sup 2+/spl delta/+/spl epsi// (N/sup 3+/spl delta/+/spl epsi//) CRCW processors. To the best of our knowledge, all results derived above are the best O(1) time algorithms known.

Parallel algorithms for arbitrary dimensional Euclidean distance transforms with applications on arrays with reconfigurable optical buses

In this paper, we present algorithms for computing the Euclidean distance transform (EDT) of a binary image on the array with reconfigurable optical buses (AROB). First, we develop a parallel algorithm termed as Algorithm Expander which can be implemented in O(1) time on an AROB with N x Ndelta processors, where delta = 1/k, k is a constant and a positive integer. Algorithm Expander is designed to compute a higher dimensional EDT based on the computed lower dimensional EDT. It functions as a general EDT expander for us to expand EDT from a lower dimension to a higher dimension. We then develop parallel algorithms for the two-dimensional (2-D)_EDT of a binary image array of size N x N in O(1) time on an AROB with N x N x Ndelta processors and for the three-dimensional (3-D)_EDT of a binary image of size N x N x N in O(1) time on an AROB with N x N x N x Ndelta processors. To the best of our knowledge, all results derived above are the best O(1) time algorithms known. We then extend it to compute the nD_EDT of a binary image of size Nn in O(n) time on an AROB with Nn+delta processors. We also apply our parallel EDT algorithms to build Voronoi diagram and Voronoi polyhetra (polygons), to find all maximal empty spheres and the largest empty sphere, and to compute the medial axis transform. All of these applications can be solved in the same time complexity on an AROB with the same number of processors as needed for solving the EDT problems in the same dimensions.

A novel real time hand detection based on skin-color

This paper proposes a novel real time hand detection algorithm based on skin-color. The proposed algorithm does not need training data and can be applied in dynamic and complex environments. The proposed method can directly find the palm area and fingers. It needs neither segmenting the wrist nor finding the contour. The detecting procedure is simple and fast. The experimental results show that the proposed method can detect hands in real time with low computing consumption under up to 3 meters of the distance between webcam and hand.