We present a novel hybrid method that includes a modulated scheme for reducing the distortion of the reconstructed image and a two-layered structure for grouping participants with different weights in a secret image sharing problem. Conventional secret image sharing methods suffer from truncation distortion, which is the difference between a pixel value and its truncated result. We first present a histogram modulation scheme to modulate the truncated pixels to prevent the truncation distortion. A two-layer secret image sharing scheme groups the participants in the first layer and weights them differently in the second layer. The proposed modulated secret image sharing method merges these two schemes. Experimental results reveal that the proposed method efficiently reduces the truncation distortion and groups the participants with different possessing weights.
The jigsaw puzzle has been popular from past to present, and there are many jigsaw puzzle images on the Internet. This paper proposes a novel method to hide secret data in jigsaw puzzle images. First, a digital image is taken as input and divided into blocks. Then, a semicircle is drawn and attached to the right and bottom sides of each block. The secret data are embedded through the attached positions and orientations of the semicircles according to a stegokey. The resulting image looks like those jigsaw puzzle images appearing on many jigsaw puzzle Web sites. Experiments show that the proposed method is undetectable under the passive warden and robust to format conversion, lossy compression, and lossy recompression.
A new steganographic method for data hiding in jig swap puzzle images is proposed. First, a color image is taken as input and divided into blocks. Second, each block is rearranged to a new position according to the secret data and a stegokey. The resulting image is a perfect jig swap puzzle. The original image is needed for extracting the secret data. Under the assumption that the receiver and the sender share some common images, the receiver can extract the secret data from the jig swap puzzle image. We also present a scenario for secret data transmission based on an online jig swap puzzle. Experimental results show that the proposed method is undetectable and robust under lossy image compression and format conversion.
This paper proposes a watermark, W, a set of independent and identically distributed Gaussian pseudo random signals, which is embedded into the coeÆcients of the high-low band and ?W is embedded into those of the low-high band at level 3 in a 3-scale wavelet transform. A watermark generation, insertion, extraction, and veri cation after a variety of attacks via image operations such as scaling, smoothing, cropping, noise adding, JPEG, SPIHT, and fractal compression, are demonstrated by using Haar and Daubechies' four wavelet transforms on the image Lenna. Experiments reporting the PSNR value of each attacked image with its corresponding detected level show that the proposed watermarking strategy is promising.
This paper presents a novel algorithm to accelerate the encoding procedure of fractal image compression. We develop an indexing technology to access candidate domain blocks. The location of maximal gradient is adopted as the key for indexing. Only those blocks whose positions of maximal gradients matching that of a given range block are rested. In our experiments, the new algorithm promises good performance. It takes few seconds to encode a 512 by 512 image on a Pentium II 450 PC with a slight loss of decoded image fidelity.
Gabor transform has recently been exploited to do texture analysis, including texture edge detection, texture segmentation/discrimination, and texture synthesis. For most of the applications using Gabor transform, people convolve the given texture image with a set of Gabor filters with some user specified parameters. Although the mathematical formulation of applications involve the Fourier transform, few have investigated mathematical properties of the relationship between Gabor filters and their Fourier transform. This paper mainly studies mathematical properties of real Gabor filters and their corresponding Fourier transform. The goal is to select a set of `interesting' Gabor filters, or say, a set of parameters for Gabor filters to do texture analysis. We demonstrate, by means of 3-D graphical displays, that a Gabor filter or its corresponding Fourier transform may have a single peak or double peaks according to different parameters. Experiments for texture discrimination are given to demonstrate the applications of Gabor transform.
Texture is an important characteristic of analyzing images. A variety of texture features have been proposed for texture discrimination, whereas a best set of texture features never exists. This paper considers statistical textures which can be viewed as realizations of some stochastic processes, or viewed as images containing no apparent objects. We propose using singular value decomposition (SVD) strategy for texture analysis including (a) using the proportion of dominant singular values of an image matrix as texture features for texture discrimination, (b) the singular value decomposition automatically provides a compression technique for textures due to the dependency of neighboring pixels, and (c) an algorithm based on SVD is proposed to synthesize textures. The texture features derived from SVD are stable according to the stability of SVD. Experiments for discriminating synthesized textures and natural textures, for compressing texture data and for synthesizing textures are also given to demonstrate the proposed strategy.
Shape recognition has a variety of applications such as the aircraft identification, character recognition, industrial part recognition, and country map discrimination. One of the most important steps is to extract optimal features to discriminate one shape from the others. This paper reviews and evaluates shape features extracted by different methods. The shape feature sets of improved moment invariants, traditional moment invariants, and Fourier descriptors by testing three sets of digital shapes: (a) Chinese symbols, (b) animal shapes, and (c) toys show that improved moment invariants and traditional moment invariants have near a perfect recognition but Fourier descriptors do not perform well. An efficient shape recognition system based on improved moment invariants is thus established and described.
Moment invariants used as features for shape recognition has been widely used. The moments were computed using all the information of shape boundary associated with the interior region. This paper presents the theoretical improved moments computed based only on the shape boundary. Some invariants derived from improved moments in variation to translation, rotation, and scaling are presented. The computations of improved moment invariants based on chain code representation of a shape boundary can be done in real time. Experiments of discriminating country maps, industrial tools, and printed numerals by using improved moment invariants as features via graphical plots suggest that the improved moment invariants be good shape features close to human visual processing.
Texture features obtained by fitting generalized Ising, auto-binomial, and Gaussian Markov random fields to homogeneous textures are evaluated and compared by visual examination and by standard pattern recognition methodology. The Markov random field model parameters capture the strong cues for human perception, such as directionality, coarseness, and/or contrast. The limited experiments for the classification of natural textures and sandpaper textures by using various classifiers suggest that both feature extraction and classifier design be carefully considered.