Richard O. Eason

Principles and applications of BPCS steganography

Steganography is a technique to hide secret information in some other data without leaving any apparent evidence of data alternation. All of the traditional steganographic techniques have limited information-hiding capacity. They can hide only 10 percent of the data mounts of the vessel. This is because the principle of those techniques was either to replace a special part of the frequency components of the vessel image, or to replace all the least significant bits of a multi-valued image with the secret information.

A new algorithm for N-dimensional Hilbert scanning

There have been many applications of the Hilbert curve, such as image processing, image compression, computer hologram, etc. The Hilbert curve is a one-to-one mapping between N-dimensional space and one-dimensional (l-D) space which preserves point neighborhoods as much as possible. There are several algorithms for N-dimensional Hilbert scanning, such as the Butz algorithm and the Quinqueton algorithm. The Butz algorithm is a mapping function using several bit operations such as shifting, exclusive OR, etc. On the other hand, the Quinqueton algorithm computes all addresses of this curve using recursive functions, but takes time to compute a one to-one mapping correspondence. Both algorithms are complex to compute and both are difficult to implement in hardware. In this paper, we propose a new, simple, nonrecursive algorithm for N-dimensional Hilbert scanning using look-up tables. The merit of our algorithm is that the computation is fast and the implementation is much easier than previous ones.

Depth-first coding for multivalued pictures using bit-plane decomposition

A data compression technique using a bit-plane decomposition strategy of multivalued images is described. Although the bit-plane decomposition is mainly used for image transmission, our method takes the image expression for image database into consideration. It has two merits which are a hierarchical representation using depth-first (DF) expression and a simple noise reduction algorithm for the DF expression that is similar to human perception. The DF expression is useful for image expansion, rotation, etc. We study the information in an image that should be eliminated by noise reduction. Noise-like patterns in an image are uniformalized and the edge and smooth surfaces remain nearly unchanged. They are not blurred, but instead are a little enhanced. We also study the properties of the black-and-white (B/W) boundary points on bit-planes. The algorithm of the uniformalization process with a DF-expression of an image is described. An experiment for real image data is carried out by a comparison to other methods, and the results are discussed. >

A Large Capacity Steganography Using Color BMP Images

A new steganography (information hiding technique) is proposed. It uses a color image as the information hiding dummy image, i.e., the container, or carrier of the secret information. This new technique is not based on a programming technique, but is based on a property of human vision system such that human eyes are blind to very complex binary patterns. In other word, human can not see the effect of the data change, even if the “noise-like” portions in the bit-planes of a multi-valued image are all changed to other noise-like patterns. In order to assure this property, we made a replacement experiment of noise-like portions of a color photo with random binary patterns, and it turned out in a surprising result. This human vision property is the key to the large capacity steganography which uses a color image in a BMP file format. This new technique may open a new step to an internet communication age.

Luminance quasi-preserving color quantization for digital steganography to palette-based images

This paper proposes a method to apply BPCS-Steganography that we have already proposed for gray scale images to palette-based images which consists of a palette storing color vector information and an index image whose pixel value is corresponding to an index in the palette. A palette-based image can be represented by combining R G and B color component images. We embed secret information into the G images. A number of color vectors in a palette after embedding by BPCS would be over the maximum number which is usually 256. In order to reduce the number of colors, the rest two component images are then changed in a way that minimizes the square error. The idea behind the color quantization is that the degrading of images manipulated to reduce color is worse than the degrading which occurs with the embedding.

A neural network classifier for LANDSAT image data

There have been many new developments in neural network (NN) research, and many new applications have been studied. The classification of remotely sensed multispectral data using classical statistical methods has been worked on for several decades. Among the multispectral data, we concentrate on the Landsat-5 Thematic Mapper (TM) image data which has been available since 1984. Using this classical maximum likelihood approach, a category is modeled as a multivariate normal distribution; however, the distribution for Landsat images is unknown. It is well known that NN approaches have the ability to classify without assuming a distribution. We apply the NN approach to the classification of Landsat TM images in order to investigate the robustness of this approach for multi-temporal data classification. The authors confirmed that the NN approach is effective for the classification even if the test data is taken at the different time.<<ETX>>

A camera calibration using four point-targets

A method for determining the position of a camera using four point-targets is studied. three rotation angles and a translation vector are used to describe the position of the camera for a pinhole model. For solving the six unknown parameters, a minimum of six point-targets is required to define the matrix uniquely (rotation and translation). However, it is shown that by using the properties of the matrix this number can be reduced to four. The error properties of this method are discussed using real image data.<<ETX>>

Least-squares-based data fusion strategies and robotic applications

Many approaches to data fusion involve the use of least squares methods. Such methods are typically used for parameter estimation in applications such as pose estimation, motion analysis, shape estimation, and camera calibration. In this paper we describe the general least squares problem and some common solution methods, and overview its use in several robotic applications.