Xutao Li

Complicated self-similarity of terrain surface

Fractal describes the self-similar phenomenon of signal and self-similarity is the most important character of fractal. Pentland provides an excellent explanation of the ruggedness of natural surface. Fractal-based description of image texture has been used effectively in characterization and segmentation of natural scene. A real surface is self-similar over some range of scales, rather than over all scales. That imply self-similarity of a terrain surface is not always so perfect that keep invariable in whole scale space. To describe such self-similarity distribution, a self-similarity curve could be plotted and was divided into several linear regions. We present a new parameter called Self-similarity Degree (SD) in the similitude of information entropy to denote such self-similarity distribution. In addition, one general characterization of self-similarities is result of physical processes. Terrain surface are created by the interactional inogenic and exogenic processes. Hereby, we introduce self-similarity analysis and multifractal singularity spectrum to describe such complex physical field. By the self-similarity analysis and singularity spectrum, the different self-similar structures and the interaction of processes in terrain surface were depicted. Our studies have shown that self-similarity is a relative notion and natural scenes own abundant self-similar structures. Moreover, noises always destroy the self-similarity of original natural surface and change the singularity distribution of original surface.

A new texture representation with multi-scale wavelet feature

The existing methods for texture modeling include co-occurrence statistics, filter banks and random fields. However most of these methods lack of capability to characterize the different scale of texture effectively. In this paper, we propose a texture representation which combines local scale feature, amplitude and phase of wavelet modules in multi-scales. The self-similarity of texture is not globally uniform and could be measured in both correlations across the multi-scale and statistical feature within a single-scale. In our approach, the local scale feature is represented by optimal scale obtained through the evolution of wavelet modulus across multi-scales. Then, for all the blocks of the same optimal scale, the statistical measurement of amplitude is extracted to represent the energy within the corresponding frequency band; the statistical measurement of the phase of modulus is extracted to represent the textures orientation. Our experiment indicates that, in the proposed texture representation the separability of different texture patterns is larger than the one of the traditional features.

Multiscale self-similarity features of terrain surface

Self-similarity features of natural surface play a key role in region segmentation and recognition. Due to long period of natural evolution, real terrain surface is composed of many self-similar structures. Consequently, the Self-similarity is not always so perfect that remains invariable in whole scale space and the traditional single self-similarity parameter can not represent such abundant self-similarity. In this view, the self-similarity is not a constant parameter over all scales, but multi-scale parameters. In order to describe such multi-scale self-similarities of real surface, firstly we adopt the Fractional Brownian Motion (FBM) model to estimate the self-similarity curve of terrain surface. Then the curve is divided into several linear regions to represent relevant self-similarities. Based on such regions, we introduce a parameter called Self-similar Degree (SSD) in the similitude of information entropy. Moreover, the small value of SSD indicates the more consistent self-similarity. We adopt fifty samples of terrain images and evaluate SSD that represents the multi-scale self-similarity features for each sample. The samples are clustered by unsupervised fuzzy c mean clustering into various classes according to SSD and traditional monotone Hurst feature respectively. The measurement for separability of features shows that the new parameter SSD is an effective feature for terrain classification. Therefore the similarity feature set that is made up of the monotone Hurst parameter and SSD provides more information than traditional monotone feature. Consequently, the performance of terrain classification is improved.

A new edge detection based on pyramid-structure wavelet transform

Many advance image processing, like segmentation and recognition, are based on contour extraction which usually lack of ability to allocate edge precisely in the image of heavy noise with low computation burden. For such problem, in this paper, we proposed a new approach of edge detection based on pyramid-structure wavelet transform. In order to suppress noise and keep good continuity of edge, the proposed edge representation considered both inter-correlations across the multi-scales and intra-correlations within the single-scale. The former one is described by point-wise singularity. The later one is described by the magnitude and ratio of wavelet coefficients in different sub-bands. Based on such edge modeling, the edge point allocation is then complemented in wavelet domain by synthesizing the edge information in multi-scales. The experimental results shows that our approaches achieve the pixel-level edge detection with strong resistant against noise due to scattering in water.

Super resolution reconstruction based on motion estimation error and edge adaptive constraints

In order to improve the quality of image with super-resolution reconstruction, a method based on motion estimation error and edge constraint was proposed. Under the condition of data consistency and amplitude restriction, the motion estimation error was analyzed, with its variance being calculated; meanwhile, in order to suppress the ringing artifacts, edge constraint was adopted and a method based clustering for judging the edges direction was proposed. The experimental results show that the performance of the this algorithm is better than the traditional linear interpolation and method without considering motion estimation error both in vision effect and peak signal to noise ratio.

A Classifier for Radar Clutter Using Alpha Stable Model

A valid classifier for amplitude statistic models of radar clutter is proposed, in which the clutter is modeled as the alpha stable distribution and the clutter series whose statistic distributions subject to five traditional models such as Rayleigh, Weibull, Log-norm, Rice and K distribution is recognized based on the exponents of alpha stable distribution. Simulation results show that the approach has high recognition precision and less computation burden

Peak inspecting and signal recovery methods based on triple correlation

Weak target inspecting and recovering are very important in IR detecting systems. In this paper, triple correlation peak inspecting techniques (TCPIT) are adopted for the signal processing of IR systems in detecting sub-pixel or point targets. Investigations show that the signal-to-noise ratio (SNR) improvement of approximate 23dB can be obtained with the input peak SNR of 0.84 and the input power SNR of -0.93dB. The triple correlation overlapping sampling technique (TCOST) is advanced for restoring signal waveforms of IR detection systems. Investigations show that signal waveforms can effectively be restored in the low signal-to-noise ratio circumstances using this approach.

Novel digital watermarking method based on DWT and DCT

Digital watermarking has been recently proposed as the mean for property right protection of digital products. In this paper we analyze the self-similarities of wavelet transform and present a new approach to embed a digital watermark into an image based on the qualified significant wavelet trees (QSWT) of discrete wavelet transform of the image for the purpose of protecting the copyright of the image. Our studies have shown that the watermarked image has a good quality of image, and such a watermark is difficult to detect and unchangeable without the appropriate user cryptogram.