Xun Du

Three-dimensional vector estimation for subcomponents of space object imagery

We describe a model-based image analysis system that automatically estimates the 3-D orientation vectors of satellites and their subcomponents by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database and pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index-hashing method is introduced. An affine point-matching method is also introduced for improving system performance on a wide variety of satellite shapes. We present recent results, which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites.

2D HRR Radar Data Modeling and Processing

High Range Resolution (HRR) -based Automatic Target Recognition (ATR) has attracted increasing attention due to a number of potential advantages over alternative radar techniques in moving target identification. Most current HRR-based ATR studies have been conducted using 1D HRR signatures. However, these 1D HRR signatures are generally plagued by scintillation effects, and thus demonstrate highly irregular behavior that dramatically degrades the performance and robustness of algorithms based on these signatures. In order to circumvent this difficulty, an alternative HRR radar data representation and processing technique is presented in this paper. This technique models and extracts the target characteristics directly, based on the 2D HRR raw data. In this paper, we first derive a general, but complex HRR radar model, and then simplify this model by instantiating a set of real-world radar and target parameters for the model. This simplification process produces two HRR radar models with different degrees of simplicity. After establishing this set of models, the typical HRR data processes, such as feature extraction and clutter suppression, are reduced to one problem, which is model-parameter estimation. Based upon the most simplified HRR model we proposed, we devise two model- parameter estimation algorithms. One is a scatterer extraction algorithm based on available 1D Parameter Estimation (1DPE), while the other is based on 2D discrete Fourier Transform (2DFT). In order to examine the performance of these two algorithms a set of simulations are conducted. The experimental results are presented, and the performance comparison between 1DPE and 2DFT is presented.

3D orientation vector estimation for subcomponents of space object imagery

We describe a model-based image analysis system which automatically estimates the 3D orientation vector of satellites and their sub-components by analyzing images obtained from a ground-based optical surveillance system. We adopt a two-step approach: pose estimates are derived from comparisons with a model database; pose refinements are derived from photogrammetric information. The model database is formed by representing each available training image by a set of derived geometric primitives. To obtain fast access to the model database and to increase the probability of early successful matching, a novel index hashing method is introduced. We present recent results which include our efforts at isolating and estimating orientation vectors from degraded imagery on a significant database of satellites. We also discuss the problems our system encounters with some of the images, and the solutions we are implementing to significantly improve the system.

Content-based image compression for ATR applications

Conventional image compression methods compress all regions of an image with a roughly uniform compression ratio. This means that any regions of special interest are degraded on an equal basis as the remainder of the image. Content-Based Image Compression (CBIC) methods assign different compression rates to different regions of an image according to their priorities, or according to the relative importance of the regions for certain applications. For example, for visual perception, we can assign different compression rates to different objects so that after compression the objects of interest satisfy certain MSE (Mean Square Error) requirements regardless of the overall compression rate. For optimal ATR (Automatic Target Recognition) performance, the recognition error rate might be optimized instead of MSE so that target recognition performance will be guaranteed at some desired level, and held constant throughout the entire image. In this paper, we introduce a content-based image encoder based on the popular DCT and wavelet transforms. Instead of selecting the DCT/wavelet coefficients that minimize the MSE to achieve optimum visual effects, we propose an algorithm to preserve those coefficients that minimize the recognition error. For any ATR system that utilizes the resulting compressed images, the recognition error is bounded by the information-theoretic distances. We employ Chernoff distances to compute the cost function of the recognition error. Compared to image compression methods optimized for visual perception, our results show that this CBIC method for ATR is able to achieve significantly more uniform ATR performance by assigning different compression rates to different regions.

Eigen indexing in satellite recognition

In many image analysis problems it is possible to take advantage of the structural relationships between various parts of the objects being imaged in order to index the images of the objects. For example, many satellites consists of a main body and outlying sub-components. Thus, in many circumstances satellites can be indexed in a model database by the distinct structural relationships between their sub- components. However, algorithms based on structured sub- components necessitate the use of robust and reliable 2-D image segmentation techniques to successfully partition images into their sub-components. Unfortunately, this segmentation task can be highly problematic for objects with complex components and under harsh, unfavorable lighting conditions. The research presented here describes a new method to compute indices which can be used for image indexing without image segmentation. We use satellite imagery as a convenient image class for which to demonstrate our method. Our method partitions the image into many small equal-area pieces. We refer to this technique as differentiation. Differentiated images result in a set of sub-images that collectively represent the structural information inherent in the image. We prove that a primitive matrix with at most four non-zero eigenvalues can be constructed from the differentiated image. This property (1) significantly reduces storage requirements for a model database, (2) reduces the computational burden of subsequent recognition processes, and (3) supports an efficient and accurate matching procedure. To evaluate the efficiency of our algorithm for a recognition application, we use boundary methods as a feature set evaluation method to quantify the utility of the eigen-indexes obtained by our method as compared to other existing indexing methods.

Content-based image and video compression

The term Content-Based appears often in applications for which MPEG-7 is expected to play a significant role. MPEG-7 standardizes descriptors of multimedia content, and while compression is not the primary focus of MPEG-7, the descriptors defined by MPEG-7 can be used to reconstruct a rough representation of an original multimedia source. In contrast, current image and video compression standards such as JPEG and MPEG are not designed to encode at the very low bit-rates that could be accomplished with MPEG-7 using descriptors. In this paper we show that content-based mechanisms can be introduced into compression algorithms to improve the scalability and functionality of current compression methods such as JPEG and MPEG. This is the fundamental idea behind Content-Based Compression (CBC). Our definition of CBC is a compression method that effectively encodes a sufficient description of the content of an image or a video in order to ensure that the recipient is able to reconstruct the image or video to some degree of accuracy. The degree of accuracy can be, for example, the classification error rate of the encoded objects, since in MPEG-7 the classification error rate measures the performance of the content descriptors. We argue that the major difference between a content-based compression algorithm and conventional block-based or object-based compression algorithms is that content-based compression replaces the quantizer with a more sophisticated classifier, or with a quantizer which minimizes classification error. Compared to conventional image and video compression methods such as JPEG and MPEG, our results show that content-based compression is able to achieve more efficient image and video coding by suppressing the background while leaving the objects of interest nearly intact.

Content-based image compression

First generation image compression methods using block-based DCT or wavelet transforms compressed all image blocks with a uniform compression ratio. Consequently, any regions of special interest were degraded along with the remainder of the image. Second generation image compression methods apply object-based compression techniques in which each object is first segmented and then encoded separately. Content-based compression further improves on object-based compression by applying image understanding techniques. First, each object is recognized or classified, and then different objects are compressed at different compression rates according to their priorities. Regions with higher priorities (such as objects of interest) receive more encoding bits as compared to less important regions, such as the background. The major difference between a content-based compression algorithm and conventional block-based or object-based compression algorithms is that content-based compression replaces the quantizer with a more sophisticated classifier. In this paper we describe a technique in which the image is first segmented into regions by texture and color. These regions are then classified and merged into different objects by means of a classifier based on its color, texture and shape features. Each object is then transformed by either DCT or Wavelets. The resulting coefficients are encoded to an accuracy that minimizes recognition error and satisfies alternative requirements. We employ the Chernoff bound to compute the cost function of the recognition error. Compared to the conventional image compression methods, our results show that content-based compression is able to achieve more efficient image coding by suppressing the background while leaving the objects of interest virtually intact.