AbstractsComputer Science

Low-Observable Object Detection and Tracking Using Advanced Image Processing Techniques

by Meng Li

Institution: University of Toledo
Department: Engineering (Computer Science)
Degree: MS
Year: 2014
Keywords: Computer Science; Image Denoising, Image Enhancement, Low-Observable Object Detection and Tracking, TV, PCA, MSR, GHE, GA
Record ID: 2034808
Full text PDF: http://rave.ohiolink.edu/etdc/view?acc_num=toledo1396465762


Over the past few years, digital image processing has been widely studied and used in various fields. Image processing uses computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the bulid-up of noise and signal distortion during processing. In this thesis, we are going to introduce three important algorithms dealing with digital images: image denoising, image enhancement and target detection and tracking. The proposed Genetic Algorithm (GA) can detect and track dim, low observable and point targets, mainly for remote monitoring applications. As a first step to detect and track objects more effectively, the input image is first denoised and enhanced. We use Total Variation (TV) technique to remove the noise and improve the Signal to Noise Ratio (SNR) of the input image. To further enhance the image for outdoor applications a foggy image enhancement technique is introduced which significantly benefits traffic and outdoor visual systems. Foggy image enhancement is an important branch of digital image processing, which is used when the weather is foggy. To overcome the shortcomings of the existing foggy image enhancement algorithms, we have developed a method that combines Principal Component Analysis (PCA), Multi-Scale Retinex (MSR) and Global Histogram Equalization (GHE). Initially, a PCA transform is applied to the foggy image to split the input image into a luminance and two chrominance components. In the second step, the luminance and the chrominance components are individually enhanced by MSR and GHE, respectively. In the final stage, an inverse PCA is applied to combine the results of the three channels into a new RGB image. To detect and track low observable targets in a digital image sequence. an encoding scheme along with genetic operation is designed to track the targets. To avoid missing any tracks, individual preservation method is introduced to maintain the more promising candidate tracks. Target trajectories are then confirmed by a multi-stage hypothesis testing scheme.