Nonlinear Adaptive Diffusion Models for Image Denoising

Most of digital image applications demand on high image quality. Unfortunately, images often are degraded by noise during the formation, transmission, and recording processes. Hence, image denoising is an essential processing step preceding visual and automated analyses. Image denoising methods can reduce image contrast, create block or ring artifacts in the process of denoising. In this dissertation, we develop high performance non-linear diffusion based image denoising methods, capable to preserve edges and maintain high visual quality. This is attained by different approaches: First, a nonlinear diffusion is presented with robust M-estimators as diffusivity functions. Secondly, the knowledge of textons derived from Local Binary Patterns (LBP) which unify divergent statistical and structural models of the region analysis is utilized to adjust the time step of diffusion process. Next, the role of nonlinear diffusion which is adaptive to the local context in the wavelet domain is investigated, and the stationary wavelet context based diffusion (SWCD) is developed for performing the iterative shrinkage. Finally, we develop a locally- and feature-adaptive diffusion (LFAD) method, where each image patch/region is diffused individually, and the diffusivity function is modified to incorporate the Inverse Difference Moment as a local estimate of the gradient. Experiments have been conducted to evaluate the performance of each of the developed method and compare it to the reference group and to the state-of-the-art methods

On-board three-dimensional object tracking: Software and hardware solutions

We describe a real time system for recognition and tracking 3D objects such as UAVs, airplanes, fighters with the optical sensor. Given a 2D image, the system has to perform background subtraction, recognize relative rotation, scale and translation of the object to sustain a prescribed topology of the fleet. In the thesis a comparative study of different algorithms and performance evaluation is carried out based on time and accuracy constraints. For background subtraction task we evaluate frame differencing, approximate median filter, mixture of Gaussians and propose classification based on neural network methods. For object detection we analyze the performance of invariant moments, scale invariant feature transform and affine scale invariant feature transform methods. Various tracking algorithms such as mean shift with variable and a fixed sized windows, scale invariant feature transform, Harris and fast full search based on fast fourier transform algorithms are evaluated. We develop an algorithm for the relative rotations and the scale change calculation based on Zernike moments. Based on the design criteria the selection is made for on-board implementation. The candidate techniques have been implemented on the Texas Instrument TMS320DM642 EVM board. It is shown in the thesis that 14 frames per second can be processed; that supports the real time implementation of the tracking system under reasonable accuracy limits

Real-time On-board Object Tracking for Cooperative Flight Control

One of possible cooperative Situations for flights could be a scenario when the decision on a new path is taken by A Certain fleet member, who is called the leader. The update on the new path is Transmitted to the fleet members via communication That can be noisy. An optical sensor can be used as a back-up for re-estimating the path parameters based on visual information. For A Certain topology, the issue can be solved by continuous tracking of the leader of the fleet in the video sequence and re-adjusting parameters of the flight, accordingly. To solve such a problem of a real time system has been developed for Recognizing and tracking 3D objects. Any change in the 3D position of the leading object is Determined by the on-board system and adjustments of the speed, pitch, yaw and roll angles are made to sustain the topology. Given a 2D image acquired by an on-board camera, the system has to perform the background subtraction, recognize the object, track it and evaluate the relative rotation, scale and translation of the object. In this paper, a comparative study of different algorithms is Carried out based on time and accuracy constraints. The solution for 3D pose estimation is provided based on the system of invariant Zernike moments. The candidate techniques solving the complete set of procedures have been Implemented on Texas Instruments TMS320DM642 EVM board. It is shown That 14 frames per second can be processed; That supports the real time Implementation of the tracking system with the reasonable accuracy

Image Denoising Based on Adaptive nonlinear Diffusion in Wavelet Domain

In this paper, we propose a context adaptive nonlinear diffusion method for image denoising in wavelet domain which we call context based diffusion in stationary wavelet domain (SWCD). In diffusing detail coefficients, the method adapts to the local context such that strong edges are preserved and smooth regions are diffused in a greater extent. The local context which is derived directly from the transform energies at scales 1 and 2 of two-level stationary wavelet transform (SWT) controls the diffusion. The shift invariance of SWT contributes to the performance of the method. The experiment is conducted on a number of benchmark images and compared to recently developed denoising methods which explore the adaptation concept for wavelet shrinkage and diffusion. A comparison is performed also to a method of diffusing both approximation and detail coefficients. The proposed SWCD method outperforms recently proposed adaptive shrinkage and adaptive diffusion, particularly at high noise levels. The method is computationally efficient due to the Haar wavelet and fast convergence attained due to exploiting the context information

Reliability Assessment of Microarray Data Using Fuzzy Classification Methods: A Comparative Study

Microarrays have become the tool of choice for the global analysis of gene expression. Powerful data acquisition systems are now available to produce massive amounts of genetic data. However, the resultant data consists of thousands of points that are error-prone, which in turn results in erroneous biological conclusions. In this paper, a comparative study of the performance of fuzzy clustering algorithms i.e. Fuzzy C-Means, Fuzzy C-medoid, Gustafson and Kessel, Gath Geva classification, Fuzzy Possibilistic C-Means and Kernel based Fuzzy C-Means is carried out to separate microarray data into reliable and unreliable signal intensity populations. The performance criteria used in the evaluation of the classification algorithm deal with reliability, complexity and agreement rate with that of Normal Mixture Modeling. It is shown that Kernel Fuzzy C-Means classification algorithms appear to be highly sensitive to the selection of the values of the kernel parameters

A new Method of Detecting Microcalcification Clusters for Computer Aided Digital Mammography

Digital mammograms are processed for detecting microcalcification clusters (MCCs) and prompting radiologists on their locations without specifying their type, i.e., benign, normal or malignant. The method includes image segmentation using SUSAN edge detector followed by the shape filters. Then the objects are classified with a four-level feed-forward Neural Network with four input features comprising perimeter and other three characterizing foreground-background relation. MCCs are found using the distance and the object count spatial filters. This simple yet robust system is capable to detect MC clusters with 98.4% of true positives at no false positive cases. The trial is performed on 118 mammograms from the DDSM database. It is shown in the paper that the reported performance is achieved due to the outstanding property of the edge detector to capture objects in a closed contour fashion; an efficient classifier, and significant features characterizing MCCs\u27 geometry and intensities with respect to the background

Advantages and Challenges of Radioscopic Detection of Nuclear Materials in Cargo Containers with Two Megavoltage Energy Barriers

Megavoltage X-ray technology is utilized to detect fissile materials that can be smuggled by terrorists among commercial goods in cargo containers. Material discrimination with dual energy barriers is based on a ratio of penetration levels at respective energies. However, for a broad bremsstrahlung spectrum, the approach is not reliable because of its sensitivity to mass thickness. Furthermore, cargo containers usually have combinations of materials in a stack that further complicates material identification. It is imperative to study the capability of dual mega-electron-volt energy radioscopy to detect materials of interest for its practical application at customs. The time to perform this inspection automatically and the need to manually open the container for examination are to be minimized for the smooth transport of goods through the national border. In this work, Linatron K9, developed and manufactured by Varian Inc., Inspection and Security Products, is used for experimentation. By switching 6- and 9-MeV beams, an interlaced penetration response is obtained. The automated detection of materials of high atomic numbers in the stack of materials is performed by proposed adaptive thresholding algorithm. The evaluation of the system based on a worst case scenario shows that the system meets requirements defined in the congressional report in terms of true and false positive identification rates, smallest object resolution, and the processing time

Radioscopic Inspection of Cargo Containers with Megavoltage Energy Barriers

To perform the inspection of cargo containers the radioscopic screening is performed by switching between 6 and 9 MeV of boundary energies as rapidly as 200 times per second and measuring the penetration levels in the contents of cargo. This technology facilitates the material identification via the analyses of the ratios of signals obtained at nominal and dual energies, which are 6 and 9 MeV, respectively. The techniques are developed for (a) visualizing the contents to produce an image suitable for fast inspection by a human operator, and for (b) prompting the custom personnel about the location of suspicious objects. Specifically, nuclear materials are of interest. The experiments have been conducted with Linatron K9 device designed by Varian Security and Inspection Products. The capabilities are demonstrated for detection of objects of interests for the steel shields of 10 inches of thickness