Evaluation of entropy and JM-distance criterions as features selection methods using spectral and spatial features derived from LANDSAT images

A study area near Ribeirao Preto in Sao Paulo state was selected, with predominance in sugar cane. Eight features were extracted from the 4 original bands of LANDSAT image, using low-pass and high-pass filtering to obtain spatial features. There were 5 training sites in order to acquire the necessary parameters. Two groups of four channels were selected from 12 channels using JM-distance and entropy criterions. The number of selected channels was defined by physical restrictions of the image analyzer and computacional costs. The evaluation was performed by extracting the confusion matrix for training and tests areas, with a maximum likelihood classifier, and by defining performance indexes based on those matrixes for each group of channels. Results show that in spatial features and supervised classification, the entropy criterion is better in the sense that allows a more accurate and generalized definition of class signature. On the other hand, JM-distance criterion strongly reduces the misclassification within training areas

Image segmentation by iterative parallel region growing with application to data compression and image analysis

Image segmentation can be a key step in data compression and image analysis. However, the segmentation results produced by most previous approaches to region growing are suspect because they depend on the order in which portions of the image are processed. An iterative parallel segmentation algorithm avoids this problem by performing globally best merges first. Such a segmentation approach, and two implementations of the approach on NASA's Massively Parallel Processor (MPP) are described. Application of the segmentation approach to data compression and image analysis is then described, and results of such application are given for a LANDSAT Thematic Mapper image

CAMERA TRACKING

The Camera Tracking project is a system that captures images and tracks objects. The capturing of the image is done through a web-camera connected to a computer that runs the program of image capture and tracking. This project is applicable in various fields. It offers a low cost small surveillance system. The objective of the project is to develop a system that uses a web-camera that can also act as a surveillance camera that helps to monitor a desired object. The project deals with development of algorithm through the MATLAB software. The basic principle of the project is the phase correlation principle where the phase shift in the frequency domain is equivalent to the shift ofthe object in the spatial domain. The shift of the object is represented as a peak location at the phase correlation map. The two dimensional discrete Fourier Transform is used to decrease the number of computation in the digital signal analysis. Other image processing routine includes the Fast Fourier Transform and also Inverse Fast Fourier Transform

Figure-Ground Segmentation Using Multiple Cues

The theme of this thesis is figure-ground segmentation. We address the problem in the context of a visual observer, e.g. a mobile robot, moving around in the world and capable of shifting its gaze to and fixating on objects in its environment. We are only considering bottom-up processes, how the system can detect and segment out objects because they stand out from their immediate background in some feature dimension. Since that implies that the distinguishing cues can not be predicted, but depend on the scene, the system must rely on multiple cues. The integrated use of multiple cues forms a major theme of the thesis. In particular, we note that an observer in our real environment has access to 3-D cues. Inspired by psychophysical findings about human vision we try to demonstrate their effectiveness in figure-ground segmentation and grouping also in machine vision

Image analysis, modeling, enhancement, restoration, feature extraction and their applications in nondestructive evaluation and radio astronomy

The principal topic of this dissertation is the development and application of signal and image processing to Nondestructive Evaluation (NDE) and radio astronomy;The dissertation consists of nine papers published or submitted for publication. Each of them has a specific and unique topic related to signal processing or image processing in NDE or radio astronomy. Those topics are listed in the following. (1) Time series analysis and modeling of Very Large Array (VLA) phase data. (2) Image analysis, feature extraction and various applied enhancement methods for industrial NDE X-ray radiographic images. (3) Enhancing NDE radiographic X-ray images by adaptive regional Kalman filtering. (4) Robotic image segmentation, modeling, and restoration with a rule based expert system. (5) Industrial NDE radiographic X-ray image modeling and Kalman filtering considering signal-dependent colored noise. (6) Computational study of Kalman filtering VLA phase data and its computational performance on a supercomputer. (7) A practical and fast maximum entropy deconvolution method for deblurring industrial NDE X-ray and infrared images. (8) Local feature enhancement of synthetic radio images by adaptive Kalman filtering. (9) A new technique for correcting phase data of a synthetic-aperture antenna array

Use of remote sensing for land use policy formulation

The overall objectives and strategies of the Center for Remote Sensing remain to provide a center for excellence for multidisciplinary scientific expertise to address land-related global habitability and earth observing systems scientific issues. Specific research projects that were underway during the final contract period include: digital classification of coniferous forest types in Michigan's northern lower peninsula; a physiographic ecosystem approach to remote classification and mapping; land surface change detection and inventory; analysis of radiant temperature data; and development of methodologies to assess possible impacts of man's changes of land surface on meteorological parameters. Significant progress in each of the five project areas has occurred. Summaries on each of the projects are provided

Colour map image segmentation based on supervised and unsupervised learning techniques

Image segmentation is a very important stage in any image analysis or computer vision system. Map images are considered to be among the most complex of images. The segmentation of colour map images is a difficult problem. In this thesis, four segmentation techniques are presented to extract characters and lines from colour geographic map images. There are: conventional adaptive thresholding, the supervised-learning neural network, the unsupervised fuzzy c—means clustering and nearest-prototype rule, and the combined supervised and unsupervised techniques. In the conventional adaptive thresholding technique, images are divided into subimages. For each bimodal histogram subimage, a threshold is located at the valley of the histogram using an automated histogram analysis technique. A threshold value is obtained for each pixel of the image by interpolation of the thresholds. The image is then segmented by the different thresholds at each pixel. In the supervised-learning neural network based technique, a neural network is first trained with feature values using known character and line pixels and background pixels, and is then used for classification. The image segmentation problem is treated as a pattern classification process and the neural network classifier is used to generate non—linear decision regions to separate the foreground and background of an image that containing a number of nonuniform regions with different colours. In the unsupervised fuzzy clustering and nearest-prototype rule based technique, segmentation is also considered as a process of pixel classification. A set of prototypes is generated using the fuzzy c—means clustering algorithm on the training areas selected from different colour map images, and each pixel of the image is classified into character and line class or background class according to the nearest—prototype rule. In the combined supervised and unsupervised technique, training samples are generated by the unsupervised fuzzy clustering technique applied to subimages and by randomly choosing pixels in the low contrast areas. A supervised learning based multi-layer neural network is trained for classifying character and line pixels and background pixels. These four techniques are applied to many colour geographic map images containing English, Japanese and Chinese characters with different printing styles. The conventional adaptive threshold technique does not work well. The proposed supervised and unsupervised techniques have achieved satisfactory segmentation results although some very low contrast areas require improvement in the unsupervised technique. The combined technique is a way of enchancing the performance of the supervised technique, and it has yielded good segmentation results

An approach towards standardising vulnerability categories

Computer vulnerabilities are design flaws, implementation or configuration errors that provide a means of exploiting a system or network that would not be available otherwise. The recent growth in the number of vulnerability scanning (VS) tools and independent vulnerability databases points to an apparent need for further means to protect computer systems from compromise. It is important for these tools and databases to interpret, correlate and exchange a large amount of information about computer vulnerabilities in order to use them effectively. However, this goal is hard to achieve because the current VS products differ extensively both in the way that they can detect vulnerabilities and in the number of vulnerabilities that they can detect. Each tool or database represents, identifies and classifies vulnerabilities in its own way, thus making them difficult to study and compare. Although the list of Common Vulnerabilities and Exposures (CVE) provides a means of solving the disparity in vulnerability names used in the different VS products, it does not standardise vulnerability categories. This dissertation highlights the importance of having a standard vulnerability category set and outlines an approach towards achieving this goal by categorising the CVE repository using a data-clustering algorithm. Prototypes are presented to verify the concept of standardizing vulnerability categories and how this can be used as the basis for comparison of VS products and improving scan reports.Dissertation (MSc (Computer Science))--University of Pretoria, 2008.Computer Scienceunrestricte

Segmentation of neuroanatomy in magnetic resonance images

Segmentation in neurological Magnetic Resonance Imaging (MRI) is necessary for volume measurement, feature extraction and for the three-dimensional display of neuroanatomy. This thesis proposes several automated and semi-automated methods which offer considerable advantages over manual methods because of their lack of subjectivity, their data reduction capabilities, and the time savings they give. Work has concentrated on the use of dual echo multi-slice spin-echo data sets in order to take advantage of the intrinsically multi-parametric nature of MRI. Such data is widely acquired clinically and segmentation therefore does not require additional scans. The literature has been reviewed. Factors affecting image non-uniformity for a modem 1.5 Tesla imager have been investigated. These investigations demonstrate that a robust, fast, automatic three-dimensional non-uniformity correction may be applied to data as a pre-processing step. The merit of using an anisotropic smoothing method for noisy data has been demonstrated. Several approaches to neurological MRI segmentation have been developed. Edge-based processing is used to identify the skin (the major outer contour) and the eyes. Edge-focusing, two threshold based techniques and a fast radial CSF identification approach are proposed to identify the intracranial region contour in each slice of the data set. Once isolated, the intracranial region is further processed to identify CSF, and, depending upon the MRI pulse sequence used, the brain itself may be sub-divided into grey matter and white matter using semiautomatic contrast enhancement and clustering methods. The segmentation of Multiple Sclerosis (MS) plaques has also been considered. The utility of the stack, a data driven multi-resolution approach to segmentation, has been investigated, and several improvements to the method suggested. The factors affecting the intrinsic accuracy of neurological volume measurement in MRI have been studied and their magnitudes determined for spin-echo imaging. Geometric distortion - both object dependent and object independent - has been considered, as well as slice warp, slice profile, slice position and the partial volume effect. Finally, the accuracy of the approaches to segmentation developed in this thesis have been evaluated. Intracranial volume measurements are within 5% of expert observers' measurements, white matter volumes within 10%, and CSF volumes consistently lower than the expert observers' measurements due to the observers' inability to take the partial volume effect into account

APPLICATION OF IMAGE ANALYSIS TECHNIQUES TO SATELLITE CLOUD MOTION TRACKING

Cloud motion wind (CMW) determination requires tracking of individual cloud targets. This is achieved by first clustering and then tracking each cloud cluster. Ideally, different cloud clusters correspond to diiferent pressure levels. Two new clustering techniques have been developed for the identification of cloud types in multi-spectral satellite imagery. The first technique is the Global-Local clustering algorithm. It is a cascade of a histogram clustering algorithm and a dynamic clustering algorithm. The histogram clustering algorithm divides the multi-spectral histogram into'non-overlapped regions, and these regions are used to initialise the dynamic clustering algorithm. The dynamic clustering algorithm assumes clusters have a Gaussian distributed probability density function with diiferent population size and variance. The second technique uses graph theory to exploit the spatial information which is often ignored in per-pixel clustering. The algorithm is in two stages: spatial clustering and spectral clustering. The first stage extracts homogeneous objects in the image using a family of algorithms based on stepwise optimization. This family of algorithms can be further divided into two approaches: Top-down and Bottom-up. The second stage groups similar segments into clusters using a statistical hypothesis test on their similarities. The clusters generated are less noisy along class boundaries and are in hierarchical order. A criterion based on mutual information is derived to monitor the spatial clustering process and to suggest an optimal number of segments. An automated cloud motion tracking program has been developed. Three images (each separated by 30 minutes) are used to track cloud motion and the middle image is clustered using Global-Local clustering prior to tracking. Compared with traditional methods based on raw images, it is found that separation of cloud types before cloud tracking can reduce the ambiguity due to multi-layers of cloud moving at different speeds and direction. Three matching techniques are used and their reliability compared. Target sizes ranging from 4 x 4 to 32 x 32 are tested and their errors compared. The optimum target size for first generation METEOSAT images has also been found.Meteorological Office, Bracknel