On-Line Object Feature Extraction for Multispectral Scene Representation

This thesis investigates a new on-line unsupervised object-feature extraction method that reduces the complexity and costs associated with the analysis of the multispectral image data and the data transmission, storage, archival and distribution as well. Typically in remote sensing a scene is represented by the spatially disjoint pixel-oriented features. It would appear possible to reduce data redundancy by an on-line unsupervised object-feature extraction process, where combined spatial-spectral object\u27s features, rather than the original pixel-features, are used for multispectral scene representation. The ambiguity in the object detection process can be reduced if the spatial dependencies, which exist among the adjacent pixels, are intelligently incorporated into the decision making process. We define the unity relation that must exist among the pixels of an object. The unity relation can be constructed with regard to the: adjacency relation, spectral-feature and spatial-feature characteristics in an object; e.g. AMICA (Automatic Multispectral Image Compaction Algorithm) uses the within object pixel feature gradient vector as a valuable contextual information to construct the object\u27s features, which preserve the class separability information within the data. For on-line object extraction, we introduce the path-hypothesis, and the basic mathematical tools for its realization are introduced in terms of a specific similarity measure and adjacency relation. AMICA is an example of on-line preprocessing algorithm that uses unsupervised object feature extraction to represent the information in the multispectral image data more efficiently. As the data are read into the system sequentially, the algorithm partitions the observation space into an exhaustive set of disjoint objects simultaneously with the data acquisition process, where, pixels belonging to an object form a path-segment in the spectral space. Each path-segment is characterized by an object-feature set. Then, the set of object-features, rather than the original pixel-features, is used for data analysis and data classification. AMICA is applied to several sets of real image data, and the performance and reliability of features is evaluated. Example results show an average compaction coefficient of more than 20/1 (this factor is data dependent). The classification performance is improved slightly by using object-features rather than the original data, and the CPU time required for classification is reduced by a factor of more than 20 as well. The feature extraction process may be implemented in real time, thus the object-feature extraction CPU time is neglectable; however, in the simulated satellite environment the CPU time for this process is less than 15% of CPU time for original data classification

Computerised stereoscopic measurement of the human retina

The research described herein is an investigation into the problems of obtaining useful clinical measurements from stereo photographs of the human retina through automation of the stereometric procedure by digital stereo matching and image analysis techniques. Clinical research has indicated a correlation between physical changes to the optic disc topography (the region on the retina where the optic nerve enters the eye) and the advance of eye disease such as hypertension and glaucoma. Stereoscopic photography of the human retina (or fundus, as it is called) and the subsequent measurement of the topography of the optic disc is of great potential clinical value as an aid in observing the pathogenesis of such disease, and to this end, accurate measurements of the various parameters that characterise the changing shape of the optic disc topography must be provided. Following a survey of current clinical methods for stereoscopic measurement of the optic disc, fundus image data acquisition, stereo geometry, limitations of resolution and accuracy, and other relevant physical constraints related to fundus imaging are investigated. A survey of digital stereo matching algorithms is presented and their strengths and weaknesses are explored, specifically as they relate to the suitability of the algorithm for the fundus image data. The selection of an appropriate stereo matching algorithm is discussed, and its application to four test data sets is presented in detail. A mathematical model of two-dimensional image formation is developed together with its corresponding auto-correlation function. In the presense of additive noise, the model is used as a tool for exploring key problems with respect to the stereo matching of fundus images. Specifically, measures for predicting correlation matching error are developed and applied. Such measures are shown to be of use in applications where the results of image correlation cannot be independently verified, and meaningful quantitative error measures are required. The application of these theoretical tools to the fundus image data indicate a systematic way to measure, assess and control cross-correlation error. Conclusions drawn from this research point the way forward for stereo analysis of the optic disc and highlight a number of areas which will require further research. The development of a fully automated system for diagnostic evaluation of the optic disc topography is discussed in the light of the results obtained during this research