The performance of two-dimensional image processing systems has been well documented. In this thesis we study the performance of three-dimensional imaging systems. Such a study helps in understanding the fundamental restrictions of the propagation of three-dimensional (3-D) wavefields. The knowledge of the obtainable 3-D wave structures are useful for applications such as 3-D data acquisition, material processing, radiation therapy, radiative non-invasive surgery, 3-D microscopy and robotic vision. This thesis primarily deals with some investigations of 3-D optical transfer functions (OTFs). Specific emphasis has been made on the interpretation of 3-D wavefield distributions as an extension of 2-D defocused OTFs and also the interpretations of 3-D diffraction images from convolution relations. / Master of Science

David J. Rossi. / "17 February 1983" / Bibliography: leaf [3]

Just as image processing and image data bases have moved out of the lab and into the office environment, so has the need for image enhancement. Image scanners must to be able to capture and store a wide variety of information including faded documents, carbon copies, signatures, postmarks, etc. OCR systems put further demands on scanned image quality in terms of low noise, and unbroken disconnected characters. Straight thresholding techniques do not always meet the performance requirements, but by applying simple image processing techniques some of these problems can be solved. However, more burden is placed on the users to control the image enhancement techniques. The users, most of whom have little technical background, want no part in adjusting parameters. This paper proposes a method of examining small windows of the image to derive parameter settings autonomously. Histograms allow rudimentary measures to be used in setting parameters for edge detection, non-linear filters, and point operators such as non-linear gray scale mapping. Some examples of automatic parameter setting are given in chapter three. / Master of Science

Indiana University-Purdue University Indianapolis (IUPUI) / Multiresolution image fusion is an emerging area of research for use in military and commercial applications. While many methods for image fusion have been developed, improvements can still be made. In many cases, image fusion methods are tailored to specific applications and are limited as a result. In order to make improvements to general image fusion, novel methods have been developed based on the wavelet transform and empirical variance. One particular novelty is the use of directional filtering in conjunction with wavelet transforms. Instead of treating the vertical, horizontal, and diagonal sub-bands of a wavelet transform the same, each sub-band is handled independently by applying custom filter windows. Results of the new methods exhibit better performance across a wide range of images highlighting different situations.

Indiana University-Purdue University Indianapolis (IUPUI) / Recently there has been a lot of effort in developing systems for sampling and automatically classifying plankton from the oceans. Existing methods assume the specimens have already been precisely segmented, or aim at analyzing images containing single specimen (extraction of their features and/or recognition of specimens as single targets in-focus in small images). The resolution in the existing systems is limiting. Our goal is to develop automated, very high resolution image sensing of critically important, yet under-sampled, components of the planktonic community by addressing both the physical sensing system (e.g. camera, lighting, depth of field), as well as crucial image extraction and recognition routines. The objective of this thesis is to develop a framework that aims at (i) the detection and segmentation of all organisms of interest automatically, directly from the raw data, while filtering out the noise and out-of-focus instances, (ii) extract the best features from images and (iii) identify and classify the plankton species. Our approach focusses on utilizing the full computational power of a multicore system by implementing a parallel programming approach that can process large volumes of high resolution plankton images obtained from our newly designed imaging system (In Situ Ichthyoplankton Imaging System (ISIIS)). We compare some of the widely used segmentation methods with emphasis on accuracy and speed to find the one that works best on our data. We design a robust, scalable, fully automated system for high-throughput processing of the ISIIS imagery.