A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)

Histopathological image analysis : a review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe

Mental Structures

An ongoing philosophical discussion concerns how various types of mental states fall within broad representational genera—for example, whether perceptual states are “iconic” or “sentential,” “analog” or “digital,” and so on. Here, I examine the grounds for making much more specific claims about how mental states are structured from constituent parts. For example, the state I am in when I perceive the shape of a mountain ridge may have as constituent parts my representations of the shapes of each peak and saddle of the ridge. More specific structural claims of this sort are a guide to how mental states fall within broader representational kinds. Moreover, these claims have significant implications of their own about semantic, functional, and epistemic features of our mental lives. But what are the conditions on a mental state's having one type of constituent structure rather than another? Drawing on explanatory strategies in vision science, I argue that, other things being equal, the constituent structure of a mental state determines what I call its distributional properties—namely, how mental states of that type can, cannot, or must co‐occur with other mental states in a given system. Distributional properties depend critically on and are informative about the underlying structures of mental states, they abstract in important ways from aspects of how mental states are processed, and they can yield significant insights into the variegation of psychological capacities

Differential Effect of Contrast Polarity Reversals in Closed Squares and Open L-Junctions

Scene segmentation depends on interaction between geometrical and photometric factors. It has been shown that reversals in contrast polarity at points of highest orientation discontinuity along closed contours significantly impair shape discrimination performance, while changes in contrast polarity at straight(er) contour segments do not have such deleterious effects (Spehar, 2002). Here we employ (semi) high resolution fMRI (1.5 mm × 1.5 mm × 1.5 mm) to investigate the neuronal substrate underlying these perception effects. Stimuli consisted of simple elements (a) squares with contrast reversals along straight segments; (b) squares with contrast reversals in the corner (highest orientation discontinuity); (c) L-Junctions with contrast reversals along the straight ends; (d) L-Junctions with contrast reversals in the corner. Element with contrast polarity reversals are easy to distinguish though appear geometrically equivalent. For squares with contrast polarity reversals only along straight lines we find significantly lower BOLD modulation compared to any of the control conditions, which show similar responses to each other. In the light of previous psychophysical work (Elder and Zucker, 1993; Spehar, 2002) we speculate that this effect is due to closure perception. We observe this across a wide range of areas on occipital cortex

Differential Effect of Contrast Polarity Reversals in Closed Squares and Open L-Junctions

Scene segmentation depends on interaction between geometrical and photometric factors. It has been shown that reversals in contrast polarity at points of highest orientation discontinuity along closed contours significantly impair shape discrimination performance, while changes in contrast polarity at straight(er) contour segments do not have such deleterious effects (Spehar, 2002). Here we employ (semi) high resolution fMRI (1.5 mm × 1.5 mm × 1.5 mm) to investigate the neuronal substrate underlying these perception effects. Stimuli consisted of simple elements (a) squares with contrast reversals along straight segments; (b) squares with contrast reversals in the corner (highest orientation discontinuity); (c) L-Junctions with contrast reversals along the straight ends; (d) L-Junctions with contrast reversals in the corner. Element with contrast polarity reversals are easy to distinguish though appear geometrically equivalent. For squares with contrast polarity reversals only along straight lines we find significantly lower BOLD modulation compared to any of the control conditions, which show similar responses to each other. In the light of previous psychophysical work (Elder and Zucker, 1993; Spehar, 2002) we speculate that this effect is due to closure perception. We observe this across a wide range of areas on occipital cortex

Novel Application of Neutrosophic Logic in Classifiers Evaluated under Region-Based Image Categorization System

Neutrosophic logic is a relatively new logic that is a generalization of fuzzy logic. In this dissertation, for the first time, neutrosophic logic is applied to the field of classifiers where a support vector machine (SVM) is adopted as the example to validate the feasibility and effectiveness of neutrosophic logic. The proposed neutrosophic set is integrated into a reformulated SVM, and the performance of the achieved classifier N-SVM is evaluated under an image categorization system. Image categorization is an important yet challenging research topic in computer vision. In this dissertation, images are first segmented by a hierarchical two-stage self organizing map (HSOM), using color and texture features. A novel approach is proposed to select the training samples of HSOM based on homogeneity properties. A diverse density support vector machine (DD-SVM) framework that extends the multiple-instance learning (MIL) technique is then applied to the image categorization problem by viewing an image as a bag of instances corresponding to the regions obtained from the image segmentation. Using the instance prototype, every bag is mapped to a point in the new bag space, and the categorization is transformed to a classification problem. Then, the proposed N-SVM based on the neutrosophic set is used as the classifier in the new bag space. N-SVM treats samples differently according to the weighting function, and it helps reduce the effects of outliers. Experimental results on a COREL dataset of 1000 general purpose images and a Caltech 101 dataset of 9000 images demonstrate the validity and effectiveness of the proposed method

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

Integrating spatial and spectral information for automatic feature identification in high -resolution remotely sensed images

This research used image objects, instead of pixels, as the basic unit of analysis in high-resolution imagery. Thus, not only spectral radiance and texture were used in the analysis, but also spatial context. Furthermore, the automated identification of attributed objects is potentially useful for integrating remote sensing with a vector-based GIS.;A study area in Morgantown, WV was chosen as a site for the development and testing of automated feature extraction methods with high-resolution data. In the first stage of the analysis, edges were identified using texture. Experiments with simulated data indicated that a linear operator identified curved and sharp edges more accurately than square shaped operators. Areas with edges that formed a closed boundary were used to delineate sub-patches. In the region growing step, the similarities of all adjacent subpatches were examined using a multivariate Hotelling T2 test that draws on the classes\u27 covariance matrices. Sub-patches that were not sufficiently dissimilar were merged to form image patches.;Patches were then classified into seven classes: Building, Road, Forest, Lawn, Shadowed Vegetation, Water, and Shadow. Six classification methods were compared: the pixel-based ISODATA and maximum likelihood approaches, field-based ECHO, and region based maximum likelihood using patch means, a divergence index, and patch probability density functions (pdfs). Classification with the divergence index showed the lowest accuracy, a kappa index of 0.254. The highest accuracy, 0.783, was obtained from classification using the patch pdf. This classification also produced a visually pleasing product, with well-delineated objects and without the distracting salt-and-pepper effect of isolated misclassified pixels. The accuracies of classification with patch mean, pixel based maximum likelihood, ISODATA and ECHO were 0.735, 0.687, 0.610, and 0.605, respectively.;Spatial context was used to generate aggregate land cover information. An Urbanized Rate Index, defined based on the percentage of Building and Road area within a local window, was used to segment the image. Five summary landcover classes were identified from the Urbanized Rate segmentation and the image object classification: High Urbanized Rate and large building sizes, Intermediate Urbanized Rate and intermediate building sizes, Low urbanized rate and small building sizes, Forest, and Water