2,374 research outputs found

    Edge detection in multispectral images using the n-dimensional self-organizing map

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    We propose a new method for performing edge detection in multi-spectral images based on the self-organizing map (SOM) concept. Previously, 1-dimensional or 2-dimensional SOMs were trained to provide a linear mapping of high-dimensional multispectral vectors. Then, edge detection was applied on that mapping. However, the 1-dimensional SOM may not converge on a suitable global order for images with rich content. Likewise, the 2-dimensional SOM intro-duces false edges due to linearization artifacts. Our method feeds the edge detector without linearization. Instead, it exploits directly the distances of SOM neurons. This avoids the aforementioned draw-backs and is more general, as a SOM of arbitrary dimensionality can be used. We show that our method achieves significantly bet-ter edge detection results than previous work on a high-resolution multispectral image database. Index Terms — Multispectral imaging, Image edge detection, Self organizing feature maps, Machine Visio

    Model-Based Edge Detector for Spectral Imagery Using Sparse Spatiospectral Masks

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    Two model-based algorithms for edge detection in spectral imagery are developed that specifically target capturing intrinsic features such as isoluminant edges that are characterized by a jump in color but not in intensity. Given prior knowledge of the classes of reflectance or emittance spectra associated with candidate objects in a scene, a small set of spectral-band ratios, which most profoundly identify the edge between each pair of materials, are selected to define a edge signature. The bands that form the edge signature are fed into a spatial mask, producing a sparse joint spatiospectral nonlinear operator. The first algorithm achieves edge detection for every material pair by matching the response of the operator at every pixel with the edge signature for the pair of materials. The second algorithm is a classifier-enhanced extension of the first algorithm that adaptively accentuates distinctive features before applying the spatiospectral operator. Both algorithms are extensively verified using spectral imagery from the airborne hyperspectral imager and from a dots-in-a-well midinfrared imager. In both cases, the multicolor gradient (MCG) and the hyperspectral/spatial detection of edges (HySPADE) edge detectors are used as a benchmark for comparison. The results demonstrate that the proposed algorithms outperform the MCG and HySPADE edge detectors in accuracy, especially when isoluminant edges are present. By requiring only a few bands as input to the spatiospectral operator, the algorithms enable significant levels of data compression in band selection. In the presented examples, the required operations per pixel are reduced by a factor of 71 with respect to those required by the MCG edge detector

    A Novel Framework for Interactive Visualization and Analysis of Hyperspectral Image Data

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    Self-organizing maps for texture classification

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    Histopathological image analysis : a review

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    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
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