This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed s...
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This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed system are described, namely the filtering and statistical modules, automatic feature vector optimization module and the classification module using clustering and fuzzy clustering methods. This texture classification system can easily be adapted for other tasks, including tasks in the field of medical imaging, remotesensing and quality control.
We propose a method for the automatic segmentation, recognition and measurement of neuronal fibers in microscopic images of nerves. This permits a quantitative analysis of the distribution of the areas of the fibers, ...
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ISBN:
(纸本)0819431338
We propose a method for the automatic segmentation, recognition and measurement of neuronal fibers in microscopic images of nerves. This permits a quantitative analysis of the distribution of the areas of the fibers, while nowadays such morphometrical methods are limited by the practical impossibility to process large amounts of fibers in histological routine. First, the image is thresholded to provide a coarse classification between myelin (black) and non-myelin (white) pixels. The resulting binary image is simplified using connected morphological operators. These operators simplify the zonal graph, whose vertices are the connected areas of the binary image. An appropriate set of semantic rules allow us to identify a number of white areas as axon candidates, some of which are isolated, some of which are connected. To separate connected fibers - candidates sharing the same neighboring black area - we evaluate the thickness of the myelin ring around each candidate area through Euclidean distance transformation by propagation with a stopping criterion on the pixels in the propagation front. Finally, properties of each detected fibers are computed and false alarms are suppressed. The computational cost of the method is evaluated and the robustness of the method is assessed by comparison to the manual procedure. We conclude that the method is fast and accurate for our purpose.
Recently a number of airborne nadir scanning laser radars have been developed for both military and civilian applications. These have range resolutions on the order of 10 cm but relatively moderate area coverage rates...
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ISBN:
(纸本)0819431818
Recently a number of airborne nadir scanning laser radars have been developed for both military and civilian applications. These have range resolutions on the order of 10 cm but relatively moderate area coverage rates, in the range 1000-10000 m(2)/s (3.6-36 km(2)/h) when operating in a high resolution mode with 0.25 m spot distance. Technology development in laser sources, scanning techniques and signal processing will probably improve the area coverage substantially and lead to compact systems suitable for new applications, including the use in UAV:s. Present nadir capability could be combined with a forward looking capability for guidance and obstacle avoidance in autonomous or semi-autonomous systems. The paper will investigate the potential performance of such combined systems using state-of-the-art lasers and receiver technology. Among the applications for both military and civilian users we note the collection of 3-D data for terrain modeling and object recognition. For these functions signal processing using multiple echo and intensity information is of great value as well as adding passive sensor information. Full wave form processing will further improve the information for example to characterize trees. The use of high resolution 3-D data in synthetic environments is obvious and will be discussed. Experimental data collected with a commercial laser system, TopEye, developed by Saab Dynamics, will be shown and some image examples will be discussed in relation to different applications.
Fuzzy Models and Algorithms for patternrecognition and imageprocessing presents a comprehensive introduction of the use of fuzzy models in patternrecognition and selected topics in imageprocessing and computer vis...
ISBN:
(纸本)9780792385219
Fuzzy Models and Algorithms for patternrecognition and imageprocessing presents a comprehensive introduction of the use of fuzzy models in patternrecognition and selected topics in imageprocessing and computer vision. Unique to this volume in the Kluwer Handbooks of Fuzzy Sets Series is the fact that this book was written in its entirety by its four authors. A single notation, presentation style, and purpose are used throughout. The result is an extensive unified treatment of many fuzzy models for patternrecognition. The main topics are clustering and classifier design, with extensive material on feature analysis relational clustering, imageprocessing and computer vision. Also included are numerous figures, images and numerical examples that illustrate the use of various models involving applications in medicine, character and word recognition, remotesensing, military image analysis, and industrial engineering.
Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui,...
Changing land-cover in the tropics is a central issue in global change research. This dissertation used Landsat-TM data to examine processes of land-use and land-cover changes for a lowland tropical site in Sarapiqui, Costa Rica. Performances of selected image-processing methods to detect and identify land-cover changes were evaluated. A land-cover time-series from 1960 to 1996 for the site was generated using maps derived from aerial photographs and Landsat-TM classifications. Changes in land-cover from 1986 to 1996 were evaluated using standard landscape indices, and interpreted in terms of their historical context. Dominant changes in the site during this decade included the breakup of extensive cattle ranches for large-scale plantation enterprises and small-scale farming. Colonization processes, improvements in access, and changes in export markets were identified as the major driving forces of change. Evaluation of change-detection methods revealed that postclassification comparison performed significantly better than image differencing algorithms. image differencing using mid- infrared bonds performed the best of the differencing algorithms tested. Selection of a suitable change-detection method can be aided through examination of the individual bond statistics for the specific area and problem in question. The univariate bond differencing technique has potential for identification of 'hot spots' of change using Landsat-TM data. Spatial pattern-recognition techniques to characterize complexity of Landsat-TM data were evaluated. Fractal dimension calculated using the triangular prism surface area method, and Moran's I index of spatial autocorrelation, clearly distinguished different land-cover types. Shannon's diversity index and the contagion metric were not found to be useful in characterizing the images. The use of fractal dimension, in conjunction with standard non-spatial descriptive band statistics, are seen as having great potential in characterizing unc
Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. ...
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Mathematical morphology coupled with creation of a time stack image and principal oscillation pattern analysis are used to determine the water depths over a known sloping bottom from synthetic remotely sensed images. The data, produced by a simulator, consisted of 60 images, each 256/spl times/256 pixels, separated by 1 second in time. The mathematical depth results are compared with those derived using principal oscillation pattern analysis by which three significant complex pairs of patterns are found with their corresponding characteristic times: e-folding times and periods. From these results and by using the classical hydrodynamic theory of gravity waves, water depths are determined. Both analyses yield small errors for the simulated data, indicating both methods should perform reliably for real data.
We present a compact optical correlator with Internet access. Users can remotely download images and get the optically computed correlation results back on their monitor.
We present a compact optical correlator with Internet access. Users can remotely download images and get the optically computed correlation results back on their monitor.
We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing is done using analog circuits fabricated on the same die as the photosen...
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We describe focal plane image processors which filter images in space by filters which are similar to even and odd Gabor filters. All processing is done using analog circuits fabricated on the same die as the photosensors allowing combined sensing and processing at rates as high as 5000 frames per second. Both 1D and 2D sensors have been designed, fabricated and tested. In the 2D sensor the tuned orientation can be steered and the filter response scaled by adjusting external bias voltages controlling parameters such as the gains of the analog processing circuits. Because both even and odd Gabor-type filter outputs are calculated, sensors can be used in Gabor phase-based algorithms. As a simple example, we have embedded a 1D sensor on a mobile robot platform to perform image fixation.
We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multi...
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We present a new formalism for the treatment and understanding of multispectral images and multisensor imagery based on first order contrast information. Although little attention has been paid to the utility of multispectral contrast, we develop a theory for multispectral contrast that enables us to produce an optimal grayscale visualization of the first order contrast of an image with an arbitrary number of bands. We demonstrate how our technique can reveal significantly more interpretive information to an image analyst, who can use it in a number of image understanding algorithms. Existing grayscale visualization strategies are reviewed and a discussion is given as to why our algorithm is optimal and outperforms them. A variety of experimental results are presented.
This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed s...
详细信息
This paper describes the use of a complex modular imageprocessing system for texture classification. An introduction into problems that arise when handling textures is given. Furthermore the modules of the proposed system are described, namely the filtering and statistical modules, automatic feature vector optimization module and the classification module using clustering and fuzzy clustering methods. This texture classification system can easily be adapted for other tasks, including tasks in the field of medical imaging, remotesensing and quality control.
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