The evaluation of 3D scenes observed from different sensors requires the co-registration of sensor images and the reconstruction of the 3D geometry. To solve both tasks the presented system exploits prior knowledge, r...
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The evaluation of 3D scenes observed from different sensors requires the co-registration of sensor images and the reconstruction of the 3D geometry. To solve both tasks the presented system exploits prior knowledge, represented explicitly by semantic nets, and uses a digital landscape model of a geographic information system (GIS) as a hint for the object location. This is shown for the detection of control points for image registration and the extraction of objects (roads, buildings) for 3D reconstruction. For real time visualization the 3D geometry is approximated by a polygon mesh with overlaid photo texture.
Biological and technical autonomous agents have to achieve basic behaviors of navigation and obstacle avoidance. If they are confined to a monocular visual sensor the optical flow field induced by egomotion can be eva...
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Biological and technical autonomous agents have to achieve basic behaviors of navigation and obstacle avoidance. If they are confined to a monocular visual sensor the optical flow field induced by egomotion can be evaluated in the periphery for speed and direction control, whereas the central flow indicates imminent collisions. We show how a complex-logarithmic mapping of the image is especially apt for the combined evaluation of the flow field for these navigational tasks. We implemented a robot simulation tool to test reliability, speed, control and robustness of our scheme.
This work is concerned with lake bed categorization in terms of surficial substrates using remotesensing data. A single beam echosounder coupled with a RoxAnn/sup TM/ bottom classification sensor were used. To improv...
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This work is concerned with lake bed categorization in terms of surficial substrates using remotesensing data. A single beam echosounder coupled with a RoxAnn/sup TM/ bottom classification sensor were used. To improve the current design and to better interpret the output signal from RoxAnn, several bottom classifiers were developed by using statistical methods. Although the methods utilized belong to an off-line procedure, the classifiers obtained are applicable to online isolation and useful for automated classification of underlake terrain. The application considered is the finding of areas suitable for trout spawning on the Minnesota shoreline of Lake Superior.
This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired ...
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This paper describes a technique for the reconstruction and segmentation of three-dimensional acoustical images using a coupled Random Fields able to actively integrate confidence information associated with acquired data. Beamforming, a method widely applied in acoustic imaging, is used to build a three-dimensional image, associated point by point with another kind of information representing the reliability (i.e. "confidence") of such an image. Unfortunately, this kind of images is plagued by several problems due to the nature of the signal and to the related sensing system, thus heavily affecting data quality. Specifically, speckle noise and the broad directivity characteristic of the sensor lead to very degraded images. In the proposed algorithm, range and confidence images are modelled as Markov Random Fields whose associated probability distributions are specified by a single energy functional. A three-fold process has been applied able to reconstruct, segment, and restore the involved acoustic images exploiting both types of data. Our approach showed better performances with respect to other MRF-based methods as well as classical methods disregarding reliability information. Optimal (in the Maximum A-Posteriori probability sense) estimates of the 3D and confidence images are obtained by minimizing the energy functional by using simulated annealing.
This high-resolution, low-power direct-contact capacitive sensor using standard CMOS front-end processing exhibits high sensitivity while maintaining an effective barrier to chemical, physical and electrostatic intrus...
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This high-resolution, low-power direct-contact capacitive sensor using standard CMOS front-end processing exhibits high sensitivity while maintaining an effective barrier to chemical, physical and electrostatic intrusion. The sensor uses direct finger contact with the surface of the sensor IC to capture a capacitive fingerprint image. The sensor consists of a 2-D array of metal plates capped with a thin dielectric layer. Unlike previous designs, each sensing site uses one metal sensor plate. Each functions as capacitor bottom plate, with the finger surface acting as the grounded top plate. Distance between the finger and the sensor and hence the measured capacitance varies with the pattern of ridges and valleys in the fingerprint. The capacitance is "measured" as the change in voltage that results when a fixed charge is removed from each sensing plate.
In this paper, we propose a pattern classification method for remotesensing data using both a neural network and knowledge-based processing. A neural network has the ability to recognize complex patterns, and classif...
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In this paper, we propose a pattern classification method for remotesensing data using both a neural network and knowledge-based processing. A neural network has the ability to recognize complex patterns, and classifies them to one of the classes. However, the neural network might produce misclassification. A knowledge-based system which uses human geographical knowledge improves the classification results, compared with a conventional statistical method. The disadvantage of using a knowledge-based system is that it needs a large amount of knowledge to classify the data correctly. We propose a pattern classification method that integrates the advantages of both the neural network and knowledge-based system. The proposed system is divided into two subsystems which consist of recognition and error correction. We use the neural network for classification and the knowledge-based system for correcting misclassification created by the neural network. Experimental results are shown to illustrate the performance of the proposed system.
作者：
Paillou, PLaboratory of Chromatography
DEPg.Fac.Quimica Universidad Nacional Autonoma de Mexico Circuito interior Cd Universitaria/CP 04510 Mexico D.F.Mexico
We present in this communication a new linear edge detector that is very well suited to process noisy images. Performances of the new operator have been evaluated using Canny's criteria together with Experimental ...
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We present in this communication a new linear edge detector that is very well suited to process noisy images. Performances of the new operator have been evaluated using Canny's criteria together with Experimental comparison results on a noisy SAR image.
作者：
Schwarz, GDatcu, MDLR
German Remote Sensing Data Ctr German Aerosp Res Estab DFD D-82234 Oberpfaffenhofen Germany
During the last years, wavelets have become very popular in the fields of signal processing and patternrecognition and have led to a large number of publications. In the discipline of remotesensing several applicati...
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ISBN:
(纸本)0819426490
During the last years, wavelets have become very popular in the fields of signal processing and patternrecognition and have led to a large number of publications. In the discipline of remotesensing several applications of wavelets have emerged, too. Among them are such diverse topics as image data compression, image enhancement, feature extraction, and detailed data analysis. On the other hand, the processing of remotesensingimage data-both for optical and radar data-follows a well-known systematic sequence of correction and data management steps supplemented by dedicated image enhancement and data analysis activities. In the following we will demonstrate where wavelets and wavelet transformed data can be used advantageously within the standard processing chain usually applied to remotesensingimage data. Summarizing potential wavelet applications for remotesensingimage data, we conclude that wavelets offer a variety of new perspectives especially for image coding, analysis, classification, archiving, and enhancement. However, applications requiring geometrical corrections and separate dedicated representation bases will probably remain a stronghold of classical image domain processing techniques.
A multi-channel image segmentation method is discussed that utilizes a Markov random field (MRF) region label model with adaptive neighbourhoods. Bayesian inference is applied to realize the combination of evidence fr...
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A multi-channel image segmentation method is discussed that utilizes a Markov random field (MRF) region label model with adaptive neighbourhoods. Bayesian inference is applied to realize the combination of evidence from different knowledge sources. In such a way, optimization of the shape of a neighbourhood set is achieved by following a criterion that makes use of hypothesis on the Markovian property by exploiting the local image content. The purpose of the article is to show the theoretical validity of the approach by elucidating correspondences and differences with a similar concept. Results are shown using optical remotesensing data. (C) 1997 Elsevier Science B.V.
In the framework of the European Community programme Training and Mobility for Researchers, the project Analysis and Segmentation of remote-sensingimages for Land-Cover mapping has been proposed and approved. This ar...
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