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NON-PARAMETRIC MULTISCALE CURVE SMOOTHING

International Journal of pattern recognition and Artificial Intelligence 1994年第6期8卷 1381-1406页

作者： PAUL L. ROSIN School of Computing Cognitive Systems Group Curtin University of Technology Perth 6001 Western Australia Current address: Institute for Remote Sensing Applications Terminale Postale 442 Joint Research Centre 1–21020 Ispra (VA) Italy.

Lowe 8 demonstrated a method for automatically segmenting and smoothing image curves by varying degrees. It was intended to remove noise and unnecessary fine detail, aiding subsequent processing such as grouping and matching. An alternative technique is described in this paper that is based on recursively subdividing the curve into alternative sets of sections. Rather than use thresholds on the values of curvature and its derivatives to determine the segmentation and degree of smoothing our technique is driven by three qualitative measures: (1) a criterion for selecting potential breakpoints, (2) a criterion for determining the amount of smoothing for curve sections, (3) a significance measure that determines which sections form the best selection. The advantages of the technique are robustness, scale invariance, and the absence of parameters.

关键词： Gaussian smoothing multiscale curve representation polynomial fitting natural scales significance measure segmentation tree

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Hand gesticulation interpretation via smart sensing

Hand gesticulation interpretation via smart sensing

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Asilomar conference on Signals, Systems & Computers

作者： O. Alsayegh N. Vujovic D. Brzakovic Department of Electrical Engineering and Computer Science Lehigh University Bethlehem PA USA Epartment of Electrical Engineering and Computer Science Lehigh University Bethlehem PA USA

This paper describes a part of a vision system that interprets hand gesticulation. The system consists of three sub-systems each of which comprises a number of modules targeted at performing specific functions. The modules contain adaptive image processing and pattern recognition algorithms. The first sub-system is a hand-motion tracker that determines hand trajectory in a 3-D space using a sequence of low-resolution images. The second sub-system interprets the hand articulation using a smart sensor that provides selective views of variable resolution. The third sub-system fuses the outputs of the motion tracker and smart sensor and gives the meaning to hand gesticulation. The paper discusses the technical details of the algorithms used for interpreting hand articulation.< >

关键词： Handicapped aids Machine vision Target tracking Shape Motion analysis Cameras image processing Fuses Auditory system pattern recognition

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Medium level scene representation using a VLSI smart hexagonal sensor with multiresolution edge extraction capability and scale space integration processing

Medium level scene representation using a VLSI smart hexagon...

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IEEE Computer Society conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Tremblay Savard Poussart Computer Vision and Digital Systems Laboratory Department of Electrical Engineering Laval University QUE Canada

This paper presents a sensing approach where photo-transduction, multi-resolution feature extraction, scale-space integration and edge tracking are performed on a mixed (digital-analog) VLSI architecture in order to generate medium-level scene description. The proposed system is mainly targeted for robot vision applications where feature description is preferred to a set of raw or raster 2D images and edge maps. The Multiport Access photo-Receptor (MAR) is a CMOS sensor and represents the main sensory part of this integrated image acquisition system. VLSI also provides means to integrate analog computing, digital controller and DSP co-processor modules which define a powerful sensory chip set for focal plane image processing. A current version of the MAR sensor which implements 256/spl times/256 pixels includes 16 analog spatial filters which simultaneously compute multiresolution edge maps. This unique 2D hexagonal smart sensor approach which performs up to 8.5/spl times/10/sup 9/ arithmetic Op/sec during the acquisition/filtering phase and 25/spl times/10/sup 9/ Logical Op/sec for scale-space integration allows high resolution image capability. It represents a significant improvement for passive sensory units in a compact assembly for computer vision applications.< >

关键词： Very-large-scale integration image edge analysis Feature extraction Robots, vision systems Spatial filters CMOS integrated circuits image sensors

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Classification of multispectral images using BP-neural network classifier-input codings assessment

Classification of multispectral images using BP-neural netwo...

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IEEE Region 10 International conference TENCON

作者： C.C. Chong J.C. Jia D.P. Mital School of Electrical and Electronic Engineering Nanyang Technological University Singapore

The research effort reported in this paper focused on the evaluation of different input codings influencing the performance of a backpropagation (BP) neural network for the classification of multispectral images. The assessments of the input codings are based on the performances of a network classifier using five different input coding schemes, namely normalization, temperature, coarse, binary coded decimal and Gray codings. The clustering property, which can be visualized through the "Euclidean distance" graph, is also introduced as a tool to predict the generalization capability of each input coding method. Experimental results obtained indicated that in order to fully exploit the generalization property of the neural network, the clustering property of the spectral features must be maintained during the input coding process.< >

关键词： Multispectral imaging image coding Neural networks remote sensing Feature extraction Data mining Temperature Visualization pattern recognition image classification

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Integrating multiple knowledge sources using genetic algorithm applied to hierarchically structured sensor data

Integrating multiple knowledge sources using genetic algorit...

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International conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： T. Sawaragi J. Umemura O. Katai S. Iwai Department of Precision Mechanics Faculty of Engineering Kyoto University Kyoto Japan Electronics Research Laboratory Nippon Steel Company Limited Futtsu Chiba Japan Faculty of Physics and Technology Meiji University Nagoya Japan

The paper presents a new approach for implementing a human expert's proficient interpretation skills for data and knowledge fusion in signal understanding tasks. The authors start by recognizing the fact that signal interpretation is attributed much to a human expert's domain-specific, pattern-perceiving capability of grasping raw signals by structured representations having multiple levels of abstraction. First, they attempt to organize such structured representations by using the hierarchical clustering method of data analysis. Then, based on these representations they formulate a human expert's interpretation skills as an activity of searching for an optimum combination of those perceptual units within that structured representation space being constrained by the situational data. In order to implement this activity, they introduce a genetic algorithm and apply it to the structured representation space assimilating a human analyst's creative interpreting task of flexibly shifting the focal view of attention from the coarse to the precise. They implement a working system for signal understanding of the remote sensing data of seismic prospecting.< >

关键词： Genetic algorithms Humans Data engineering Clustering methods Signal processing Gas detectors Steel Physics pattern recognition Algorithm design and analysis

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COMPUTER processing AND pattern recognition OF RADAR remote sensing imageS OF THE HUAI RIVER BASIN

COMPUTER PROCESSING AND PATTERN RECOGNITION OF RADAR REMOTE ...

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计算机，通信，控制与电力工程国际会议

作者： Zhang Xiaoming Zhou Xiuguang Lu Songlin Institute of Nuclear Research Academia Sinica East China University of Chemical Technology

ISBN: (纸本)0780312333

We introduce remote sensing image processing and recognition in this *** the radar remote sensing and image processing system make errors,the image has distortion,we adopt gradient and linear regression to correct the grey distortion,use multinomial warping method to rectificate geometric *** last,we use supervised classification and recognition method to analysize the remote sensing image, and support the user true flooding region distribution map and *** result is very good

关键词： multinomial warping linear regression supervised classification

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SURFACE CIRCULATION ESTIMATION USING image-processing AND COMPUTER VISION METHODS APPLIED TO SEQUENTIAL SATELLITE imageRY

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PHOTOGRAMMETRIC ENGINEERING AND remote sensing 1993年第3期59卷 407-413页

作者： YAN, XH BREAKER, LC NOAA NWSNATL METEOROL CTRCAMP SPRINGSMD 20746

Two methods of automating the process of ocean feature tracking for estimating surface currents in coastal areas are outlined. These methods involve pattern recognition and have certain advantages over the more familiar maximum cross-correlation technique of Emery et al. (1986). The first method requires three steps in its application-pattern selection, pattern recognition, and geometrical calculations-to determine both the cross- and the along-isotherm displacements. The second method calculates certain surface motion parameters, including rotation and translation in Hough parameter space. Each method is applied to sequential AVHRR IR satellite imagery off the U.S. east coast. Finally, some of the practical problems encountered in the application of these methods are described.

关键词： Oceanography

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LINDA - a system for automated linear feature detection and analysis

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Canadian Journal of remote sensing 1993年第1期19卷 9-21页

作者： Wang, Jinfei Univ of Windsor Windsor Canada

Automated linear pattern recognition and interpretation from remotely sensed data and other cartographic data are important in many environmental problems, such as road and land-use updating, geological lineament mapping, and hydrologic applications. Different from current digital image processing and Geographic Information Systems (GIS), this paper presents a new X-Window-based system, called LInear-feature Network Detection and Analysis (LINDA), that allows automated recognition and analysis of line/edge-like features. Input to the system is any digital grey-level image containing lines and/or edges. The system consists of several modules, including: line detection - automated extraction of roads, stream networks, and so on;edge detection - automated extraction of field boundaries, land-water boundaries, and so on;Hough transform - automated detection of geologic lineaments (straight edges);structural pattern recognition - automated generation of structural measures and associated statistics from the extracted linear feature maps;and other modules. This paper describes the architecture of LINDA, illustrates its use with examples, and describes some aspects of LINDA implementation. The graphics interface of LINDA makes it a menu-driven and user-friendly system. This system can be combined with existing GIS and image processing systems. It has the potential for various applications in environmental studies.

关键词： remote sensing

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pattern-recognition METHOD OF MULTISPECTRAL remote-sensing FOR SUSPENDED MATTER IN COASTAL WATER 16

PATTERN-RECOGNITION METHOD OF MULTISPECTRAL REMOTE-SENSING F...

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CONF ON APPLICATIONS OF DIGITAL image processing 16

作者： MA, J HUANG, XS HUANG, T LIU, ZS Ocean Univ. of Qingdao (China)

ISBN: (纸本)0819412775

Simple and successful methods have been presented for deriving suspended matter concentration from optical reflectance spectra of seawater. The pattern recognition method allows multispectral radiance to be analyzed for signatures characteristic of different, independently vary phenomena and can be used to evaluate the information content. Landsat MSS data on Nov. 28, 1983 and quasi-synchronous field data at the mouth of Yellow River have been used in this paper. By pattern recognition, the first three characteristic vectors which are derived from the radiance covariance matrix are enough for this study. These vectors accounted for 98.1, 98.9, 99.2 percent respectively. The first vector is mainly loaded by visible light channels, the optical properties of suspended matter enhance the backscattered radiance in these channels. The scalar multiplier for the first vector represents the first vector weighted in each station. We get the regression equation from a least-square fit between the scalar multiplier and the logarithm of the field data observed at each station. The correlation coefficient is 0.81, which is much higher than the correlation coefficient between the radiance of single channel and the logarithm of suspended matter concentration (0.59, 0.70, 0.72, 0.37 respectively). Using the model, we get the information of concentration and distribution of suspended matter from Landsat data on Nov. 28, 1983, Oct. 5 1984, Dec. 3 1988. These results confirmed with hydrology and meteorology information were satisfactory.

关键词： Earth observing sensors Landsat Mouth pattern recognition Data modeling remote sensing Digital image processing Optical properties Spatial resolution Reflectivity

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IDENTIFICATION AND CLASSIFICATION OF FOREST LANDSCAPE - FUZZY MODELING AND processing OF remote-sensing image

IDENTIFICATION AND CLASSIFICATION OF FOREST LANDSCAPE - FUZZ...

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CONF ON GROUND sensing

作者： TIAN, HQ State University of New York College of Environmental Science and Forestry Syracuse 13210 NY United States

ISBN: (纸本)0819411779

Much information from remote sensing image is fuzzy and uncertain, which greatly influences the recognition and monitoring of landscape pattern. It is more difficult to identify the types of forest landscape, due to its relative homogeneity compared to larger scale landuse type. Moreover, the categories and process of forest landscape classification, in fact, are fuzzy. In this paper, I: use a fuzzy modeling approach to deal with the fuzzy phenomena during the process of identification and classification of forest landscape image. This approach also incorporates the knowledge of vegetation ecology and expert experiences into clasification model. Using supervised classification of pixel-by-pixel and group-by-group, I classified a tropical forest landscape. The results indicate that the fuzzy modeling approach can effectively deal with fuzzy information from remote sensing image. This model is also available for developing the expert system of remote image processing for the identification and classification of forest landscape. However, the structure and algorithm of the model can be used to the identification and classification of any other landscape pattern. © 1993 SPIE. All rights reserved.

关键词： Expert systems

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