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Experiments in transform-based range image compression

Experiments in transform-based range image compression

引用

16th International conference on pattern recognition, ICPR 2002

作者： Campbell, Richard J. Flynn, Patrick J. Sharp Laboratories of America CamasWA98607 United States CS and E Dept. Univ. of Notre Dame Notre DameIN46556 United States

ISBN: (纸本)0769516963

Range images (depth maps) are seeing increased usage in a variety of application areas including entertainment, industrial automation, inspection, remote sensing, and military tactical planning. As the corpus of range imagery increases in size and the need to communicate such images over fixed-bandwidth channels increases, the compression of range data deserves investigation. Since the geometry encoded by range sensors is inherently "low-bandwidth", transform-based techniques seem appropriate for investigation in this context. This paper reports on experiments with a popular zerotree-based image codec (the SPIHT algorithm developed by Said and Peariman) and its application to the compression of range imagery. Experiments suggest that compression rates of 1 bit/pixel and below are achievable with minimal impact on fidelity. © 2002 IEEE.

关键词： image compression

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Signal and image processing techniques for environmental and clinical applications of infrared spectroscopy

Signal and image processing techniques for environmental and...

引用

作者： Wabomba, Mukire John Ohio University

学位级别：Ph.D.

The development of automated, real time, and robust measurement techniques is the focus of much current research in analytical chemistry. Of the measurement approaches under study, infrared spectroscopy offers the capability to implement a selective, nondestructive analysis of a variety of chemical samples. In an automated analysis, current infrared instrumentation produces volumes of data necessitating the development of data processing techniques to extract useful information. In this dissertation, automated analysis methods for qualitative and quantitative applications of infrared spectroscopy are explored. These methods are directed to environmental remote sensing and clinical applications and focus on both single-point measurements with Fourier transform infrared (FT-IR) instrumentation and imaging measurements performed with a multispectral line scanner. The developed methodology is directed to isolating the analyte signature from the data for use in a qualitative determination of analyte presence or in a quantitative measurement of analyte amount. A study was conducted to find optimal parameters for generating finite impulse response matrix (FIRM) digital filters for use in isolating analyte signals directly from FT-IR interferogram data. The filter design protocols established in this study are used to generate filters for quantitative and qualitative applications. Filters are designed to extract the glucose signal from a complex simulated biological matrix with severely overlapped spectra. Digital filters are also developed to isolate ammonia signals for use with pattern recognition techniques for the remote detection of ammonia in heated plumes from stack emissions. Analysis techniques are also developed for use with data from an infrared multispectral imaging system, designed to detect chemical plumes from stack emissions. These plumes are viewed from above by installing the imaging system on an aircraft platform. The alpha residual method is used to remo

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A glass bottle defect detection system without touching

A glass bottle defect detection system without touching

引用

Proceedings of 2002 International conference on Machine Learning and Cybernetics

作者： Ma, Hui-Min Su, Guang-Da Wang, Jun-Yan Ni, Zheng Dept. of Electronic Engineering Tsinghua University Beijing 100084 China

ISBN: (纸本)0780375084

This paper presents a new remote detection system for defect in upper portion of a glass bottle in production line. The system uses eight video cameras installed beside the production line to capture the images of the mouth, lip and neck of each bottle. Then, two computers collect and process these images to detection defects. The detection process includes two parts: one is to detect defects occurred in the mouth and lip of a glass bottle. This part uses the images captured from the top of a bottle. The other is for the defects occurred in the neck and shoulder of a bottle, by processing the images shot on the upper-side of the bottle. The processing includes image orientation, preprocessing, and image recognizing. The system sends a control signal to manufacture's mainframe once it recognizes a defect bottle, and can eject the defect bottle off the production line. The adequate test result is given to show the recognition rate system for all occurred in the upper part of a glass bottle.

关键词： pattern recognition systems

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Signal processing for preserving the environment

Signal processing for preserving the environment

引用

作者： Chen, C.H. University of Massachusetts Dartmouth Electrical and Computer Engineering Dept. N. Dartmouth MA 02747-2300 United States

Signal processing has now had many benefits to our daily life though we may not be aware of. For examples: microprocessors (a kind of signal processor) are used to control gas consumption to make our cars more fuel efficient, and signal processing is used to predict the major weather changes. Signal processing has played important roles in our environment including land water and air, through monitoring, control and prediction functions. In this presentation, the recent progress of signal processing, including hardware, software and algorithms, the neural networks, and the roles of remote sensing, will be reviewed first. This is followed by presenting several examples of using signal processing to preserve our environment. Some notable examples are: monitoring of landmines, pollutants in waterways, the salinity in the coastal zones, and the changes in environment;control of oil spills, flood mapping and control;inspection of damages of forest fires;prediction of major storms;et al. It is noted that signal processing is closely linked to pattern recognition in many of these examples. Signal processing in conjunction with pattern recognition will play an increasingly important roles in all of these activities that aim to preserve our precious environmental resources. With rapid progress in signal processors and signal processing techniques, many environmental problems can get better solutions. With collaborative efforts of all nations in the globe, and with the help of signal processing, we can predict that the environment can be preserved along with increased population growth and industrial development © 2002 IEEE.

关键词： Signal processing

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Experiments in transform-based range image compression

Experiments in transform-based range image compression

引用

作者： Campbell, Richard J. Flynn, Patrick J. Sharp Laboratories of America Camas WA 98607 United States CS and E Dept. Univ. of Notre Dame Notre Dame IN 46556 United States

Range images (depth maps) are seeing increased usage in a variety of application areas including entertainment, industrial automation, inspection, remote sensing, and military tactical planning. As the corpus of range imagery increases in size and the need to communicate such images over fixed-bandwidth channels increases, the compression of range data deserves investigation. Since the geometry encoded by range sensors is inherently "low-bandwidth", transform-based techniques seem appropriate for investigation in this context. This paper reports on experiments with a popular zerotree-based image codec (the SPIHT algorithm developed by Said and Pearlman) and its application to the compression of range imagery. Experiments suggest that compression rates of 1 bit/pixel and below are achievable with minimal impact on fidelity. © 2002 IEEE.

关键词： image compression

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pattern recognition methods as tools of restoration project in Maavesi water area

Pattern recognition methods as tools of restoration project ...

引用

IEEE International Symposium on Geoscience and remote sensing (IGARSS)

作者： A. Kaarna R. Spacil M. Jantunen Department of Information Technology Lappeenranta University of Technology Lappeeranta Finland Lake Saimaa Protection Association Lappeeranta Finland

In this report we examine the usability of different computational image segmentation and recognition methods as a tool of water area restoration projects. An airborne, mosaic image of Maavesi water area in South-east Finland was measured in September 2000 with a three-channel camera covering parts from the spectrum in red, green and infra-red wavelengths. image segmentation and recognition methods were then applied in purpose to recognise and localize the aquatic macrophyte plant communities into several groups or by species. Results were confirmed by a field study performed in September-October 2001. The main interest of image processing was to have data for design and monitoring of water plant biomass removal in near future.

关键词： pattern recognition image recognition image restoration image segmentation Usability Area measurement Wavelength measurement Cameras Infrared imaging Infrared spectra

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Improved back end for integer PCA and wavelet transforms for lossless compression of multispectral images

Improved back end for integer PCA and wavelet transforms for...

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作者： Mielikäinen, Jarno Kaarna, Arto Department of Information Technology Lappeenranta University of Technology P.O. Box 20 FIN-53851 Lappeenranta Finland

remote sensing produces large amounts of digital data that is collected into databases. Since a variety of applications utilize multispectral data, the data cannot be compressed with lossy methods for some user communities. In this paper, we propose improvements for the combination of two reversible methods for the lossless compression of multispectral images. Our improvements are three-fold: number of bits allocated to the coefficients from PCA is not constant but it is based on heuristics, difference between consecutive coefficients are entropy-coded, also the back-end is modified so that all bands are separately entropy coded, i.e. instead of one entropy coder, we used several. Depending on the AVIRIS image, the actual compression ratios, calculated from the files sizes, were in the range from 3.05 to 3.21. © 2002 IEEE.

关键词： image compression

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Texture feature neural classifier for remote sensing image retrieval systems

Texture feature neural classifier for remote sensing image r...

引用

Brazilian Symposium on Computer Graphics and image processing (SIBGRAPI)

作者： M.P. Martins L.N. Frutuoso Guimaraes L. Maria Garcia Fonseca Laboratório Associado de Computação e Matemática Aplicada INPE-Instituto Nacional de Pesquisas Espaciais Sao Paulo Brazil CTA-Centro Técnico Aeroespacial IEAv-Instituto de Estudos Avançados Sao Paulo Brazil Divisão de Processamento de Imagens INPE-Instituto Nacional de Pesquisas Espaciais Sao Paulo Brazil

Texture information is useful for image data browsing and retrieval. The goal of this paper is to present a texture classification system for remote sensing images aimed at the administration of large collections of those images. The proposed classifier is a hybrid system composed by an unsupervised neural network and a supervised one. Starting from a small portion of the image (pattern) the system should recognize the most similar class to a pattern in a database as well as to identify images that contain similar patterns. The texture feature vectors used to characterize the patterns are obtained from the images processed by a bank of Gabor filters. Experimental results using textures of the Brodatz album, multi-spectral and radar images are presented.

关键词： remote sensing image retrieval Information retrieval Neural networks pattern recognition image recognition image databases Spatial databases Gabor filters Radar imaging

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Entropy optimized contrast stretch to enhance remote sensing imagery

Entropy optimized contrast stretch to enhance remote sensing...

引用

International conference on pattern recognition

作者： Xiaoyin Xu E.L. Miller Center for Subsurface Sensing and Imaging Systems Department of ECE Northeastern University Boston MA USA

This paper presents a contrast stretch (CS) method based on minimum entropy constraint to enhance images obtained in remote sensing applications such as ground penetrating radar (GPR), synthetic aperture radar, and infra-red imagery. The CS enhances contrast of the low-contrast part of an image. In remote sensing, it is usually the desirable signals that are of low contrast while interference of high contrast. The CS modifies the original image such that pixel values above and below preset boundaries are set to Zero and the maximum possible pixel value and the pixel values falling between the boundaries are stretched out to enhance the contrast of the image. Using the CS we can enhance the contrast of desirable signals from, for example, a buried landmine or an object obscured by some interference. On the other hand, the CS inevitably enhances other parts of a remote sensing images, such as clutter and measurement noise. Therefore there is a trade-off in using the CS. It is beneficial to find the correct "cut-off" boundaries in the CS in some optimal sense. We propose using minimum entropy as a criterion of looking for the optimal CS parameter Using field data from GPR application, we show that improved image can be obtained which makes further processing such as detection more accurate.

关键词： Entropy remote sensing Ground penetrating radar Clutter Interference Sensor arrays Reflection Pixel Landmine detection Noise measurement

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Thresholding video images for text detection

Thresholding video images for text detection

引用

International conference on pattern recognition

作者： E.Y. Du Chang C P.D. Thouin Remote Sensing Signal and Image Processing Laboratory. Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA Department of Defense Fort Meade MD USA

Thresholding video images is very challenging due to the fact that image background generally has low resolution and is also more complicated and highly distorted than document images. As a result, thresholding methods that work well for document images may not work effectively for video images in some applications. This paper investigates the issue of thresholding video images for text detection and further develops a relative entropy-based thresholding approach that can effectively extract text from complicated video images. In order to demonstrate its performance a comparative study is conducted among the proposed thresholding method and several thresholding techniques which are widely used for document and gray scale images. The experimental results show that thresholding video images is far more difficult than thresholding document images and simple histogram-based methods generally do not perform well.

关键词： Entropy Shape measurement image resolution Information retrieval image retrieval Particle measurements remote sensing Signal processing image processing Laboratories

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