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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yi Liu Y.F. Zheng Department of Electrical and Computer Engineering Ohio State Uinversity Columbus OH USA

Support vector machines (SVM) are state-of-the-art learning machines and have found a great deal of success in a wide range of applications. In the framework of SVM, each sample belongs to either one class or the other. This requirement, however, makes it difficult to apply SVM to the applications where the data exhibit partial or unclear class memberships. To address this problem, this paper reformulates the standard SVM to be a new learning machine that is capable of dealing with binary (or hard) as well as real-valued (or soft) class memberships. The new machine, which is named soft SVM (S-SVM), has been integrated into a classification-based video object extraction approach, and the experimental results demonstrate the effectiveness of the new approach.

关键词： Support vector machines Support vector machine classification Machine learning Application software Risk management pattern recognition image analysis pattern analysis Soil remote sensing

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An Illuminance-Reflectance Model for Nonlinear Enhancement of Color images

An Illuminance-Reflectance Model for Nonlinear Enhancement o...

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

作者： Li Tao R. Tompkins V.K. Asari Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

An image enhancement algorithm based on illuminance-reflectance model is proposed for improving the visual quality of digital images captured under insufficient and/or non-uniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. The images are processed in a similar way as human eyes sensing a scene. The algorithm demonstrates high quality of enhanced images, robust performance and fast processing speed. Compared with Retinex and multi-scale retinex with color restoration (MSRCR), the proposed method shows a better balance between luminance enhancement and contrast enhancement as well as a more consistent and reliable color rendition without introducing incorrect colors. This is an effective technique for image enhancement with simple computational procedures, which makes real-time application successfully realized. The application of this image enhancement technique to the FRGC images yields improved face recognition results.

关键词： Color image enhancement Frequency estimation Layout Digital images Reflectivity Dynamic range image coding Humans Eyes

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The roles of independent component analysis in the knowledge intensive systems

The roles of independent component analysis in the knowledge...

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International conference on Integration of Knowledge Intensive Multi-Agent Systems (KIMAS)

作者： Chi Hau Chen Electrical and Computer Engineering Department University of Massachusetts Dartmouth North Dartmouth MA USA

ISBN: (纸本)078039013X

The independent component analysis (ICA) has been a well studied subject in recent years. Its implementation may employ neural networks or other techniques with the objective of deriving component signals or images that are as independent as possible. ICA has been used in remote sensing, medical signal analysis, speech and seismic signal processing, wireless communications, nondestructive testing, and other areas. All of such applications involve knowledge intensive systems that employ a large amount of data. ICA may be considered as a mechanism for knowledge decomposition and integration, as well as multiresolution representation and generalization.

关键词： Independent component analysis pattern recognition Principal component analysis Signal processing Nondestructive testing Data mining USA Councils Knowledge engineering Neural networks remote sensing

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3D Assisted Face recognition: A Survey of 3D Imaging, Modelling and recognition Approachest

3D Assisted Face Recognition: A Survey of 3D Imaging, Modell...

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

作者： J. Kittler A. Hilton M. Hamouz J. Illingworth Centre for Vision Speech and Signal Processing University of Surrey Guildford UK

3D face recognition has lately been attracting ever increasing attention. In this paper we review the full spectrum of 3D face processing technology, from sensing to recognition. The review covers 3D face modelling, 3D to 3D and 3D to 2D registration, 3D based recognition and 3D assisted 2D based recognition. The fusion of 2D and 3D modalities is also addressed. The paper complements other reviews in the face biometrics area by focusing on the sensor technology, and by detailing the efforts in 3D face modelling and 3D assisted 2D face matching.

关键词： Face recognition image recognition Face detection Decision making Computer vision Robustness Humans Photometry Feature extraction Testing

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Detecting temporal changes in satellite imagery using ANN

Detecting temporal changes in satellite imagery using ANN

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International conference on Recent Advances in Space Technologies (RAST)

作者： P. Mathur R. Govil Apaji Institute of Mathematics and Applied Computer Technology Rajasthan India

One of the most interesting aspects of the world is that it can be considered made up of patterns. In the most pattern recognition problem pattern have a dynamic nature and non-adaptive algorithms (instruction sets) will fail to give a realistic solution to model them. In these cases, adaptive algorithms are used and among them, neural networks have the greatest hit. For example, the defense applications very frequently need to record, detect, identify and classify images of objects or signals coming from various directions and from various sources - static or dynamic. There are many applications in remote sensing where study of dynamic data is needed such as deforestation, effects of natural and man made disasters, migration in the paths of rivers due to the dynamic nature of Earth's plates. Artificial Neural Networks (ANN) can play a role in modeling such applications because of their capability to model nonlinear processes and to identify unknown patterns and images based on their learning model, or to forecast certain outcomes by extrapolation. In this study we present results on classifying the images using SOFM classification and detect temporal changes in patterns.

关键词： Satellites Artificial neural networks Predictive models pattern recognition Instruction sets Adaptive algorithm Neural networks Object detection Signal processing remote sensing

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模式识别技术在数字城市图像处理中的应用

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微计算机信息 2005年第10X期21卷 107-108页

作者：刘浩戚文静戴居丰姜庆娜刘秀婷牛小飞济南山东建筑工程学院计算机系山东建筑工程学院计算机系天津大学电信学院

数字地球、数字中国、数字城市是当前的一个研究热点。其中的一个重要方面是将遥感图象进行模式识别将遥感图象矢量化,再将矢量化的图形用计算机三维立体化。本文阐述了数字城市中的模式识别的方法和结果。结果表明,本文的模式识别技术... 详细信息

数字地球、数字中国、数字城市是当前的一个研究热点。其中的一个重要方面是将遥感图象进行模式识别将遥感图象矢量化,再将矢量化的图形用计算机三维立体化。本文阐述了数字城市中的模式识别的方法和结果。结果表明,本文的模式识别技术是成功的。

关键词：数字城市模式识别图像处理

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Using coupled subspace models for recovery of reflectance spectra from airborne images

Using coupled subspace models for recovery of reflectance sp...

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conference on Computer Vision and pattern recognition (CVPR)

作者： K. Chandra G. Healey Computer Vision Laboratory Department of Electrical Engineering and Computer Science University of California Irvine Irvine CA USA

The spectral radiance measured by an airborne sensor is dependent on the spectral reflectance of the ground material, the orientation of the material surface, and the atmospheric and illumination conditions. We present a nonlinear algorithm for estimating surface spectral reflectance of a surface on the ground from the spectral radiance measured by an airborne sensor. The nonlinear separation algorithm uses a low-dimensional subspace model for the reflectance spectra. The algorithm also considers the inter-dependence of the path radiance and illumination spectra by using a coupled subspace model. We have applied the algorithm to a large set of 0.4-1.74 micron sensor radiance spectra. We have examined the use of the recovered reflectance vectors for material identification over a database of materials from the US geological Survey (USGS) library. We also extend the nonlinear algorithm to estimate the reflectance spectra of materials having varying orientations. We have applied the algorithm to 0.42-1.74 micron sensor radiance spectra in digital imaging and remote sensing image generation (DIRSIG) model scenes that contain 3D objects.

关键词： Reflectivity Atmospheric modeling Atmospheric measurements Geologic measurements Lighting image databases Geology Software libraries image sensors Digital images

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Texture classification using wavelet transform and support vector machines

Texture classification using wavelet transform and support v...

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Canadian conference on Electrical and Computer Engineering (CCECE)

作者： S. Sidhu K. Raahemifar Department of Electrical and Computer Engineering Ryerson University Toronto ONT Canada

In this paper, we have investigated an approach based on support vector machines (SVMs) and wavelet transform (WT) for texture analysis. Texture analysis plays an important role in many tasks, ranging from remote sensing to medical imaging and query by content in large image databases. The main difficulty of texture analysis in the past was the lack of adequate tools to characterize different scales of texture effectively. The development in multi-resolution analysis such as wavelet transform has helped overcome this difficulty. It was found that the results using the combination of wavelet statistical and wavelet co-occurrence features generated from discrete wavelet transform for texture classification are promising. In recent years, support vector machines (SVM) have demonstrated excellent performance in a variety of pattern recognition problems. By applying SVM in tandem with the discrete wavelet transform for texture classification, it has produced more accurate classification results based on the Brodatz texture database

关键词： Wavelet transforms Support vector machines Support vector machine classification Discrete wavelet transforms image texture analysis Wavelet analysis image analysis remote sensing Biomedical imaging image databases

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Optimal approach for enhancement of large and small scale near-infrared and infrared imaging

Optimal approach for enhancement of large and small scale ne...

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International conference on Industrial Electronics and Control Applications, ICIECA

作者： Zhengmao Ye Jiecai Luo P. Bhattacharya Electrical Engineering Department Southern University Baton Rouge LA USA

In a broad area of industry such as remote sensing and medical diagnosing, imaging enhancement technology takes a leading role, where energy distribution of the light source depends not only on image coordinate but also on wavelength. Both infrared (IR) and near-infrared (NIR) imaging techniques have a variety of applications in these fields. For instance, satellite images are taken via IR or NIR spectrometer and laser Doppler medical scanning is collaborated with NIR spectrometer. Matrix functions of any image correspond to brightness or energy at each image pixel. The actual decision making must rely on detailed investigation of images being obtained. Therefore, image processing should be taken into account so as to enhance the results from real world. Segmentation is an image analysis approach to clarify feature ambiguity and information noise, which divides an image into separate parts that correlate with the objects or areas of the particular object involved. This procedure can be conducted by clustering, which is a process of partitioning a set of pattern vectors into subsets. Being a simple unsupervised learning algorithm, k-means clustering algorithm has the potential to both simplify the computation and accelerate the convergence. In most cases optimization is closely related to clustering, which gives rise to the best way of problem solving. In this article, optimal approach is proposed to be implemented along with image segmentation. This methodology is to enhance both large scale and small scale IR and NIR image processing

关键词： Infrared imaging Optical imaging Spectroscopy image processing image segmentation Clustering algorithms Partitioning algorithms remote sensing Medical diagnosis Light sources

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Skew estimation for scanned documents from "noises"

Skew estimation for scanned documents from "noises"

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International conference on Document Analysis and recognition

作者： B. Yuan C.L. Tan Centre for Remote Imaging Sensing and Processing Department of Computer Science National University of Singapore Singapore Department of Computer Science National University of Singapore

The vast majority of the published skew estimation methods for scanned document images are for textual documents. These methods are based on the principle that the skew angles can be derived from the presence of the obvious text lines. The non-textual objects, such as line drawings, photographic inserts, scan artifacts including the dark bars around the borders and the center spine of bounded materials, and media contaminations are considered as "noises", thus are subject to elimination. Skew estimators that work in the presence of excessive noises are considered robust. This paper presents a skew estimation method that is based on the straight lines or edges. It uses the Muff Transform with a probe-line mapping scheme for feature identification. Various strategies for optimized line probing are devised. This method is applicable to both textual and graphical documents scanned with ordinary scanners or copiers under normal conditions. Selected images from the University of Washington English document image database I (UWDB-I) are used for its usability evaluation.

关键词： Bars Contamination Shape Computer science Noise robustness image databases Usability Filters Computer vision image edge detection

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