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IceSynth: An image Synthesis System for Sea-Ice Segmentation Evaluation

IceSynth: An Image Synthesis System for Sea-Ice Segmentation...

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Canadian Conference on Computer and Robot vision, CRV

作者： Alexander Wong Wen Zhang David A. Clausi Vision and Image Processing Group Department of Systems Design Engineering University of Waterloo Waterloo Canada

An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.

关键词： image generation Sea ice image segmentation Synthetic aperture radar Automatic testing Stochastic processes image sampling Probability distribution Computerized monitoring Shape

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Adaptive Monte Carlo Retinex Method for Illumination and Reflectance Separation and Color image Enhancement

Adaptive Monte Carlo Retinex Method for Illumination and Ref...

引用

Canadian Conference on Computer and Robot vision, CRV

作者： Alexander Wong David A. Clausi Paul Fieguth Vision and Image Processing Group Department of Systems Design Engineering University of Waterloo Waterloo Canada

A novel stochastic Retinex method based on adaptive Monte Carlo estimation is presented for the purpose of illumination and reflectance separation and color image enhancement. A spatially-adaptive sampling scheme is employed to generate a set of random samples from the image field. A Monte Carlo estimate of the illumination is computed based on the Pearson Type VII error statistics of the drawn samples. The proposed method takes advantage of both local and global contrast information to provide better separation of reflectance and illumination by reducing the effects of strong shadows and other sharp illumination changes on the estimation process, improving the preservation of the original photographic tone, and avoiding the amplification of noise in dark regions. Experimental results using monochromatic face images under different illumination conditions and low-contrast chromatic images show the effectiveness of the proposed method for illumination and reflectance separation and color image enhancement when compared to existing Retinex and color enhancement techniques.

关键词： Monte Carlo methods Lighting Reflectivity Color Computer vision Layout Pixel image sampling Noise reduction Separation processes

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SEC: Stochastic Ensemble Consensus Approach to Unsupervised SAR Sea-Ice Segmentation

SEC: Stochastic Ensemble Consensus Approach to Unsupervised ...

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Canadian Conference on Computer and Robot vision, CRV

作者： Alexander Wong David A. Clausi Paul Fieguth Vision and Image Processing Group Department of Systems Design Engineering University of Waterloo Waterloo Canada

The use of synthetic aperture radar (SAR) has become an integral part of sea-ice monitoring and analysis in the polar regions. An important task in sea-ice analysis is to segment SAR sea-ice imagery based on the underlying ice type, which is a challenging task to perform automatically due to various imaging and environmental conditions. A novel stochastic ensemble consensus approach to sea-ice segmentation (SEC) is presented to tackle this challenging task. In SEC, each pixel in the SAR sea-ice image is assigned an initial sub-class based on its tonal characteristics. Ensembles of random samples are generated from a random field representing the SAR sea-ice imagery. The generated ensembles are then used to re-estimate the sub-class of the pixels using a weighted median consensus strategy. Based on the probability distribution of the sub-classes, an expectation maximization (EM) approach is utilized to estimate the final class likelihoods using a Gaussian mixture model (GMM). Finally, maximum likelihood (ML) classification is performed to estimate the final class of each pixel within the SAR sea-ice imagery based on the estimated GMM and the assigned sub-classes. SEC was tested using a variety of operational RADARSAT-1 and RADARSAT-2 SAR sea-ice imagery provided by the Canadian Ice Service (CIS) and was shown to produce successfully segmentation results that were superior to approaches based on K-means clustering, Gamma mixture models, and Markov Random Field (MRF) models for sea-ice segmentation.

关键词： Stochastic processes Sea ice image segmentation Pixel Maximum likelihood estimation Synthetic aperture radar image analysis Performance analysis image generation Probability distribution

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A Comparison of unmixing algorithms for hyperspectral imagery

A Comparison of unmixing algorithms for hyperspectral imager...

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Santos-García, Andrea Velez-Reyez, Miguel Rosario-Torres, Samuel Chinea, Jesus D Laboratory for Applied Remote Sensing and Image Processing Center for Subsurface Sensing and Imaging Systems University of Puerto Rico-Mayaguez PR United States Biology Department University of Puerto Rico-Mayaguez PR United States

ISBN: (纸本)9780819476005

In this paper, we present an experimental comparison of unmixing using the constrained positive matrix factorization (cPMF) with SMACC and MaxD unmixing algorithms that retrieve endmembers from the image *** comparison was made using hyperspectral images collected over Vieques Island in Puerto Rico using the AISA sensor. Based on field work, six information classes were identified in the area of interest and the algorithms are evaluated in their capability to retrieve information about the classes of interest. The cPMF was the only approach capable of identifying all six informational classes with one or more spectral classes assigned to them. SMACC and MaxD were unable to extract one of the classes. The abundance maps from cPMF describe the spatial distribution of the information classes.©2009 SPIE.

关键词： Hyperspectral imaging

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image thresholding using ultrafuzziness optimization based on type II fuzzy sets

Image thresholding using ultrafuzziness optimization based o...

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International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME)

作者： Agus Zainal Arifin Aidila Fitri Heddyanna Hudan Studiawan Vision and Image Processing Laboratory Department of Informatics Faculty of Information Technology Sepuluh Nopember Institute of Technology Surabaya Indonesia

ISBN: (纸本)9781424449996

image thresholding is a critical process in digital image processing application. However, there are some disturbing factors like image vagueness and bad illumination resulting in not satisfied image thresholding output. Several fuzzy thresholding techniques are developed to remove graylevel ambiguity during threshold selection. One of the techniques is thresholding method using type II fuzzy sets. In this paper, we propose relaxation of the ultrafuzziness measurement by considering ultrafuzziness for background and object fuzzy sets separately. The proposed method optimizing ultrafuzziness to decrease uncertainty in fuzzy system used type II fuzzy sets. Experimental results on several images show the effectiveness of the proposed method.

关键词： Fuzzy sets image processing Digital images Lighting Fuzzy systems Fuzzy set theory Laboratories Informatics Information technology Optimization methods

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2-D Roger-Ramanujan continued fraction and inversion

2-D Roger-Ramanujan continued fraction and inversion

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International Symposium on Signals, Circuits and systems (ISSCS)

作者： George E. Antoniou P. A. Katsalis Image Processing and Systems Laboratory Department of Computer Science Montclair State University Montclair NJ USA

In this paper the generalized numerical Rogers-Ramanujan continued fraction expansion is extended to two-dimensions (2-D). A new fast algorithm is proposed for the inversion of the 2-D Rogers-Ramanujan continued fraction expansion. The algorithm is based on matrix formulations. The simplicity and efficiency of the algorithm are illustrated by step-by-step examples.

关键词： Transfer functions Polynomials Digital filters Delay image processing Laboratories Computer science Frequency conversion Signal analysis Process control

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2-D FIR discrete bidiagonal filters: Minimal circuit and state space realization

2-D FIR discrete bidiagonal filters: Minimal circuit and sta...

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International Symposium on Signals, Circuits and systems (ISSCS)

作者： George E. Antoniou Image Processing and Systems Laboratory Department of Computer Science Montclair State University Montclair NJ USA

In this paper 2-D circuit and state space realizations are presented for FIR bidiagonal discrete 2-D filters/systems. The proposed state space realization is based on the corresponding circuit implementation. For the state space realization the classical 2-D model of the Roesser type was used. The dimension of the state vector and the number of delay elements are minimal. Two examples are presented to illustrate the proposed results.

关键词： Finite impulse response filter Circuits State-space methods Transfer functions image processing Computer science Delay Control theory Signal processing Signal synthesis

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Robust image modeling technique with a bioluminesence image segmentation application

Robust image modeling technique with a bioluminesence image ...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Zhong, Jianghong Wang, Ruiping Tian, Jie Department of Biomedical Engineering School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Medical Image Processing Group Key Laboratory of Complex Systems and IntelligenceScience Institute of Automation Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9780819475138

A robust pattern classifier algorithm for the variable symmetric plane model, where the driving noise is a mixture of a Gaussian and an outlier process, is developed. The veracity and high-speed performance of the pattern recognition algorithm is proved. Bioluminescence tomography (BLT) has recently gained wide acceptance in the field of in vivo small animal molecular imaging. So that it is very important for BLT to how to acquire the highprecision region of interest in a bioluminescence image (BLI) in order to decrease loss of the customers because of inaccuracy in quantitative analysis. An algorithm in the mode is developed to improve operation speed, which estimates parameters and original image intensity simultaneously from the noise corrupted image derived from the BLT optical hardware system. The focus pixel value is obtained from the symmetric plane according to a more realistic assumption for the noise sequence in the restored image. The size of neighborhood is adaptive and small. What's more, the classifier function is base on the statistic features. If the qualifications for the classifier are satisfied, the focus pixel intensity is setup as the largest value in the ***, it will be ***,pseudo-color is added up to the result of the bioluminescence segmented image. The whole process has been implemented in our 2D BLT optical system platform and the model is proved. © 2009 SPIE.

关键词： Bioluminescence

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Interest point detection for hyperspectral imagery

Interest point detection for hyperspectral imagery

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Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral imagery XV

作者： Dorado-Munoz, Leidy P. Velez-Reyes, Miguel Roysam, Badrinath Mukherjee, Amit Center for Subsurface Sensing and Imaging Systems Laboratory for Applied Remote Sensing and Image Processing University of Puerto Rico at Mayaguez Mayaguez PR.00681-9048 United States Center for Subsurface Sensing and Imaging Systems Department of Electrical Computer and Systems Engineering Rensseleaer Polytechnic Institute Troy NY. 12180-3590 United States

ISBN: (纸本)9780819476005

This paper presents an algorithm for automated extraction of interest points (IPs)in multispectral and hyperspectral images. Interest points are features of the image that capture information from its neighbours and they are distinctive and stable under transformations such as translation and rotation. Interest-point operators for monochromatic images were proposed more than a decade ago and have since been studied extensively. IPs have been applied to diverse problems in computer vision, including image matching, recognition, registration, 3D reconstruction, change detection, and content-based image retrieval. Interest points are helpful in data reduction, and reduce the computational burden of various algorithms (like registration, object detection, 3D reconstruction etc) by replacing an exhaustive search over the entire image domain by a probe into a concise set of highly informative points. An interest operator seeks out points in an image that are structurally distinct, invariant to imaging conditions, stable under geometric transformation, and interpretable which are good candidates for interest points. Our approach extends ideas from Lowe's keypoint operator that uses local extrema of Difference of Gaussian (DoG) operator at multiple scales to detect interest point in gray level images. The proposed approach extends Lowe's method by direct conversion of scalar operations such as scale-space generation, and extreme point detection into operations that take the vector nature of the image into consideration. Experimental results with RGB and hyperspectral images which demonstrate the potential of the method for this application and the potential improvements of a fully vectorial approach over band-by-band approaches described in the literature. © 2009 SPIE.

关键词： Object detection

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Ringing artifact removal in digital restored images using multi-resolution edge map

Ringing artifact removal in digital restored images using mu...

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作者： Kim, Sangjin Jun, Sinyoung Lee, Eunsung Shin, Jeongho Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University 221 Heuksuk-Dong Dongjak-Ku Seoul 156-756 Korea Republic of Department of Web Information Engineering Hankyong National University 67 Seokjeong-Dong Anseong Kyonggi-Do 456-749 Korea Republic of

ISBN: (纸本)9783642105456

This paper presents a novel approach to reducing ringing artifact in digitally restored images by using multi-resolution edge map. The performance of reducing ringing artifacts depends on the accurate classification of the local features in the image. The discrete wavelet transform (DWT) provides effective insight into both spatial and frequency characteristics of an image. Through the DWT analysis, we show that ringing artifacts can be suppressed to a great extent by using multiple-level edge maps, which provide enhanced matching to local edges. Base on the experimental results, the proposed method can reduce ringing artifacts with minimized edge degradation by using DWT analysis. © 2009 Springer-Verlag Berlin Heidelberg.

关键词： Discrete wavelet transforms

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