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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Daniel L. Morstad Dennis L. Helder Image Processing Laboratory South Dakota State University SD USA Electrical Engineering and Computer Science Department South Dakota State University SD USA

Over the past several years, pseudo-invariant sites for trending the radiometric gain of multispectral satellite imaging systems has proven to be very useful and accurate. For example, the lifetime calibration of the Landsat 5 Thematic Mapper (TM) was recently updated based in large part upon this approach. This effort reduced the error in the gain estimate from roughly 13 percent to less than 5 percent and integrated a variety of absolute calibration estimates for the sensor. Typically, pseudo-invariant sites are located in arid regions where very little temporal and spatial change occurs over long periods of time and significant extent. Sites in the Sahara desert are especially attractive. However, drawbacks of this approach are that the sites are relatively unacceptable and, for some instruments, only limited numbers of data sets are available. As a result, it would be helpful to use sites that are observed often, such as locations in North America, so that the technique could be applied to more sensors both historically and in the future. The drawback to these sites is that they are typically of much smaller extent and, therefore, are more difficult to use from a geometric perspective. Recent work has shown that small pseudo-invariant sites, if carefully chosen, can also be used for accurate trending of satellite instruments. This paper will illustrate an approach that suggests the calibration site commonly known as Sonoran Desert can be used to observe the characteristic calibration curve of Landsat 5 TM. Because of the significant number of observations of this site, and other similar sites in North America, there is good potential to extend this technique to additional multispectral instruments for enhanced long term monitoring of radiometric gain.

关键词： Calibration Remote sensing Radiometry Satellite broadcasting Instruments North America Remote monitoring Uncertainty Layout Sun

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Constrained Markov Random Field Model for Color and Texture image Segmentation

Constrained Markov Random Field Model for Color and Texture ...

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International Conference on Signal processing, Communication and Networking (ICSCN)

作者： Rahul Dey P. K. Nanda Sucheta Panda Image Processing and Computer Vision Lab Department of Electrical Engineering National Institute of Technology Rourkela Orissa India Department of Electronics & Telecommunication Engineering C.V. Raman College of Engineering Bhubaneswar Orissa India

In this paper, the problem of color image segmentation is addressed as a pixel labeling problem. The observed color image is assumed to be the degraded version of the image labels. We have proposed a new Markov random field (MRF) model known as constrained MRF (CMRF) model to model the unknown image labels and Ohta (I 1 I 2 I 3 ) model is used as the color model. The unique feature of the proposed CMRF model is found to posses a unifying feature of modeling scene and texture images as well. The labels are estimated using maximum a posteriori (MAP) estimation criterion. A hybrid algorithm is proposed to obtain the MAP estimate and the performance of the algorithm is found to be better than that of using simulated annealing (SA) algorithm. The performance of the proposed model is compared with JSEG method and the proposed model is found to be better than JSEG method.

关键词： Markov random fields image segmentation Color Hidden Markov models Pixel Degradation Layout Signal processing algorithms image converters Labeling

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Radiometric Calibration of Reflective Bands of Landsat 4 Thematic Mapper using Pseudo-Invariant Site Technique

Radiometric Calibration of Reflective Bands of Landsat 4 The...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Rimy Malla Dennis L. Helder Image Processing Laboratory South Dakota State University SD USA Electrical Engineering and Computer Science Department South Dakota State University SD USA

Historically, for purposes of calibration, the Landsat 4 Thematic Mapper (TM) sensor has relied upon prelaunch calibration data, internal calibration data and very limited post-launch data. Landsat 4 additionally has used Landsat 5 TM cross-calibration data. The availability of one more independent source, commonly called pseudo-invariant sites, can be used to improve the calibration of the sensor. Pseudo-invariant sites are mainly well characterized desert sites exhibiting the properties of high reflectance, high spatial and spectral uniformity, temporal stability and are nearly Lambertian to decrease BRDF and shadow effects. The successful update of the calibration of the Landsat 5 TM sensor using the pseudo-invariant site approach suggested using this approach to calibrate the Landsat 4 TM sensor. The pseudo-invariant sites selected to characterize the Landsat 4 TM sensor are Middle East sites at Path 166, Row 39 and Path 166, Row 40. Previously, calibration attempts have been primarily based only on the internal calibrator (IC) and prelaunch data. The Landsat 5 internal calibrator was found to be unreliable approximately three years after launch. The Landsat 4 IC data also become questionable especially knowing that the internal calibrators for both the instruments were essentially identical. Thus, it is necessary to characterize the system with another independent and reliable source-in this case pseudo-invariant sites. Unfortunately, due to the unavailability of an adequate number of scenes at the site used for Landsat 5, the Middle East site of Path 166, Row 39 and Path 166, Row 40 were selected. A large drawback of these sites is that they are the locations of the First Gulf War in 1991. This negates the assumption that the surface and atmosphere did not change during the study period. To account for the change, radiometric correction was done using the known Landsat 5 gain signature. A correction factor, which is the ratio of the averages of Landsat 5 pre-g

关键词： Radiometry Calibration Satellites Remote sensing Sensor phenomena and characterization Integrated circuit modeling Reflectivity Stability Instruments Layout

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Fundamental Uncertainty Rotationally Symmetric and Anamorphic Laser Triangulation

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电子学报(英文版) 2008年第1期17卷 8-11页

作者： WANG Lei GAO Jun WANG Xiaojia Johannes Eckstein Peter Ott Laboratory of Image Information Processing School of Computer and Information Hefei University of Technology Hefei 230009 China Optical Engineering Laboratory Heilbronn University Heilbronn 74081 Germany Department of Mechanical and Electrical Engineering Xiamen University Xiamen 361005 China

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A PYRAMID-BASED BLOCK OF SKEWERS FOR PIXEL PURITY INDEX FOR ENDMEMBER EXTRACTION IN HYPERSPECTRAL imageRY

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International Journal of High Speed Electronics and Systems 2008年第2期18卷 469-482页

作者： CHEIN-I CHANG MINGKAI HSUEH WEIMIN LIU CHAO-CHENG WU FARZEEN CHAUDHRY GREGORY SOLYAR ANTONIO PLAZA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA Computer Science Department University of Extremadura Avda. de la Universidad s/n 10.071 Caceres Spain

Pixel Purity Index (PPI) has been widely used for endmember extraction. Recently, an approach using blocks of skewers was proposed by Theiler et al., called blocks of skewers (BOS) method, to improve computation of the PPI. It utilizes a block of skewers to reduce number of calculations of dot products operated by the PPI on each skewers with all data sample vectors. Unfortunately, the BOS method also suffers from the same drawbacks that the PPI does in terms of several parameters which are needed to be determined a priori . Besides, it also has an additional parameter, block size, B needed to be determined where no guideline is provided of how to select this parameter. In this paper, the BOS method is also investigated. Most importantly, a new pyramid-based block design for the BOS method is also introduced as opposed to the cube-based block designed used by Theiler et al.'s BOS. One major advantage of our proposed pyramid-based BOS over Theiler et al.'s cube-design BOS is the hardware design for Field Programmable Gate Arrays (FPGAs) implementation.

关键词： Blocks of skewers (BOS) method Field Programmable Gate Arrays (FPGAs) Pixel purity index (PPI)

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A study on the effect of camera motion on human visual attention

A study on the effect of camera motion on human visual atten...

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IEEE International Conference on image processing

作者： Golnaz Abdollahian Zygmunt Pizlo Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Psychological Sciences Purdue University West Lafayette IN USA

The aim of this paper is to examine the effect of camera motion in user generated video with respect to human visual attention. Having a more accurate human attention model is particularly useful in applications such as video summarization where the identification of visual importance is crucial to the quality of the results. Most models proposed thus far, have not considered camera motion as an independent factor in identifying visual saliency. In this study eye movement was recorded while subjects watched videos with different types of camera motion. The distribution of the fixation points indicated high correlation between the visual saliency and the type and direction of camera movement. The statistical tests confirmed that the results are statistically significant.

关键词： Cameras Humans Layout Testing Video sequences image processing Laboratories Psychology Information analysis Shape

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A Comparative Study of Classification Techniques for Knowledge-Assisted image Analysis

A Comparative Study of Classification Techniques for Knowled...

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International Workshop on image Analysis for Multimedia Interactive Services, WIAMIS

作者： G. T. Papadopoulos K. Chandramouli V. Mezaris I. Kompatsiaris E. Izquierdo M. G. Strintzis Informatics and Telematics Institute Centre for Research and Technology Hellas Thessaloniki Greece Information Processing Laboratory Electrical & Computer Engineering Department Aristotle University of Thessaloniki Thessaloniki Greece Multimedia and Vision Research Group Queen Mary University of London London UK

In this paper, four individual approaches to region classification for knowledge-assisted semantic image analysis are presented and comparatively evaluated. All of the examined approaches realize knowledge-assisted analysis via implicit knowledge acquisition, i.e. are based on machine learning techniques such as support vector machines (SVMs), self organizing maps (SOMs), genetic algorithm (GA)and particle swarm optimization (PSO). Under all examined approaches, each image is initially segmented and suitable low-level descriptors are extracted for every resulting segment. Then, each of the aforementioned classifiers is applied to associate every region with a predefined high-level semantic concept. An appropriate evaluation framework has been employed for the comparative evaluation of the above algorithms under varying experimental conditions.

关键词： image analysis image segmentation Algorithm design and analysis Knowledge acquisition Machine learning Support vector machines Support vector machine classification Self organizing feature maps Genetic algorithms Particle swarm optimization

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4D wavelet noise suppression of MR diffusion tensor data

4D wavelet noise suppression of MR diffusion tensor data

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

作者： Hesamoddin Jahanian Azadeh Yazdan-Shahmorad Hamid Soltanian-Zadeh Department of Radiology Henry Ford Health System Image Analysis and Communications Laboratory Detroit MI USA Department of Biomedical Engineering University of Michigan Ann Arbor MI USA Controll and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran

Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio (SNR) of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.

关键词： Tensile stress Diffusion tensor imaging Signal to noise ratio Noise reduction Magnetic noise Anisotropic magnetoresistance Eigenvalues and eigenfunctions In vivo Magnetic resonance imaging Wavelet analysis

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Feature fusion as a practical solution toward noncooperative iris recognition

Feature fusion as a practical solution toward noncooperative...

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International Conference on Information Fusion

作者： Nima Tajbakhsh Babak N. Araabi Hamid Soltanianzadeh Control & Intelligent Processing Center of Excellence School of Electrical & Computer Engineering University of Tehran Tehran Iran School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

In noncooperative Iris recognition one should deal with uncontrolled behavior of the subject as well as uncontrolled lighting conditions. That means imperfect focus, contrast, brightness, and orientation among the others. To cope with this situation we propose to take iris images at both near infrared (NIR) and visible light (VL) and use them simultaneously for recognition. In this paper, a novel approach for iris recognition is proposed so that extracted features of NIR and VL images are fused to improve the recognition rate. When the images do not have enough quality due to focus, contrast, etc., effects of feature fusion is more pronounced. This is the situation in UTIRIS database, which is used in our experiments. Experimental results show that the proposed approach, especially in small training samples, leads to a remarkable improvement on recognition rate compared with either NIR or VL recognition.

关键词： Iris Feature extraction Discrete cosine transforms image recognition Databases Training Humans

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Constrained optimization of nonparametric entropy-based segmentation of brain structures

Constrained optimization of nonparametric entropy-based segm...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi Asl Hamid Soltanian-Zadeh School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence (CIPCE) School of Electrical and Computer Engineering University of Tehran Tehran Iran Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA

We propose a constrained, three-dimensional, nonparametric, entropy-based, coupled, multi-shape approach to segment subcortical brain structures from magnetic resonance images (MRI). The proposed method uses PCA to develop shape models that capture structural variability. It integrates geometrical relationship between different structures into the algorithm by coupling them (limiting their independent deformations). On the other hand, to allow variations among coupled structures, it registers each structure separately when building the shape models. It defines an entropy-based energy function, which is minimized using quasi-Newton algorithm. To this end, probability density functions (pdf) are estimated iteratively using nonparametric Parzen window method. In the optimization algorithm, constraints are used to improve segmentation quality. These constraints are extracted from training data. Sample results are given for the segmentation of caudate, hippocampus, and putamen, illustrating highly superior performance of the proposed method compared to the most similar methods in the literature.

关键词： Constraint optimization Brain Iterative algorithms image segmentation Shape Couplings Magnetic resonance Magnetic resonance imaging Principal component analysis Buildings

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