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A new semi-supervised algorithm for hyperspectral image classification based on spectral unmixing concepts

A new semi-supervised algorithm for hyperspectral image clas...

引用

Workshop on Hyperspectral image and Signal Processing: Evolution in Remote Sensing, WHISPERS

作者： Alberto Villa Jun Li Antonio Plaza José M. Bioucas-Dias GIPSA-Laboratory Signal & Image Department Grenoble Institute of Technology (INPG) France Faculty of Electrical and Computer Engineering University of Iceland Iceland Instituto de Telecomunicações Instituto Superior Técnico TU Lisbon Lisbon Portugal Hyperspectral Computing Laboratory Department of Technology of Computers and Communications University of Extremadura Caceres Spain

Spectral unmixing is a fast growing area in hyperspectral image analysis. Many algorithms have been recently developed to retrieve pure spectral components (endmembers) and determine their abundance fractions in mixed pixels, which dominate hyperspectral images. However, possible connections between spectral unmixing concepts and classification algorithms have been rarely investigated. In this work, we propose a new method to perform semi-supervised hyperspectral image classification exploiting the information retrieved with spectral unmixing. The proposed method integrates a well-established discriminative classifier (multinomial logistic regression) with linear spectral unmixing. Furthermore, the proposed method uses a new active sampling approach which takes into account spatial context when generating new samples. The proposed method is experimentally validated using both simulated and real hyperspectral data sets.

关键词： Hyperspectral imaging Logistics Training Signal processing algorithms Accuracy

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Unscented transformation for depth from motion-blur in videos

Unscented transformation for depth from motion-blur in video...

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2010 IEEE computer Society Conference on computer vision and Pattern Recognition - Workshops, CVPRW 2010

作者： Paramanand, C. Rajagopalan, A.N. Image Processing and Computer Vision Laboratory Department of Electrical Engineering Indian Institute of Technology Madras Chennai 600 036 India

ISBN: (纸本)9781424470297

In images and videos of a 3D scene, blur due to camera shake can be a source of depth information. Our objective is to find the shape of the scene from its motion-blurred observations without having to restore the original image. In this paper, we pose depth recovery as a recursive state estimation problem. We show that the relationship between the observation and the scale factor of the motion-blur kernel associated with the depth at a point is nonlinear and propose the use of the unscented Kalman filter for state estimation. The performance of the proposed method is evaluated on many examples. © 2010 IEEE.

关键词： State estimation

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Cardiac motion estimation from MR image sequences

Cardiac motion estimation from MR image sequences

引用

Digital image Synthesis and Inverse Optics

作者： Prince, Jerry L. Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD 21218 United States

A method to reconstruct motion using magnetic resonance (MR) imaging is presented. Magnetic resonance imaging tagging is used to create intensity gradients within objects, where the intensity would otherwise be largely constant. Since the tag field decays with T1, a modified optical flow algorithm is developed and implemented. Object parameters T1, T 2, and D0 are required to be known across the image at the time the tag field is created;motion is then determined by computing optical flow between pairs of images and estimating the implied motion reference map at each pixel site. © 2010 SPIE-The International Society for Optical engineering.

关键词： Magnetic resonance imaging

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Multibody motion segmentation using the geometry of 6 points in 2D images

Multibody motion segmentation using the geometry of 6 points...

引用

2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Nordberg, Klas Zografos, Vasileios Computer Vision Laboratory Department of Electrical Engineering Linköping University Sweden

ISBN: (纸本)9780769541099

We propose a method for segmenting an arbitrary number of moving objects using the geometry of 6 points in 2D images to infer motion consistency. This geometry allows us to determine whether or not observations of 6 points over several frames are consistent with a rigid 3D motion. The matching between observations of the 6 points and an estimated model of their configuration in 3D space, is quantified in terms of a geometric error derived from distances between the points and 6 corresponding lines in the image. This leads to a simple motion inconsistency score, based on the geometric errors of 6 points that in the ideal case should be zero when the motion of the points can be explained by a rigid 3D motion. Initial point clusters are determined in the spatial domain and merged in motion trajectory domain based on this score. Each point is then assigned to the cluster, which gives the lowest score. Our algorithm has been tested with real image sequences from the Hopkins155 database with very good results, competing with the state of the art methods, particularly for degenerate motion sequences. In contrast to the motion segmentation methods based on multi-body factorization, that assume an affine camera model, the proposed method allows the mapping from 3D space to the 2D image to be fully projective. © 2010 IEEE.

关键词： Geometry

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Head pose estimation from images using Canonical Correlation analysis

Head pose estimation from images using Canonical Correlation...

引用

2010 IEEE 39th Applied imagery Pattern Recognition Workshop, AIPR 2010

作者： Foytik, Jacob Asari, Vijayan K. Youssef, Menatoallah Tompkins, R. Cortland Computer Vision and Wide Area Surveillance Laboratory Department of Electrical and Computer Engineering University of Dayton Dayton OH United States

ISBN: (纸本)9781424488339

Head pose estimation, though a trivial task for the human visual system, remains a challenging problem for computer vision systems. The task requires identifying the modes of image variance that directly pertain to pose changes, while generalizing across face identity and mitigating other image variances. Conventional methods such as Principal Component analysis (PCA) fail to identify the true relationship between the observed space and the pose variable, while supervised methods such as Linear Discriminant analysis (LDA) neglect the continuous nature of pose variation and take a discrete multi-class approach. We present a method for estimating head pose using Canonical Correlation analysis (CCA), where pose variation is regarded as a continuous variable and is represented by a manifold in feature space. The proposed technique directly identifies the underlying dimension that maximizes correlation between the observed image and pose variable. It is shown to increase estimation accuracy and provide a more compact image representation that better captures pose features. Additionally, an enhanced version of the system is proposed that utilizes Gabor filters for providing pose sensitive input to the correlation based system. The preprocessed input serves to increase the overall accuracy of the pose estimation system. The accuracy of the techniques is evaluated using the Pointing '04 and CUbiC FacePix(30) pose varying face databases and is shown to produce a lower estimation error when compared to both PCA and LDA based methods. © 2010 IEEE.

关键词： Principal component analysis

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Road network extraction using edge detection and spatial voting

Road network extraction using edge detection and spatial vot...

引用

2010 20th International Conference on Pattern Recognition, ICPR 2010

作者： Sirmaçek, Beril Ünsalan, Cem Computer Vision Research Laboratory Department of Electrical and Electronics Engineering Yeditepe University Istanbul 34755 Turkey

ISBN: (纸本)9780769541099

Road network detection from very high resolution satellite images is important for two main reasons. First, the detection result can be used in automated map making. Second, the detected network can be used in trajectory planning for unmanned aerial vehicles. Although an expert can label road pixels in a given satellite image, this operation is prone to errors. Therefore, an automated system is needed to detect the road network in a given satellite image in a robust manner. In this study, we propose a novel approach to detect the road network from a given panchromatic Ikonos satellite image. Our method has five main steps. First, we apply a nonlinear bilateral filtering to smooth the given image. Then, we extract Canny edges and the gradient information as local features. Using these local features, we generate a spatial voting matrix. This voting matrix indicates the possible locations of the road network pixels. By processing this voting matrix in an iterative manner, we detect initial road pixels. Finally, we apply a tracking algorithm on the voting matrix to detect the missing road pixels. We tested our method on various satellite images and provided the extracted road networks in the experiments section. © 2010 IEEE.

关键词： Edge detection

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Retinal vessel segmentation using color image morphology and local binary patterns

Retinal vessel segmentation using color image morphology and...

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Iranian Conference on Machine vision and image Processing

作者： Zabihi, Seyed Mohsen Delgir, Morteza Pourreza, Hamid Reza Computer Engineering Department Ferdowsi University of Mashhad Iran Machine Vision Laboratory Eye Images Analysis Research Group Iran

ISBN: (纸本)9781424497065

In this paper, an automated retinal vessel extraction algorithm is represented. A multi-scale morphological algorithm is used for local contrast enhancement of color retinal image. This method enhances vessels not only in color image, but also in the three color components of that image. After feature extraction using LBP and spatial image processing, MLP as a classifier segments the pixels into vessels and non-vessels. Finally, in post processing step, we used binary morphologies for noise removing and smoothing. The performance of the proposed algorithm is tested on the images of DRIVE database. © 2010 IEEE.

关键词： Local binary pattern

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Text extraction using document structure features and support vector machines

Text extraction using document structure features and suppor...

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11th IASTED International Conference on computer Graphics and Imaging, CGIM 2010

作者： Zagoris, Konstantinos Papamarkos, Nikos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

ISBN: (纸本)9780889868243

In order to successfully locate and retrieve document images such as technical articles and newspapers, a text localization technique must be employed. The proposed method detects and extracts homogeneous text areas in document images indifferent to font types and size by using connected components analysis to detect blocks of foreground objects. Next, a descriptor that consists of a set of structural features is extracted from the merged blocks and used as input to a trained Support Vector Machines (SVM). Finally, the output of the SVM classifies the block as text or not.

关键词： Support vector machines

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Applying deep-layered clustering to mammography image analytics

Applying deep-layered clustering to mammography image analyt...

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2010 Biomedical Science and engineering Conference, BSEC 2010: Biomedical Research and analysis in Neuroscience, BRAiN

作者： Rose, Derek C. Arel, Itamar Karnowski, Thomas P. Paquit, Vincent C. Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN 37996 United States Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831 United States

ISBN: (纸本)9781424467143

This paper details a methodology and preliminary results for applying a hierarchy of clustering units to mammographic image data. The identification of patients with breast cancer through the detection of microcalcifications and masses is a demanding classification problem;minimal false negatives are desired while simultaneously avoiding false positives that cause unnecessary cost to patients and health institutions. This research examines a segmented look at mammograms for computer aided detection with the goal of reliably labeling regions of interest requiring the attention of a radiologist. Classification is achieved by employing the building blocks, namely unsupervised clustering, of a deep learning architecture in tandem with a standard feed-forward neural network. Early results show promise for creating a classification engine that handles high-dimensional data with minimum engineering of image features, with a per-image patch sensitivity of 0.96 and specificity of 0.99. © 2010 IEEE.

关键词： Clustering algorithms

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Deep spatiotemporal feature learning with application to image classification

Deep spatiotemporal feature learning with application to ima...

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9th International Conference on Machine Learning and Applications, ICMLA 2010

作者： Karnowski, Thomas P. Arel, Itamar Rose, Derek Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831 United States Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN 37996 United States

ISBN: (纸本)9780769543000

Deep machine learning is an emerging framework for dealing with complex high-dimensionality data in a hierarchical fashion which draws some inspiration from biological sources. Despite the notable progress made in the field, there remains a need for an architecture that can represent temporal information with the same ease that spatial information is discovered. In this work, we present new results using a recently introduced deep learning architecture called Deep Spatio-Temporal Inference Network (DeSTIN). DeSTIN is a discriminative deep learning architecture that combines concepts from unsupervised learning for dynamic pattern representation together with Bayesian inference. In DeSTIN the spatiotemporal dependencies that exist within the observations are modeled inherently in an unguided manner. Each node models the inputs by means of clustering and simple dynamics modeling while it constructs a belief state over the distribution of sequences using Bayesian inference. We demonstrate that information from the different layers of this hierarchical system can be extracted and utilized for the purpose of pattern classification. Earlier simulation results indicated that the framework is highly promising, consequently in this work we expand DeSTIN to a popular problem, the MNIST data set of handwritten digits. The system as a preprocessor to a neural network achieves a recognition accuracy of 97.98% on this data set. We further show related experimental results pertaining to automatic cluster adaptation and termination. © 2010 IEEE.

关键词： Hierarchical systems

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