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Spectral Segmentation Using Cartoon-Texture Decomposition and Inner Product-Based Metric

Spectral Segmentation Using Cartoon-Texture Decomposition an...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Wallace Casaca Afonso Paiva Luis Gustavo Nonato Instituto de Ciências Matemáticas e de Computação USP Brazil

This paper presents a user-assisted image partition technique that combines cartoon-texture decomposition, inner product-based similarity metric, and spectral cut into a unified framework. The cartoon-texture decomposition is used to first split the image into textured and texture-free components, the latter being used to define a gradient-based inner-product function. An affinity graph is then derived and weights are assigned to its edges according to the inner product-based metric. Spectral cut is computed on the affinity graph so as to partition the image. The computational burden of the spectral cut is mitigated by a fine-to-coarse image representation process, which enables moderate size graphs that can be handled more efficiently. The partitioning can be steered by interactively by changing the weights of the graph through user strokes. Weights are updated by combining the texture component computed in the first stage of our pipeline and a recent harmonic analysis technique that captures waving patterns. Finally, a coarse-to-fine interpolation is applied in order to project the partition back onto the original image. The suitable performance of the proposed methodology is attested by comparisons against state-of-art spectral segmentation methods.

关键词： image segmentation image edge detection Measurement Pipelines Vectors Eigenvalues and eigenfunctions Interpolation

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Progressive Background image Generation of Surveillance Traffic Videos Based on a Temporal Histogram Ruled by a Reward/Penalty Function

Progressive Background Image Generation of Surveillance Traf...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Rensso Mora Colque Guillermo C´mara-Ch´vez Departamento de Computação Universidade Federal de Ouro Preto Ouro Preto Brazil

In most surveillance traffic systems the background image is used for segmenting the moving objects. In this paper, we propose a method for progressive and constant renewal of the background image of a video sequence. Our method is divided in three parts: frame difference to get an actual information of the video sequence, reward and penalty function that modifies a temporal histogram, and the background generation uses the information found in the histogram. The principal characteristic of the method is the constant regeneration of the histogram using neighborhood information of intensity levels, penalizing the occurrence of foreign objects and rewarding the values of static objects. For that, the histogram values are modified regionally and its variation correspond to a normal distribution. The principal advantages of our proposed method lies in the progressive increment of the background pixel intensity occurrences, the fast reaction to changes between foreground and background segments and the simplicity of the method that can be used in real time applications. At the end of this paper, experimental results demonstrated the feasibility of the proposed technique in different weather conditions and traffic situations.

关键词： Histograms Vehicles Indexes Cameras Video sequences Videos Gaussian distribution

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Frameworks for GPU Accelerators: A comprehensive evaluation using 2D/3D image registration

Frameworks for GPU Accelerators: A comprehensive evaluation ...

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symposium on Application Specific Processors, SASP

作者： Richard Membarth Frank Hannig Jürgen Teich Mario Körner Wieland Eckert Department of Computer Science University of Erlangen Nuremberg Germany Siemens Healthcare Sector Forchheim Germany

In the last decade, there has been a dramatic growth in research and development of massively parallel many-core architectures like graphics hardware, both in academia and industry. This changed also the way programs are written in order to leverage the processing power of a multitude of cores on the same hardware. In the beginning, programmers had to use special graphics programming interfaces to express general purpose computations on graphics hardware. Today, several frameworks exist to relieve the programmer from such tasks. In this paper, we present five frameworks for parallelization on GPU Accelerators, namely RapidMind, PGI Accelerator, HMPP Workbench, OpenCL, and CUDA. To evaluate these frameworks, a real world application from medical imaging is investigated, the 2D/3D image registration.

关键词： graphics processing unit graphics Hardware Three dimensional displays image registration Parallel processing

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Elucidating the Relations among Seeded image Segmentation Methods and their Possible Extensions

Elucidating the Relations among Seeded Image Segmentation Me...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Paulo Miranda Alexandre Falcao Department of Computer Science IME University of São Paulo Sao Paulo Sao Paulo Brazil Information Systems Department LIV-Institute of Computing Unicamp Campinas Sao Paulo Brazil

Many image segmentation algorithms have been proposed, specially for the case of binary segmentation (object/background) in which hard constraints (seeds) are provided interactively. Recently, several theoretical efforts were made in an attempt to unify their presentation and clarify their relations. These relations are usually pointed out textually or depicted in the form of a table of parameters of a general energy formulation. In this work we introduce a more general diagram representation which captures the connections among the methods, by means of conventional relations from set theory. We formally instantiate several methods under this diagram, including graph cuts, power watersheds, fuzzy connectedness, grow cut, distance cuts, and others, which are usually presented as unrelated methods. The proposed diagram representation leads to a more elucidated view of the methods, being less restrictive than the tabular representation. It includes new relations among methods, besides bringing together the connections gathered from different works. It also points out some promising unexplored intermediate regions, which can lead to possible extensions of the existing methods. We also demonstrate one of such possible extensions, which is used to effectively combine the strengths of region and local contrast features.

关键词： image segmentation Communities Labeling Frequency control Transforms Force

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Corner Detection within a Multiscale Framework

Corner Detection within a Multiscale Framework

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： I´lis Paula F´tima Medeiros Francisco Nivando Bezerra Daniela Ushizima Departamento de Engineering de Teleinformática Universidade Federal do Cearà Fortaleza Ceara Brazil Ciência e Tecnologia Institute Fed. de Educação Maracana Ceara Brazil Math and Visualization Groups Lawrence Berkeley National Laboratory Berkeley CA USA

We present a new multi scale method for corner detection. The proposed algorithm embodies an undecorated wavelet decomposition of the angulations signal of a shape contour to identify significant points on it. It detects peaks that persist through several scales from the correlation signal between scales of its non-orthogonal sub-band decompositions. These peaks correspond to high curvature points (HCPs). Furthermore, we compare the proposed method with others available in the literature, including the well-known curvature scale-space (CSS) method. The quantitative assessment of the algorithms is provided by some figures of merit (FOM) measures that indicate which method better detects the relevant points in terms of compaction and shape reconstruction.

关键词： Shape Detectors Correlation Cascading style sheets Feature extraction Equations image edge detection

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High Level Classification for Pattern Recognition

High Level Classification for Pattern Recognition

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Thiago Silva Thiago Cupertino Liang Zhao Department of Computer Sciences Institute of Mathematics and Computer Science (ICMC) University of São Paulo Sao Carlos Sao Paulo Brazil

Traditional data classification techniques consider only physical features of input data in order to construct their hypotheses. On the other hand, the human (animal) brain performs both low and high order learning and it has facility to identify patterns according to the semantic meaning of input data. In this paper, we propose a data classification technique by combining the low level and the high level learning. The low level term can be implemented by any classification technique, while the high level classification is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies data instances by their physical features, while the latter measures the compliance to the pattern formation of the data. Our study shows that the proposed technique can not only realize classification according to the pattern formation, but it is also able to improve the performance of traditional classification techniques. An application on handwritten digits recognition is performed, revealing that higher classification rates can be obtained when we have a proper mixture of low and high level classifiers.

关键词： Semantics Feature extraction Complex networks Pattern formation Artificial neural networks Training Data mining

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Parallelized Automatic False Defect Detection using GPUs during Mask Inspection

Parallelized Automatic False Defect Detection using GPUs dur...

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31st International symposium on Photomask Technology

作者： Pereira, Mark Maji, Manabendra Gangadhar, Budde Pai, Ravi R. Nigam, Ila Parchuri, Anil SoftJin Technol Pvt Ltd India EPIP Bangalore 560066 Karnataka India

ISBN: (纸本)9780819487919

The mask inspection and review process is a vital part of mask preparation technology and consumes a significant amount of mask preparation time. As the patterns on a mask become smaller and more complex, the need for a highly precise mask inspection system with a high detection sensitivity and low number of false defects becomes greater. A low number of false defects is desirable as the results of the mask inspection are typically reviewed manually by an operator in the mask shop. However, due to various reasons, the probable mask defects identified by any mask inspection machine could include significant number of false defects. The false defects could be due to registration or focus errors between the defect and reference images (Die-to-Die or D2D comparison), CCD (Charge-coupled device) errors in the camera, noisy pixels etc. These false defects cannot be ignored and require the operator to review them manually before classifying them as false defects. This takes valuable time and effort of the mask inspector and increases the turn-around-time of mask inspection. We propose a software tool which automatically detects most of the false defects generated due to registration and CCD errors in the mask inspection system. It is quite common to find several thousands of defects (real as well as false defects) during mask inspection. We have observed that significant percentage of these false defects are due to registration and CCD errors in defect and reference images during D2D inspection. Automatic detection of registration and CCD errors requires image processing to be done on the defect images. This process is typically, time consuming. However, image processing algorithms are well suited for parallelization. We explore the use of GPUs to speed up the false defect detection process by analyzing the defects in parallel on multiple cores of a GPU. In addition, GPUs are inexpensive, readily available and can be plugged in to any desktop computer which makes it easier

关键词： Mask Inspection False Defect False Alarm GPU (graphics processing Unit) CCD (Charge-coupled device) D2D (Die to die) IC (Integrated Circuit) GL (Grey Level)

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Watershed-Based Segmentation of the Midsagittal Section of the Corpus Callosum in Diffusion MRI

Watershed-Based Segmentation of the Midsagittal Section of t...

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Pedro Freitas Leticia Rittner Simone Appenzeller Roberto Lotufo School of Electrical and Computer Engineering State University of Campinas-UNICAMP Campinas Sao Paulo Brazil Department of Medicine Rheumatology Unit State University of Campinas-UNICAMP Campinas Sao Paulo Brazil

The corpus callosum (CC) is one of the most important white matter structures of the brain, interconnecting the two cerebral hemispheres. The corpus callosum is related to several neurodegenerative diseases and, as segmentation is usually the first step for studies in this structure, it is important to have a robust method for CC segmentation. We propose here a new approach for fully automatic segmentation of the CC in the magnetic resonance diffusion tensor images. The method uses the watershed transform and is performed on the fractional anisotropy (FA) map weighted by the projection of the principal eigenvector in the left-right direction. It first computes the section of the CC in the midsagittal slice and uses it as a seed for the 3D volume segmentation. Experiments with real diffusion MRI data showed that the proposed method is able to quickly segment the CC without any user intervention, with great results when compared to manual segmentation. Since it is simple, fast and does not require parameter settings, the proposed method is well suited for clinical applications.

关键词： image segmentation Transforms Diffusion tensor imaging Anisotropic magnetoresistance Three dimensional displays Tensile stress Manuals

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View Clustering of Wide-Baseline N-views for Photo Tourism

View Clustering of Wide-Baseline N-views for Photo Tourism

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brazilian symposium on computer graphics and image processing (SIBGRAPI)

作者： Aveek Brahmachari Sudeep Sarkar Computer Science and Engineering University of South Florida Tampa FL USA

The problem of view clustering is concerned with finding connected sets of overlapping views in a collection of photographs. The view clusters can be used to organize a photo collection, traverse through a collection, or for 3D structure estimation. For large datasets, geometric matching of all image pairs via pose estimation to decide on content overlap is not viable. The problem becomes even more acute if the views in the collection are separated by wide baselines, i.e. we do not have a dense view sampling of the 3D scene that leads to increase in computational cost of epipolar geometry estimation and matching. We propose an efficient algorithm for clustering of such many weakly overlapping views, based on opportunistic use of epipolar geometry estimation for only a limited number of image pairs. We cast the problem of view clustering as finding a tree structure graph over the views, whose weighted links denote likelihood of view overlap. The optimization is done in an iterative fashion starting from an minimum spanning tree based on photometric distances between image pairs. At each iteration step, we rule out edges with low confidence of overlap between the respective views, based on epipolar geometry estimates. The minimum spanning tree is recomputed and the process is repeated until there is no further change in the link structure. We show results on the images in the 2010 Nokia Grand Challenge Dataset that contains images with low overlap with each other.

关键词： Geometry Estimation Cameras Training Three dimensional displays Global Positioning System Clustering algorithms

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Metrics for comparing explicit representations of interconnected biological networks

Metrics for comparing explicit representations of interconne...

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1st IEEE symposium on Biological Data Visualization, BioVis 2011

作者： Mayerich, David Bjornsson, Chris Taylor, Jonothan Roysam, Badrinath Beckman Institute for Advanced Science and Technology University of Illinois United States Center for Biotechnology Interdisciplinary Studies Rensselaer Polytechnic Institute United States Department of Electrical Computer and Systems Engineering University of Houston United States

ISBN: (纸本)9781467300025

One of the major goals in biomedical image processing is accurate segmentation of networks embedded in volumetric data sets. Biological networks are composed of a meshwork of thin filaments that span large volumes of tissue. Examples of these structures include neurons and microvasculature, which can take the form of both hierarchical trees and fully connected networks, depending on the imaging modality and resolution. Network function depends on both the geometric structure and connectivity. Therefore, there is considerable demand for algorithms that segment biological networks embedded in three-dimensional data. While a large number of tracking and segmentation algorithms have been published, most of these do not generalize well across data sets. One of the major reasons for the lack of general-purpose algorithms is the limited availability of metrics that can be used to quantitiatively compare their effectiveness against a pre-constructed ground-truth. In this paper, we propose a robust metric for measuring and visualizing the differences between network models. Our algorithm takes into account both geometry and connectivity to measure network similarity. These metrics are then mapped back onto an explicit model for visualization. © 2011 IEEE.

关键词： Volumetric analysis

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