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Accurate Estimation of Pulmonary Nodule's Growth Rate in CT Images with Nonrigid Registration and Precise Nodule Detection and Segmentation

Accurate Estimation of Pulmonary Nodule's Growth Rate in CT ...

引用

ieee computer society conference on computer vision and pattern recognition workshops

作者： Yuanjie Zheng Chandra Kambhamettu Thomas Bauer Karl Steiner Video/Image Modeling and Synthesis (VMS) Lab. Department of Computer Science University of Delaware USA Helen F. Graham Cancer Center Christiana Care Health Services USA Department of Electrical Engineering University of Delaware USA

ISBN: (纸本)9781424439942

We propose a new tumor growth measure for pulmonary nodules in CT images, which can account for the tumor deformation caused by the inspiration level's difference. It is accomplished with a new nonrigid lung registration process, which can handle the tumor expanding/shrinking problem occurring in many conventional nonrigid registration methods. The accurate nonrigid registration is performed by weighting the matching cost of each voxel, based on the result of a new nodule detection approach and a powerful nodule segmentation algorithm. Comprehensive experiments show the high accuracy of our algorithms and the promising results of our new tumor growth measure.

关键词： Computed tomography Image segmentation Costs computer science Electric variables measurement Lung neoplasms Cancer detection High-resolution imaging Lesions computer aided diagnosis

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Echocardiographic contour extraction with local and global priors through boosting and level sets

Echocardiographic contour extraction with local and global p...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Ayse Betul Oktay Yusuf Sinan Akgul Department of Computer Engineering Gebze Institute of Technology (GYTE) Gebze Kocaeli Turkey

Extracting endocardium and epicardium from echocardiographic images is a challenging task because of large amounts of noise, signal drop-out, unrelated structures, and unseen wall parts. This paper introduces a new technique that automatically extracts cardiac borders by incorporating local and global priors through boosting and level set methods. The shape-based global prior is incorporated into the system by regularly re-initializing the level set surface under the influence of the expert detected contours. The local priors with image and temporal information are learned through boosting. The proposed system has many advantages. First, boosting encodes the knowledge about the image information and the temporal cardiac wall motion effectively by using spatiotemporal filters. Second, the local priors can use any features from the images including different filters and intensity profiles. Furthermore, other hard constraints like local shape, texture, distance, etc. can be added to local features effectively. The system is validated on echocardiograms and the results are found to be promising.

关键词： Boosting Level set Shape Image segmentation Data mining Spatiotemporal phenomena Spatial filters computer vision Noise shaping Noise level

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Is there a general structure for grammars?

Is there a general structure for grammars?

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： David Mumford Division of Applied Mathematics Brown University USA

Summary form only given. Linguists have proposed dozens of formalisms for grammars and now vision is weighing in with its versions based on its needs. Ulf Grenander has proposed general pattern theory, and has used grammar-like graphical parses of "thoughts" in the style of AI. One wants a natural, simple formalism treating all these cases. I want to pose this as a central problem in modeling intelligence. pattern theory started in the 70's with the ideas of Ulf Grenander and his school at Brown. The aim is to analyze from a statistical point of view the patterns in all "signals" generated by the world, whether they be images, sounds, written text, DNA or protein strings, spike trains in neurons, time series of prices or weather, etc. pattern theory proposes that the types of patterns-and the hidden variables needed to describe these patterns - found in one class of signals will often be found in the others and that their characteristic variability will be similar. The underlying idea is to find classes of stochastic models which can capture all the patterns that we see in nature, so that random samples from these models have the same "look and feel" as the samples from the world itself. Then the detection of patterns in noisy and ambiguous samples can be achieved by the use of Bayes' rule, a method that can be described as "analysis by synthesis".

关键词： pattern analysis Artificial intelligence Educational institutions Image analysis Signal analysis Time series analysis Signal generators DNA Proteins Neurons

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Adventures in Archiving and Using Three Years of Webcam Images

Adventures in Archiving and Using Three Years of Webcam Imag...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Nathan Jacobs Walker Burgin Richard Speyer David Ross Robert Pless Department of Computer Science and Engineering Washington University

ISBN: (纸本)9781424439942

Recent descriptions of algorithms applied to images archived from webcams tend to underplay the challenges in working with large data sets acquired from uncontrolled webcams in real environments. In building a database of images captured from 1000 webcams, every 30 minutes for the last 3 years, we observe that these cameras have a wide variety of failure modes. This paper details steps we have taken to make this dataset more easily useful to the research community, including (a) tools for finding stable temporal segments, and stabilizing images when the camera is nearly stable, (b) visualization tools to quickly summarize a years worth of image data from one camera and to give a set of exemplars that highlight anomalies within the scene, and (c) integration with LabelMe, allowing labels of static features in one image of a scene to propagate to the thousands of other images of that scene. We also present proof-of-concept algorithms showing how this data conditioning supports several problems in inferring properties of the scene from image data.

关键词： Layout Cameras Jacobian matrices Image databases Image segmentation computer science Data engineering Data visualization Data mining Calibration

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Fast features for time constrained object detection

Fast features for time constrained object detection

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Gary Overett Lars Petersson NICTA Canberra Australia

This paper concerns itself with the development and design of fast features suitable for time constrained object detection. Primarily we consider three aspects of feature design; the form of the precomputed datatype (e.g. the integral image), the form of the features themselves (i.e. the measurements made of an image), and the models/weak- learners used to construct weak classifiers (class, non-class statistics). The paper is laid out as a guide to feature designers, demonstrating how appropriate choices in combining the above three characteristics can prevent bottlenecks in the run-time evaluation of classifiers. This leads to reductions in the computational time of the features themselves and, by providing more discriminant features, reductions in the time taken to reach specific classification error rates. Results are compared using variants of the well known Haar-like feature types, Rectangular Histogram of Oriented Gradient (RHOG) features and a special set of Histogram of Oriented Gradient features which are highly optimized for speed. Experimental results suggest the adoption of this set of features for time-critical applications. Time-constrained comparisons are presented using pedestrian and road sign detection problems. Comparison results are presented on time-error plots, which are a replacement of the traditional ROC performance curves.

关键词： Time factors Object detection Histograms Error analysis Boosting Roads computer vision Change detection algorithms Australia Statistics

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Tracking of Cell Populations to Understand their Spatio-Temporal Behavior in Response to Physical Stimuli

Tracking of Cell Populations to Understand their Spatio-Temp...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： David House Matthew L. Walker Zheng Wu Joyce Y. Wong Margrit Betke Departments of Computer Science Boston University Biomedical Engineering Boston University

ISBN: (纸本)9781424439942

We have developed methods for segmentation and tracking of cells in time-lapse phase-contrast microscopy images. Our multi-object Bayesian algorithm detects and tracks large numbers of cells in presence of clutter and identifies cell division. To solve the data association problem, the assignment of current measurements to cell tracks, we tested various cost functions with both an optimal and a fast, suboptimal assignment algorithm. We also propose metrics to quantify cell migration properties, such as motility and directional persistence, and compared our findings of cell migration with the standard random walk model. We measured how cell populations respond to the physical stimuli presented in the environment, for example, the stiffness property of the substrate. Our analysis of hundreds of spatio-temporal cell trajectories revealed significant differences in the behavioral response of fibroblast cells to changes in hydrogel conditions.

关键词： Microscopy Cost function Image segmentation Current measurement Fibroblasts Image analysis Shape Tracking Iterative algorithms Motion analysis

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Lonely but Attractive: Sparse Color Salient Points for Object Retrieval and Categorization

Lonely but Attractive: Sparse Color Salient Points for Objec...

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Julian Stottinger Allan Hanbury Theo Gevers Nicu Sebe Vienna University of Technology Austria University of Amsterdam The Netherlands

ISBN: (纸本)9781424439942

Local image descriptors computed in areas around salient points in images are essential for many algorithms in computer vision. Recent work suggests using as many salient points as possible. While sophisticated classifiers have been proposed to cope with the resulting large number of descriptors, processing this large amount of data is computationally costly. In this paper, computational methods are proposed to compute salient points designed to allow a reduction in the number of salient points while maintaining state of the art performance in image retrieval and object recognition applications. To obtain a more sparse description, a color salient point and scale determination framework is proposed operating on color spaces that have useful perceptual and saliency properties. This allows for the necessary discriminative points to be located, allowing a significant reduction in the number of salient points and obtaining an invariant (repeatability) and discriminative (distinctiveness) image description. Experimental results on large image datasets show that the proposed method obtains state of the art results with the number of salient points reduced by half. This reduction in the number of points allows subsequent operations, such as feature extraction and clustering, to run more efficiently. It is shown that the method provides less ambiguous features, a more compact description of visual data, and therefore a faster classification of visual data.

关键词： Image retrieval Object recognition computer vision Image sampling Data mining Personal digital assistants Color Feature extraction Mobile computing Mobile handsets

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Action recognition via local descriptors and holistic features

Action recognition via local descriptors and holistic featur...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xinghua Sun Mingyu Chen Alexander Hauptmann Nanjing University of Science and Technology of PLA Nanjing China Carnegie Mellon University Pittsburgh PA USA

In this paper we propose a unified action recognition framework fusing local descriptors and holistic features. The motivation is that the local descriptors and holistic features emphasize different aspects of actions and are suitable for the different types of action databases. The proposed unified framework is based on frame differencing, bag-of-words and feature fusion. We extract two kinds of local descriptors, i.e. 2D and 3D SIFT feature descriptors, both based on 2D SIFT interest points. We apply Zernike moments to extract two kinds of holistic features, one is based on single frames and the other is based on motion energy image. We perform action recognition experiments on the KTH and Weizmann databases, using Support Vector Machines. We apply the leave-one-out and pseudo leave-N-out setups, and compare our proposed approach with state-of-the-art results. Experiments show that our proposed approach is effective. Compared with other approaches our approach is more robust, more versatile, easier to compute and simpler to understand.

关键词： Spatial databases Hidden Markov models Support vector machines Surveillance Feature extraction Sun Image databases Robustness Human computer interaction Motion analysis

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Robust feature matching in 2.3μs

Robust feature matching in 2.3μs

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Simon Taylor Edward Rosten Tom Drummond Department of Engineering University of Cambridge UK

ISBN: (纸本)9781424439942

In this paper we present a robust feature matching scheme in which features can be matched in 2.3μs. For a typical task involving 150 features per image, this results in a processing time of 500μs for feature extraction and matching. In order to achieve very fast matching we use simple features based on histograms of pixel intensities and an indexing scheme based on their joint distribution. The features are stored with a novel bit mask representation which requires only 44 bytes of memory per feature and allows computation of a dissimilarity score in 20ns. A training phase gives the patch-based features invariance to small viewpoint variations. Larger viewpoint variations are handled by training entirely independent sets of features from different viewpoints. A complete system is presented where a database of around 13, 000 features is used to robustly localise a single planar target in just over a millisecond, including all steps from feature detection to model fitting. The resulting system shows comparable robustness to SIFT [3] and Ferns [14] while using a tiny fraction of the processing time, and in the latter case a fraction of the memory as well.

关键词： Robustness Histograms Runtime Image edge detection Spatial databases computer vision Image databases Data mining Feature extraction Indexing

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Image Registration by Minimization of Mapping Complexity

Image Registration by Minimization of Mapping Complexity

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ieee computer society conference on computer vision and pattern recognition workshops

作者： Andriy Myronenko Xubo Song Dept. of Science and Engineering School of Medicine Oregon Health and Science University

ISBN: (纸本)9781424439942

The criterion for the correct spatial alignment is a key component in image registration. We formulate the registration problem as one that finds the spatial and intensity mappings of minimal complexity that make images exactly equal. We do not assume any parametric forms of these functions, and estimate them within variational calculus. We analytically solve for non-stationary intensity mapping, eliminate it from the objective function and arrive with a new similarity measure. We name it the Mapping Complexity (MC) similarity measure, because it achieves the optimum when intensity and spatial mappings are of minimal complexity. Due to its general formulation, the similarity measure works both for complex intensity relationships (e.g. multimodal registration) and for spatially-varying intensity distortions. Our similarity measure can be interpreted as the one that favors one image to lie mostly within a span of the leading eigenvectors of the kernel matrix, where the kernel matrix is constructed from the second image. We introduce a fast algorithm to compute the similarity measure. In particular, we introduce a fast kernel vector product (FKVP) algorithm, which is of general interest in computer vision. We demonstrate the accuracy of the new similarity measure on several mono- and multi-modal examples with complex intensity non-uniformities.

关键词： Image registration Distortion measurement Kernel Calculus computer vision Magnetic resonance imaging Convolution Filters Biomedical imaging Particle measurements

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