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image-based indoor localization system based on 3D SfM model

Image-based indoor localization system based on 3D SfM model

引用

Intelligent Robots and Computer Vision XXXI: Algorithms and Techniques

作者： Lu, Guoyu Kambhamettu, Chandra Video/Image Modeling and Synthesis Lab. University of Delaware Newark DE United States

ISBN: (纸本)9780819499424

Indoor localization is an important research topic for both of the robot and signal processing communities. In recent years, image-based localization is also employed in indoor environment for the easy availability of the necessary equipment. After capturing an image and sending it to an image database, the best matching image is returned with the navigation information. By allowing further camera pose estimation, the image-based localization system with the use of Structure-from-Motion reconstruction model can achieve higher accuracy than the methods of searching through a 2D image database. However, this emerging technique is still only on the use of outdoor environment. In this paper, we introduce the 3D SfM model based image-based localization system into the indoor localization task. We capture images of the indoor environment and reconstruct the 3D model. On the localization task, we simply use the images captured by a mobile to match the 3D reconstructed model to localize the image. In this process, we use the visual words and the approximate nearest neighbor methods to accelerate the process of nding the query feature's correspondences. Within the visual words, we conduct linear search in detecting the correspondences. From the experiments, we nd that the image-based localization method based on 3D SfM model gives good localization result based on both accuracy and speed. © 2013 Copyright SPIE.

关键词： Nearest neighbor search

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UTILIZING image-BASED FEATURES IN BIOMEDICAL DOCUMENT CLASSIFICATION

UTILIZING IMAGE-BASED FEATURES IN BIOMEDICAL DOCUMENT CLASSI...

引用

IEEE International Conference on image Processing

作者： Kaidi Ma Hogyeong Jeong Rohith MV Gowri Somanath Ryan Tarpine Kyle Schutter Dorothea Blostein Sorin Istrail Chandra Kambhamettu Hagit Shatkay Computational Biomedicine University of Delaware Video/Image Modeling and Synthesis Lab CS Dept. University of Delaware Google Center for Computational Molecular Biology CS Dept. Brown University School of Computing Queen's University Kingston

ISBN: (纸本)9781479983407

images form a rich information source, which remains under-utilized in biomedical document classification. We present here work that uses both image- and text-based features in order to identify articles of interest, in this case, pertaining to cis-regulatory modules in the context of gene-networks. Extending on our new idea, which we have recently introduced, of using OCR-based features to identify DNA contents in images, we combine image and text based classifiers to categorize documents as relevant or irrelevant to cis-regulatory modules. Using a set of hundreds of articles, marked by experts as relevant or irrelevant to cis-regulatory modules, we train/test image and text based classifiers, as well as classifiers integrating both. Our results indicate that the latter show the best performance with Recall, F-measure and Utility measures all above 0.9, demonstrating the significance of incorporating image data, and specifically OCR-based features, into the document categorization process. Moreover, the use of character distribution properties to represent images is directly relevant to other biomedical images containing text (e.g. RNA, proteins). Diagrams and other images containing text are also prevalent outside the biomedical domain, hence the work stands to be applicable and beneficial in other application areas.

关键词： image-based features OCR Document classification Document-representation Bioinformatics images optical character recognition Bioinformatics Classifiers texts PA700 Regulatory Module imagery (Psychotherapy) biomedical Information source program area image data

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Knowing where I am: Exploiting multi-task learning for multi-view indoor image-based localization 25

Knowing where I am: Exploiting multi-task learning for multi...

引用

25th British Machine Vision Conference, BMVC 2014

作者： Lu, Guoyu Yan, Yan Sebe, Nicu Kambhamettu, Chandra Video/Image Modeling and Synthesis Lab University of Delaware United States Department of Information Engineering and Computer Science University of Trento Italy

Indoor localization has attracted a large amount of applications in mobile and robotics area, especially in vast and sophisticated environments. Most indoor localization methods are based on cellular base stations and WiFi signals. Such methods require users to carry additional equipment. Localization accuracy is largely based on the beacon distribution. image-based localization is mainly applied for outdoor environments to overcome the problem caused by weak GPS signals in large building areas. In this paper, we propose to localize images in indoor environments from multi-view settings. We use Structure-from-Motion to reconstruct the 3D environment of our indoor buildings to provide users a clear view of the whole building's indoor structure. Since the orientation information is also quite essential for indoor navigation, images are localized based on a multi-task learning method, which treats each view direction classification as a task. We perform image retrieval based on the trained multi-task classifiers. Thus the orientation of the image together with the location information is achieved. We assign the pose of the retrieved image to the query image calculated from SfM reconstruction with the use of bundle adjustment to refine the pose estimation. © 2014. The copyright of this document resides with its authors.

关键词： Indoor positioning systems

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Structure-from-Motion Reconstruction Based on Weighted Hamming Descriptors

Structure-from-Motion Reconstruction Based on Weighted Hammi...

引用

International Joint Conference on Neural Networks

作者： Guoyu Lu Vincent Ly Chandra Kambhamettu Video/Image Modeling and Synthesis Lab University of Delaware

ISBN: (纸本)9781479914821

We propose a pipelined methods to reduce memory consumption of large-scale Structure-from-Motion reconstruction with the use of unsorted images extracted from photo collection websites. Recent research is able to reconstruct cities based on extracted images from photo collection websites. SIFT feature is used to find the correspondences between two images. For the large-scale reconstruction with unsorted images, the system needs to store all the descriptors and feature points information in memory to search for correspondences. As each SIFT descriptor is a 128 dimensional real-value vector, storing all the descriptors would consume a significant amount of memory. Based on this limitation, we project the high dimensional features into a low-dimensional space using a learned projection matrix. After projection, the distance of the descriptors belonging to the same point in 3D space is decreased;the distance of the descriptors belonging to the different points is increased. Furthermore, we learn a mapping function, which maps the real-value descriptor into binary code. As Hamming descriptors contain only two value options per bit and the length of the descriptor is limited, there are usually multiple descriptors having the same Hamming distance to the query descriptor. In dealing with this problem, we give different weights to each dimension and rank each bit of the Hamming descriptor based on each dimensions discriminant power;this contributes to reduce the ambiguity in matching the descriptors. The experiments show that our method achieves dense reconstruction results with less than 10 percent of the original memory consumption.

关键词： image reconstruction Three-dimensional displays Memory management Computers Feature extraction Cameras Equations

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Large-scale structure-from-motion reconstruction with small memory consumption 13

Large-scale structure-from-motion reconstruction with small ...

引用

11th International Conference on Advances in Mobile Computing and Multimedia, MoMM 2013

作者： Lu, Guoyu Ly, Vincent Kambhamettu, Chandra Video/Image Modeling and Synthesis Lab. University of Delaware Newark DE United States

ISBN: (纸本)9781450321068

Structure-from-Motion reconstruction is to recover the 3 dimensional structure from 2 dimensional images. Recent research in this field demonstrates the ability to reconstruct cities based on images extracted from a photo collection website;SIFT feature is typically extracted to detect correspondences between images. For the reconstruction of large scale unsorted images, the system is required to store all features and points information in the memory to search for correspondences. As SIFT feature is a 128 dimensional real-valued vector, storing each descriptor would consume a significant amount of memory. Due to this limitation, we propose to project the high-dimensional feature into a lower-dimensional space by using a new learned projection matrix while still maintaining the property of the original features. Hence, the result of this projection will shorten the distance among descriptors of the same point while lengthening the distance among descriptors of different points. These projected descriptors use Hellinger distance for calculation of the similarity between features. Furthermore, we learn a mapping function, which will map the real-valued descriptor into binary code coping with the variation of correspondence searching method. Experiments demonstrate that our method achieve excellent results with limited memory requirement. © 2013 ACM.

关键词： image reconstruction

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Mobile Scene Flow synthesis

Mobile Scene Flow Synthesis

引用

IEEE International Symposium on Multimedia

作者： Vincent Ly Chandra Kambhamettu Video/Image Modeling and Synthesis (VIMS) Lab University of Delaware Newark USA

Scene flow is the motion of the 3D world, it is used in obstacle avoidance, slow motion interpolation, surveillance, studying human behavior, and much more. A mobile implementation of scene flow can greatly increase the flexibility of scene flow applications. Furthermore, combining multiple scene flows to one panoramic scene flow can aid in these tasks: allowing coverage of dead spots in surveillance, studying human motion from multiple views, or simply obtain a larger motion view of a scene. In this paper, a robust algorithm for building panoramic scene flow obtained from a mobile device is described. Since scene flow is estimated from a mobile device, observer motion is compensated for using least squares fitting over the entire scene. Furthermore, noise is reduced and outliers are eliminated from the 3D motion field using motion model fitting. The results demonstrate the effectiveness of the suggested algorithm for constructing a scene flow panorama from moving sources.

关键词： Three-dimensional displays Cameras Estimation Optical imaging Feature extraction Smart phones Solid modeling

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VADANA: A dense dataset for facial image analysis

VADANA: A dense dataset for facial image analysis

引用

International Conference on Computer Vision Workshops (ICCV Workshops)

作者： Gowri Somanath MV Rohith Chandra Kambhamettu Video/Image Modeling and Synthesis Lab University of Delaware USA Video/Image Modeling and Synthesis Lab Department of Computer and Information Sciences University of Delaware USA

Analysis of face images has been the topic of in-depth research with wide spread applications. Face recognition, verification, age progression studies are some of the topics under study. In order to facilitate comparison and benchmarking of different approaches, various datasets have been released. For the specific topics of face verification with age progression, aging pattern extraction and age estimation, only two public datasets are currently available. The FGNET and MORPH datasets contain a large number of subjects, but only a few images are available for each subject. We present a new dataset, VADANA, which complements them by providing a large number of high quality digital images for each subject within and across ages (depth vs. breadth). It provides the largest number of intrapersonal pairs, essential for better training and testing. The images also offer a natural range of pose, expression and illumination variation. A parallel version with aligned faces is also created. Additionally, we provide relationships between subjects. We demonstrate the difference and difficulty of VADANA by testing with state-of-the-art algorithms. Our findings from experiments show how VADANA can aid further research on different types of verification algorithms. The variety of characteristics our data offers facilitate testing and benchmarking of other facial analysis algorithms.

关键词： Face Training Lighting Databases Algorithm design and analysis Testing Aging

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Single-image optical center estimation from vignetting and tangential gradient symmetry

Single-image optical center estimation from vignetting and t...

引用

Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yuanjie Zheng Chandra Kambhamettu Stephen Lin Video/Image Modeling and Synthesis (VIMS) Lab Department of Computer Science University of Delaware Newark DE USA Microsoft Research Asia Beijing China

In this paper, we propose a method for estimating the optical center of a camera given only a single image with vignetting. This is accomplished by identifying the center of the vignetting effect in the image through an analysis of semicircular tangential gradients (SCTGs). For a given image pixel, the SCTG is the image gradient along the tangential direction of the circle centered at the currently estimated optical center and passing through the pixel. We show that for natural images with vignetting, the distribution of SCTGs is generally symmetric if the optical center is estimated accurately, but is skewed otherwise. By minimizing the asymmetry of the SCTG distribution with nonlinear optimization, our method is able to obtain reliable estimates of the optical center. Experiments on simulated and real vignetting images demonstrate the effectiveness of this technique.

关键词： Nonlinear optics Optical distortion Optical sensors Cameras Optical attenuators Layout Geometrical optics Calibration Lenses Pixel

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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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Vector field resampling using local streamline approximation

Vector field resampling using local streamline approximation

引用

作者： Thomas, M. Kambhamettu, C. Geiger, C.A. Dept. of Computer and Info. Sciences Video/Image Modeling and Synthesis Lab University of Delaware United States Center for Climatic Research Dept. of Geography University of Delaware United States

ISBN: (纸本)9781424421756

In this paper, we propose an algorithm to resample coarse vector fields in order to obtain vector fields of a higher density. Unlike the typical linear interpolation scheme, our algorithm attempts to identify streamline characteristics in the flow field, and uses local polynomial parameterization of the flow to perform interpolation. Quantitative validation of the streamline oriented algorithm indicate that the Mean Square Error of the flow field obtained is more accurate than those obtained by bilinear interpolation schemes. © 2008 IEEE.

关键词： Flow fields

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