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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vijay Mahadevan Nuno Vasconcelos Department of Electrical and Computer Engineering University of California San Diego USA

A biologically inspired approach for automated visual tracking is proposed. In this approach it is hypothesized that target initialization and tracking are a consequence of saliency mechanisms that guide the deployment of visual attention. The recently proposed discriminant center-surround saliency model, is used to derive the tracking framework. In this framework, automatic tracker initialization is achieved using bottom-up saliency with motion features, while the tracking problem is formulated as one of continuous target-background classification, implemented using saliency in two stages. The first, or learning stage, combines a focus of attention mechanism and bottom-up saliency to identify a maximally discriminant set of features for target detection. The second, or detection stage, uses a feature based attention mechanism and a target-tuned top-down discriminant saliency detector, to detect the target. Overall, the tracker iterates between learning discriminant features from the target location in a video frame and detecting the location of the target in the next frame. To implement this tracker, well known properties of the statistics of natural images are exploited leading to computational efficiency. Experimental results comparing the proposed method to the state of the art in tracking are presented, showing improved performance.

关键词： Target tracking Visualization Object detection Biology Feature extraction Robustness

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Computing Diffeomorphic Paths for Large Motion Interpolation

Computing Diffeomorphic Paths for Large Motion Interpolation

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ieee conference on computer vision and pattern recognition

作者： Dohyung Seo Jeffrey Ho Baba C. Vemuri Department of Electrical and Computer Engineering University of Florida Department of Computer and Information Sciences and Engineering University of Florida

ISBN: (纸本)9781467364102

In this paper, we introduce a novel framework for computing a path of diffeomorphisms between a pair of input diffeomorphisms. Direct computation of a geodesic path on the space of diffeomorphisms Diff(Ω) is difficult, and it can be attributed mainly to the infinite dimensionality of Diff(Ω). Our proposed framework, to some degree, bypasses this difficulty using the quotient map of Diff(Ω) to the quotient space Diff(M)/Diff(M)_μ obtained by quotienting out the subgroup of volume-preserving diffeomorphisms Diff(M)_μ. This quotient space was recently identified as the unit sphere in a Hilbert space in mathematics literature, a space with well-known geometric properties. Our framework leverages this recent result by computing the diffeomorphic path in two stages. First, we project the given diffeomorphism pair onto this sphere and then compute the geodesic path between these projected points. Second, we lift the geodesic on the sphere back to the space of diffeomerphisms, by solving a quadratic programming problem with bilinear constraints using the augmented Lagrangian technique with penalty terms. In this way, we can estimate the path of diffeomorphisms, first, staying in the space of diffeomorphisms, and second, preserving shapes/volumes in the deformed images along the path as much as possible. We have applied our framework to interpolate intermediate frames of frame-sub-sampled video sequences. In the reported experiments, our approach compares favorably with the popular Large Deformation Diffeomorphic Metric Mapping framework (LDDMM).

关键词： Geodesic Spheres Diffeomorphism Byways Frameworks Hilbert space Quotient Interpolation Pairing

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Wii Remote calibration using the sensor bar

Wii Remote calibration using the sensor bar

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Alparslan Yildiz Abdullah Akay Yusuf Sinan Akgul GIT Vision Laboratory Gebze Institute of Technology (GYTE) Kocaeli Turkey

Wii Remote is the standard controller of the game console Nintendo Wii®. Despite its low cost, it has a very high performance and high resolution infrared camera. It also has a built-in chip for tracking up to 4 points from the infrared images. With these properties, the Wii Remote has attracted several attempts to calibrate and use them for metric 3D information. Thanks to Wii Remote's 4 point tracking hardware, people were able to calibrate Wii Remotes using carefully designed square patterns with infrared LEDs at each corner. However, it is not easy for average Wii Remote users to implement such calibration tools. In this paper, we give an overview of the current Wii Remote calibration implementations and present a novel method to fully calibrate multiple Wii Remotes using only two points, such as the LEDs of the Sensor Bar that comes with the Wii console. For the 3D reconstruction, only two Wii Remotes and a PC is needed. We also demonstrate the usability of a full 3D tracker with a multiplayer game that receives inputs from up to 4 users with only two Wii Remotes.

关键词： Calibration Cameras Image reconstruction Three dimensional displays Light emitting diodes Materials Measurement uncertainty

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Fast opposite weight learning rules with application in breast cancer diagnosis

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computerS IN BIOLOGY AND MEDICINE 2013年第1期43卷 32-41页

作者： Saki, Fatemeh Tahmasbi, Amir Soltanian-Zadeh, Hamid Shokouhi, Shahriar B. Univ Texas Dallas Dept Elect Engn Richardson TX 75080 USA Univ Texas SW Med Ctr Dallas Dept Immunol Dallas TX 75390 USA Univ Tehran Sch Elect & Comp Engn CIPCE Tehran 14395515 Iran Henry Ford Hlth Syst Radiol Image Anal Lab Detroit MI 48202 USA IUST Dept Elect Engn Tehran *** Iran

Classification of breast abnormalities such as masses is a challenging task for radiologists. computer-aided Diagnosis (CADx) technology may enhance the performance of radiologists by assisting them in classifying patterns into benign and malignant categories. Although Neural Networks (NN) such as Multilayer Perceptron (MLP) have drawbacks, namely long training times, a considerable number of CADx systems employ NN-based classifiers. The reason being that they provide high accuracy when they are appropriately trained. In this paper, we introduce three novel learning rules called Opposite Weight Back Propagation per pattern (OWBPP), Opposite Weight Back Propagation per Epoch (OWBPE), and Opposite Weight Back Propagation per pattern in Initialization (OWBPI) to accelerate the training procedure of an MLP classifier. We then develop CADx systems for the diagnosis of breast masses employing the traditional Back Propagation (BP), OWBPP, OWBPE and OWBPI algorithms on MLP classifiers. We quantitatively analyze the accuracy and convergence rate of each system. The results suggest that the convergence rate of the proposed OWBPE algorithm is more than 4 times faster than that of the traditional BP. Moreover, the CADx systems which use OWBPE classifier on average yield an area under Receiver Operating Characteristic (ROC), i.e. Az, of 0.928, a False Negative Rate (FNR) of 9.9% and a False Positive Rate (FPR) of 11.94%. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Classification computer-aided diagnosis Mammography Opposition-based learning

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Assessment of facial wrinkles as a soft biometrics

Assessment of facial wrinkles as a soft biometrics

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International conference on Automatic Face and Gesture recognition

作者： Nazre Batool Sima Taheri Rama Chellappa Department of Electrical and Computer Engineering and Center for Automation Research UMIACS University of Maryland College Park MD USA Department of Computer Science and Center for Automation Research UMIACS University of Maryland College Park MD USA

ISBN: (纸本)9781467355452

This paper presents results on the assessment of facial wrinkles as a soft biometrics. Recently, several micro features such as moles, scars, freckles, etc. have been used in addition to more common facial features for face recognition. The discriminative power of facial wrinkles has not been evaluated. In this paper we present results of our experiments on evaluating the discriminative power of wrinkles in recognizing subjects. We treat a set of facial wrinkles from an image as a curve pattern and find similarity between curve patterns from two subjects. Several metrics based on Hausdorff distance and curve-to-curve correspondences are introduced to quantify the similarity. A simple bipartite graph matching algorithm is introduced to find correspondences between curves from two patterns. We present experiments on data sets using manually extracted and automatically detected wrinkles. The recognition rate for these data sets using only the binary forehead wrinkle curve patterns exceeds 65% at rank 1 and 90% at rank 4.

关键词： Measurement Face recognition Biometrics (access control) Bipartite graph pattern matching Shape

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Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation

Graph-Based Optimization with Tubularity Markov Tree for 3D ...

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ieee conference on computer vision and pattern recognition

作者： Ning Zhu Albert C. S. CHUNG Lo Kwee-Seong Medical Image Analysis Laboratory Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9781467364102

In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT. The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256*256*144 image).

关键词： segmentation Segmentation 3-glucosaminoglycan sulfate Blood Vessels pressure vessels Construction methods image segmentation point location Learning-based method empirical approach alternative energy Skeletons bone structure Markov TPSG1 gene

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Decoding Children's Social Behavior

Decoding Children's Social Behavior

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conference on computer vision and pattern recognition (CVPR)

作者： James M. Rehg Gregory D. Abowd Agata Rozga Mario Romero Mark A. Clements Stan Sclaroff Irfan Essa Opal Y. Ousley Yin Li Chanho Kim Hrishikesh Rao Jonathan C. Kim Liliana Lo Presti Jianming Zhang Denis Lantsman Jonathan Bidwell Zhefan Ye Center for Behavior Imaging School of Interactive Computing Georgia Institute of Technology USA Kungliga Tekniska Hogskolan Stockholm SE Center for Behavior Imaging School of ECE Georgia Institute of Technology USA Department of Computer Science Boston University USA Department of Psychiatry and Behavioral Sciences Emory University USA

We introduce a new problem domain for activity recognition: the analysis of children's social and communicative behaviors based on video and audio data. We specifically target interactions between children aged 1-2 years and an adult. Such interactions arise naturally in the diagnosis and treatment of developmental disorders such as autism. We introduce a new publicly-available dataset containing over 160 sessions of a 3-5 minute child-adult interaction. In each session, the adult examiner followed a semi-structured play interaction protocol which was designed to elicit a broad range of social behaviors. We identify the key technical challenges in analyzing these behaviors, and describe methods for decoding the interactions. We present experimental results that demonstrate the potential of the dataset to drive interesting research questions, and show preliminary results for multi-modal activity recognition.

关键词： Face Cameras Protocols Decoding Pediatrics Training Testing

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Jointly Aligning and Segmenting Multiple Web Photo Streams for the Inference of Collective Photo Storylines

Jointly Aligning and Segmenting Multiple Web Photo Streams f...

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ieee conference on computer vision and pattern recognition

作者： Gunhee Kim Eric P. Xing School of Computer Science Carnegie Mellon University

ISBN: (纸本)9781467364102

With an explosion of popularity of online photo sharing, we can trivially collect a huge number of photo streams for any interesting topics such as scuba diving as an outdoor recreational activity class. Obviously, the retrieved photo streams are neither aligned nor calibrated since they are taken in different temporal, spatial, and personal perspectives. However, at the same time, they are likely to share common storylines that consist of sequences of events and activities frequently recurred within the topic. In this paper, as a first technical step to detect such collective storylines, we propose an approach to jointly aligning and segmenting uncalibrated multiple photo streams. The alignment task discovers the matched images between different photo streams, and the image segmentation task parses each image into multiple meaningful regions to facilitate the image understanding. We close a loop between the two tasks so that solving one task helps enhance the performance of the other in a mutually rewarding way. To this end, we design a scalable message-passing based optimization framework to jointly achieve both tasks for the whole input image set at once. With evaluation on the new Flickr dataset of 15 outdoor activities that consist of 1.5 millions of images of 13 thousands of photo streams, our empirical results show that the proposed algorithms are more successful than other candidate methods for both tasks.

关键词： Inference Collective streams streams rivers Image understanding Religious Missions image segmentation images Alignment Outdoor pursuits

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3D Pose tracking of walker users' lower limb with a structured-light camera on a moving platform

3D Pose tracking of walker users' lower limb with a structur...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Richard Zhi-Ling Hu Adam Hartfiel James Tung Adel Fakih Jesse Hoey Pascal Poupart David R. Cheriton School of Computer Science University of Waterloo Canada Department of Systems Design Engineering University of Waterloo Canada Department of Kinesiology University of Waterloo Canada

Tracking and understanding human gait is an important step towards improving elderly mobility and safety. Our research team is developing a vision-based tracking system that estimates the 3D pose of a wheeled walker user's lower limbs with a depth sensor, Kinect, mounted on the moving walker. Our tracker estimates 3D poses from depth images of the lower limbs in the coronal plane in a dynamic, uncontrolled environment. We employ a probabilistic approach based on particle filtering, with a measurement model that works directly in the 3D space and another measurement model that works in the projected image space. Empirical results show that combining both measurements, assuming independence between them, yields tracking results that are better than with either one alone. Experiments are conducted to evaluate the performance of the tracking system with different users. We demonstrate that the tracker is robust against unfavorable conditions such as partial occlusion, missing observations, and deformable tracking target. Also, our tracker does not require user intervention or manual initialization commonly required in most trackers.

关键词： Cameras Three dimensional displays Hidden Markov models Leg Solid modeling Target tracking

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Recognize Human Activities from Partially Observed Videos

Recognize Human Activities from Partially Observed Videos

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ieee conference on computer vision and pattern recognition

作者： Yu Cao Daniel Barrett Andrei Barbu Siddharth Narayanaswamy Haonan Yu Aaron Michaux Yuewei Lin Sven Dickinson Jeffrey Mark Siskind Song Wang Department of Computer Science and Engineering University of South Carolina School of Electrical and Computer Engineering Purdue University Department of Computer Science University of Toronto

ISBN: (纸本)9781467364102

Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: I) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.

关键词： Videotapes Human Activities Segments Alkali

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