IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 5 • May 2017

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Displaying Results 1 - 19 of 19
  • Table of Contents

    Publication Year: 2017, Page(s): C1
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  • Cover

    Publication Year: 2017, Page(s): C2
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  • A Stable Analytical Framework for Isometric Shape-from-Template by Surface Integration

    Publication Year: 2017, Page(s):833 - 850
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1883 KB) | HTML iconHTML Multimedia Media

    Shape-from-Template (SfT) reconstructs the shape of a deforming surface from a single image, a 3D template and a deformation prior. For isometric deformations, this is a well-posed problem. However, previous methods which require no initialization break down when the perspective effects are small, which happens when the object is small or viewed from larger distances. That is, they do not handle a... View full abstract»

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  • Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo

    Publication Year: 2017, Page(s):851 - 864
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1964 KB) | HTML iconHTML Multimedia Media

    We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based on phase modulation, our camera enables general exposure profiles. This results in added flexibility and requires novel computational approaches. To address this challenge we propose a generative probabilistic model that accurately relates latent i... View full abstract»

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  • Co-Saliency Detection via a Self-Paced Multiple-Instance Learning Framework

    Publication Year: 2017, Page(s):865 - 878
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1173 KB) | HTML iconHTML

    As an interesting and emerging topic, co-saliency detection aims at simultaneously extracting common salient objects from a group of images. On one hand, traditional co-saliency detection approaches rely heavily on human knowledge for designing handcrafted metrics to possibly reflect the faithful properties of the co-salient regions. Such strategies, however, always suffer from poor generalization... View full abstract»

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  • Detecting Flying Objects Using a Single Moving Camera

    Publication Year: 2017, Page(s):879 - 892
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2073 KB) | HTML iconHTML

    We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We argue that solving such a difficult problem requires combining both appearance and motion cues. To this end we propose a regression-based approac... View full abstract»

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  • Dynamic Whitening Saliency

    Publication Year: 2017, Page(s):893 - 907
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1556 KB) | HTML iconHTML Multimedia Media

    General dynamic scenes involve multiple rigid and flexible objects, with relative and common motion, camera induced or not. The complexity of the motion events together with their strong spatio-temporal correlations make the estimation of dynamic visual saliency a big computational challenge. In this work, we propose a computational model of saliency based on the assumption that perceptual relevan... View full abstract»

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  • Efficient Activity Detection in Untrimmed Video with Max-Subgraph Search

    Publication Year: 2017, Page(s):908 - 921
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1114 KB) | HTML iconHTML

    We propose an efficient approach for activity detection in video that unifies activity categorization with space-time localization. The main idea is to pose activity detection as a maximum-weight connected subgraph problem. Offline, we learn a binary classifier for an activity category using positive video exemplars that are “trimmed” in time to the activity of interest. Then, given ... View full abstract»

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  • Elastic Functional Coding of Riemannian Trajectories

    Publication Year: 2017, Page(s):922 - 936
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1519 KB) | HTML iconHTML

    Visual observations of dynamic phenomena, such as human actions, are often represented as sequences of smoothly-varying features. In cases where the feature spaces can be structured as Riemannian manifolds, the corresponding representations become trajectories on manifolds. Analysis of these trajectories is challenging due to non-linearity of underlying spaces and high-dimensionality of trajectori... View full abstract»

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  • Frequency-Domain Transient Imaging

    Publication Year: 2017, Page(s):937 - 950
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2481 KB) | HTML iconHTML Multimedia Media

    A transient image is the optical impulse response of a scene, which also visualizes the propagation of light during an ultra-short time interval. In contrast to the previous transient imaging which samples in the time domain using an ultra-fast imaging system, this paper proposes transient imaging in the frequency domain using a multi-frequency time-of-flight (ToF) camera. Our analysis reveals the... View full abstract»

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  • Generation of Duplicated Off-Line Signature Images for Verification Systems

    Publication Year: 2017, Page(s):951 - 964
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1399 KB) | HTML iconHTML

    Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and ... View full abstract»

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  • Hyperbolic Harmonic Mapping for Surface Registration

    Publication Year: 2017, Page(s):965 - 980
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1642 KB) | HTML iconHTML

    Automatic computation of surface correspondence via harmonic map is an active research field in computer vision, computer graphics and computational geometry. It may help document and understand physical and biological phenomena and also has broad applications in biometrics, medical imaging and motion capture industries. Although numerous studies have been devoted to harmonic map research, limited... View full abstract»

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  • $L_0$ Regularized Stationary-Time Estimation for Crowd Analysis

    Publication Year: 2017, Page(s):981 - 994
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (2307 KB) | HTML iconHTML

    In this paper, we tackle the problem of stationary crowd analysis which is as important as modeling mobile groups in crowd scenes and finds many important applications in crowd surveillance. Our key contribution is to propose a robust algorithm for estimating how long a foreground pixel becomes stationary. It is much more challenging than only subtracting background because failure at a single fra... View full abstract»

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  • Nasal Patches and Curves for Expression-Robust 3D Face Recognition

    Publication Year: 2017, Page(s):995 - 1007
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1283 KB) | HTML iconHTML Multimedia Media

    The potential of the nasal region for expression robust 3D face recognition is thoroughly investigated by a novel five-step algorithm. First, the nose tip location is coarsely detected and the face is segmented, aligned and the nasal region cropped. Then, a very accurate and consistent nasal landmarking algorithm detects seven keypoints on the nasal region. In the third step, a feature extraction ... View full abstract»

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  • Screening Tests for Lasso Problems

    Publication Year: 2017, Page(s):1008 - 1027
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (1573 KB) | HTML iconHTML

    This paper is a survey of dictionary screening for the lasso problem. The lasso problem seeks a sparse linear combination of the columns of a dictionary to best match a given target vector. This sparse representation has proven useful in a variety of subsequent processing and decision tasks. For a given target vector, dictionary screening quickly identifies a subset of dictionary columns that will... View full abstract»

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  • Super Normal Vector for Human Activity Recognition with Depth Cameras

    Publication Year: 2017, Page(s):1028 - 1039
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract |PDF file iconPDF (703 KB) | HTML iconHTML

    The advent of cost-effectiveness and easy-operation depth cameras has facilitated a variety of visual recognition tasks including human activity recognition. This paper presents a novel framework for recognizing human activities from video sequences captured by depth cameras. We extend the surface normal to polynormal by assembling local neighboring hypersurface normals from a depth sequence to jo... View full abstract»

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  • Introducing IEEE Collabratec

    Publication Year: 2017, Page(s): 1040
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  • Cover

    Publication Year: 2017, Page(s): C3
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  • Cover

    Publication Year: 2017, Page(s): C4
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Aims & Scope

The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is published monthly. Its editorial board strives to present most important research results in areas within TPAMI's scope.

Full Aims & Scope

Meet Our Editors

Editor-in-Chief
Sven Dickinson
University of Toronto
e-mail: sven@cs.toronto.edu