IEEE Transactions on Pattern Analysis and Machine Intelligence

Issue 4 • April 2016

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  • Table of Contents

    Publication Year: 2016, Page(s): C1
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  • IEEE Transactions on Pattern Analysis and Machine Intelligence Editorial Board

    Publication Year: 2016, Page(s): C2
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  • Guest Editorial: Special Section on CVPR 2013

    Publication Year: 2016, Page(s):625 - 626
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  • Adopting Abstract Images for Semantic Scene Understanding

    Publication Year: 2016, Page(s):627 - 638
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1132 KB) | HTML iconHTML

    Relating visual information to its linguistic semantic meaning remains an open and challenging area of research. The semantic meaning of images depends on the presence of objects, their attributes and their relations to other objects. But precisely characterizing this dependence requires extracting complex visual information from an image, which is in general a difficult and yet unsolved problem. ... View full abstract»

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  • Photometric Ambient Occlusion for Intrinsic Image Decomposition

    Publication Year: 2016, Page(s):639 - 651
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1458 KB) | HTML iconHTMLCode

    We present a method for computing ambient occlusion (AO) for a stack of images of a Lambertian scene from a fixed viewpoint. Ambient occlusion, a concept common in computer graphics, characterizes the local visibility at a point: it approximates how much light can reach that point from different directions without getting blocked by other geometry. While AO has received surprisingly little attenti... View full abstract»

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  • Map-Based Probabilistic Visual Self-Localization

    Publication Year: 2016, Page(s):652 - 665
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2014 KB) | HTML iconHTML

    Accurate and efficient self-localization is a critical problem for autonomous systems. This paper describes an affordable solution to vehicle self-localization which uses odometry computed from two video cameras and road maps as the sole inputs. The core of the method is a probabilistic model for which an efficient approximate inference algorithm is derived. The inference algorithm is able to util... View full abstract»

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  • Leveraging the Wisdom of the Crowd for Fine-Grained Recognition

    Publication Year: 2016, Page(s):666 - 676
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1361 KB) | HTML iconHTML

    Fine-grained recognition concerns categorization at sub-ordinate levels, where the distinction between object classes is highly local. Compared to basic level recognition, fine-grained categorization can be more challenging as there are in general less data and fewer discriminative features. This necessitates the use of a stronger prior for feature selection. In this work, we include humans in the... View full abstract»

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  • Cascades of Regression Tree Fields for Image Restoration

    Publication Year: 2016, Page(s):677 - 689
    Cited by:  Papers (3)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1914 KB) | HTML iconHTML

    Conditional random fields (CRFs) are popular discriminative models for computer vision and have been successfully applied in the domain of image restoration, especially to image denoising. For image deblurring, however, discriminative approaches have been mostly lacking. We posit two reasons for this: First, the blur kernel is often only known at test time, requiring any discriminative approach to... View full abstract»

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  • Intrinsic Scene Properties from a Single RGB-D Image

    Publication Year: 2016, Page(s):690 - 703
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2828 KB) | HTML iconHTML

    In this paper, we present a technique for recovering a model of shape, illumination, reflectance, and shading from a single image taken from an RGB-D sensor. To do this, we extend the SIRFS (“shape, illumination and reflectance from shading”) model, which recovers intrinsic scene properties from a single image [1] . Though SIRFS works well on neatly segmented images of objects, it pe... View full abstract»

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  • Bayesian Constrained Local Models Revisited

    Publication Year: 2016, Page(s):704 - 716
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1354 KB) | HTML iconHTML

    This paper presents a novel Bayesian formulation for aligning faces in unseen images. Our approach revisits the Constrained Local Models (CLM) formulation where an ensemble of local feature detectors are constrained to lie within the subspace spanned by a Point Distribution Model (PDM). Fitting such a model to an image typically involves two main steps: a local search using a detector, obtaining r... View full abstract»

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  • Hierarchical Image Saliency Detection on Extended CSSD

    Publication Year: 2016, Page(s):717 - 729
    Cited by:  Papers (12)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2101 KB) | HTML iconHTML

    Complex structures commonly exist in natural images. When an image contains small-scale high-contrast patterns either in the background or foreground, saliency detection could be adversely affected, resulting erroneous and non-uniform saliency assignment. The issue forms a fundamental challenge for prior methods. We tackle it from a scale point of view and propose a multi-layer approach to analyze... View full abstract»

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  • High Accuracy Monocular SFM and Scale Correction for Autonomous Driving

    Publication Year: 2016, Page(s):730 - 743
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2016 KB) | HTML iconHTML Multimedia Media

    We present a real-time monocular visual odometry system that achieves high accuracy in real-world autonomous driving applications. First, we demonstrate robust monocular SFM that exploits multithreading to handle driving scenes with large motions and rapidly changing imagery. To correct for scale drift, we use known height of the camera from the ground plane. Our second contribution is a novel dat... View full abstract»

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  • Partial Sum Minimization of Singular Values in Robust PCA: Algorithm and Applications

    Publication Year: 2016, Page(s):744 - 758
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2005 KB) | HTML iconHTML Multimedia Media

    Robust Principal Component Analysis (RPCA) via rank minimization is a powerful tool for recovering underlying low-rank structure of clean data corrupted with sparse noise/outliers. In many low-level vision problems, not only it is known that the underlying structure of clean data is low-rank, but the exact rank of clean data is also known. Yet, when applying conventional rank minimization for thos... View full abstract»

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  • Recognition Using Hybrid Classifiers

    Publication Year: 2016, Page(s):759 - 771
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (848 KB) | HTML iconHTML

    A canonical problem in computer vision is category recognition (e.g., find all instances of human faces, cars etc., in an image). Typically, the input for training a binary classifier is a relatively small sample of positive examples, and a huge sample of negative examples, which can be very diverse, consisting of images from a large number of categories. The difficulty of the problem sharply incr... View full abstract»

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  • Reconstruction-Free Action Inference from Compressive Imagers

    Publication Year: 2016, Page(s):772 - 784
    Cited by:  Papers (4)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1439 KB) | HTML iconHTML Multimedia Media

    Persistent surveillance from camera networks, such as at parking lots, UAVs, etc., often results in large amounts of video data, resulting in significant challenges for inference in terms of storage, communication and computation. Compressive cameras have emerged as a potential solution to deal with the data deluge issues in such applications. However, inference tasks such as action recognition re... View full abstract»

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  • Semantic Concept Co-Occurrence Patterns for Image Annotation and Retrieval

    Publication Year: 2016, Page(s):785 - 799
    Cited by:  Papers (2)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1118 KB) | HTML iconHTML Multimedia Media

    Describing visual image contents by semantic concepts is an effective and straightforward way to facilitate various high level applications. Inferring semantic concepts from low-level pictorial feature analysis is challenging due to the semantic gap problem, while manually labeling concepts is unwise because of a large number of images in both online and offline collections. In this paper, we pres... View full abstract»

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  • Sum Product Networks for Activity Recognition

    Publication Year: 2016, Page(s):800 - 813
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1677 KB) | HTML iconHTML Multimedia Media

    This paper addresses detection and localization of human activities in videos. We focus on activities that may have variable spatiotemporal arrangements of parts, and numbers of actors. Such activities are represented by a sum-product network (SPN). A product node in SPN represents a particular arrangement of parts, and a sum node represents alternative arrangements. The sums and products are hier... View full abstract»

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  • What Makes for Effective Detection Proposals?

    Publication Year: 2016, Page(s):814 - 830
    Cited by:  Papers (41)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (2518 KB) | HTML iconHTML

    Current top performing object detectors employ detection proposals to guide the search for objects, thereby avoiding exhaustive sliding window search across images. Despite the popularity and widespread use of detection proposals, it is unclear which trade-offs are made when using them during object detection. We provide an in-depth analysis of twelve proposal methods along with four baselines reg... View full abstract»

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  • Rock Stars of Risk-Based Security

    Publication Year: 2016, Page(s): 831
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  • Introducing IEEE Collabratec

    Publication Year: 2016, Page(s): 832
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  • IEEE Transactions on Pattern Analysis and Machine Intelligence Information for Authors

    Publication Year: 2016, Page(s): C3
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  • IEEE Computer Society

    Publication Year: 2016, 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