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Using global consistency to recognise Euclidean objects with an uncalibrated camera

Using global consistency to recognise Euclidean objects with...

引用

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

作者： Forsyth Mundy Zisserman Rothwell Computer Science University of Iowa Iowa IA USA General Electric Center for Research and Development Schenectady NY USA Robotics Research Group Oxford University Oxford UK

A recognition strategy consisting of a mixture of indexing on invariants and search, allows objects to be recognised up to a Euclidean ambiguity with an uncalibrated camera. The approach works by using projective invariants to determine all the possible projectively equivalent models for a particular imaged object; then a system of global consistency constraints is used to determine which of these projectively equivalent, but Euclidean distinct, models corresponds to the objects viewed. These constraints follow from properties of the imaging geometry. In particular, a recognition hypothesis is equivalent to an assertion about, among other things, viewing conditions and geometric relationships between objects, and these assertions must be consistent for hypotheses to be correct. The approach is demonstrated to work on images of real scenes consisting of polygonal objects and polyhedra.< >

关键词： Machine vision Object recognition

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recognition using visual phrases

Recognition using visual phrases

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作者： Sadeghi, Mohammad Amin Farhadi, Ali Computer Science Department University of Illinois Urbana-Champaign United States United States

ISBN: (纸本)9781457703942

In this paper we introduce visual phrases, complex visual composites like "a person riding a horse". Visual phrases often display significantly reduced visual complexity compared to their component objects, because the appearance of those objects can change profoundly when they participate in relations. We introduce a dataset suitable for phrasal recognition that uses familiar PASCAL object categories, and demonstrate significant experimental gains resulting from exploiting visual phrases. We show that a visual phrase detector significantly outperforms a baseline which detects component objects and reasons about relations, even though visual phrase training sets tend to be smaller than those for objects. We argue that any multi-class detection system must decode detector outputs to produce final results;this is usually done with non-maximum suppression. We describe a novel decoding procedure that can account accurately for local context without solving difficult inference problems. We show this decoding procedure outperforms the state of the art. Finally, we show that decoding a combination of phrasal and object detectors produces real improvements in detector results. © 2011 ieee.

关键词： Decoding

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Online environment mapping

Online environment mapping

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作者： Lim, Jongwoo Frahm, Jan-Michael Pollefeys, Marc Honda Research Institute USA inc. Mountain View CA United States Department of Computer Science University of North Carolina Chapel Hill NC United States Department of Computer Science ETH Zurich Switzerland

ISBN: (纸本)9781457703942

The paper proposes a vision based online mapping of large-scale environments. Our novel approach uses a hybrid representation of a fully metric Euclidean environment map and a topological map. This novel hybrid representation facilitates our scalable online hierarchical bundle adjustment approach. The proposed method achieves scalability by solving the local registration through embedding neighboring keyframes and landmarks into a Euclidean space. The global adjustment is performed on a segmentation of the keyframes and posed as the iterative optimization of the arrangement of keyframes in each segment and the arrangement of rigidly moving segments. The iterative global adjustment is performed concurrently with the local registration of the keyframes in a local map. Thus the map is always locally metric around the current location, and likely to be globally consistent. Loop closures are handled very efficiently benefiting from the topological nature of the map and overcoming the loss of the metric map properties as previous approaches. The effectiveness of the proposed method is demonstrated in real-time on various challenging video sequences. © 2011 ieee.

关键词： Iterative methods

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Learning Visual Feature Detectors for Obstacle Avoidance using Genetic Programming

Learning Visual Feature Detectors for Obstacle Avoidance usi...

引用

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

作者： Andrew Marek William D. Smart Martin C. Martin Department of Computer Science and Engineering Washington University Saint Louis MO USA MIT Artificial Intelligence Laboratory Cambridge MA USA

In this paper, we describe the use of Genetic Programming (GP) techniques to learn a visual feature detection for a mobile robot navigation task. We provide experimental results across a number of different environments, each with different characteristics, and draw conclusions about the performance of the learned feature detector. We also explore the utility of seeding the initial population with a previously evolved individual, and discuss the performance of the resulting individuals.

关键词： Feature extraction Pixel Training conferences Robots computer vision pattern recognition

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Sharing features between objects and their attributes

Sharing features between objects and their attributes

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作者： Hwang, Sung Ju Sha, Fei Grauman, Kristen Department of Computer Science University of Texas Austin United States Computer Science Department University of Southern California United States

ISBN: (纸本)9781457703942

Visual attributes expose human-defined semantics to object recognition models, but existing work largely restricts their influence to mid-level cues during classifier training. Rather than treat attributes as intermediate features, we consider how learning visual properties in concert with object categories can regularize the models for both. Given a low-level visual feature space together with attribute-and object-labeled image data, we learn a shared lower-dimensional representation by optimizing a joint loss function that favors common sparsity patterns across both types of prediction tasks. We adopt a recent kernelized formulation of convex multi-task feature learning, in which one alternates between learning the common features and learning task-specific classifier parameters on top of those features. In this way, our approach discovers any structure among the image descriptors that is relevant to both tasks, and allows the top-down semantics to restrict the hypothesis space of the ultimate object classifiers. We validate the approach on datasets of animals and outdoor scenes, and show significant improvements over traditional multi-class object classifiers and direct attribute prediction models. © 2011 ieee.

关键词： Semantics

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Multi-agent event recognition in structured scenarios

Multi-agent event recognition in structured scenarios

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作者： Morariu, Vlad I. Davis, Larry S. Institute for Advanced Computer Studies University of Maryland College Park MD 20742 United States

ISBN: (纸本)9781457703942

We present a framework for the automatic recognition of complex multi-agent events in settings where structure is imposed by rules that agents must follow while performing activities. Given semantic spatio-temporal descriptions of what generally happens (i.e., rules, event descriptions, physical constraints), and based on video analysis, we determine the events that occurred. Knowledge about spatio-temporal structure is encoded using first-order logic using an approach based on Allen's Interval Logic, and robustness to low-level observation uncertainty is provided by Markov Logic Networks (MLN). Our main contribution is that we integrate interval-based temporal reasoning with probabilistic logical inference, relying on an efficient bottom-up grounding scheme to avoid combinatorial explosion. Applied to one-on-one basketball, our framework detects and tracks players, their hands and feet, and the ball, generates event observations from the resulting trajectories, and performs probabilistic logical inference to determine the most consistent sequence of events. We demonstrate our approach on 1hr (100,000 frames) of outdoor videos. © 2011 ieee.

关键词： Semantics

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Attractor-Shape for Dynamical Analysis of Human Movement: Applications in Stroke Rehabilitation and Action recognition

Attractor-Shape for Dynamical Analysis of Human Movement: Ap...

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

作者： Vinay Venkataraman Pavan Turaga Nicole Lehrer Michael Baran Thanassis Rikakis Steven L. Wolf School of Electrical Computer and Energy Engineering School of Arts Media and Engineering Arizona State University USA School of Arts Media and Engineering Arizona State University USA School of Design Carnegie Mellon University USA

In this paper, we propose a novel shape-theoretic framework for dynamical analysis of human movement from 3D data. The key idea we propose is the use of global descriptors of the shape of the dynamical attractor as a feature for modeling actions. We apply this approach to the novel application scenario of estimation of movement quality from a single-marker for future usage in home-based stroke rehabilitation. Using a dataset collected from 15 stroke survivors performing repetitive task therapy, we demonstrate that the proposed method outperforms traditional methods, such as kinematic analysis and use of chaotic invariants, in estimation of movement quality. In addition, we demonstrate that the proposed framework is sufficiently general for the application of action and gesture recognition as well. Our experimental results reflect improved action recognition results on two publicly available 3D human activity databases.

关键词： Shape Three-dimensional displays Wrist Time series analysis Vectors Kinematics Quality assessment

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Three-dimensional point cloud recognition via distributions of geometric distances

Three-dimensional point cloud recognition via distributions ...

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

作者： Mona Mahmoudi Guillermo Sapiro Electrical and Computer Engineering University of Minnesota Minneapolis MN USA

A geometric framework for the recognition of three-dimensional objects represented by point clouds is introduced in this paper. The proposed approach is based on comparing distributions of intrinsic measurements on the point cloud. In particular, intrinsic distances are exploited as signatures for representing the point clouds. The first signature we introduce is the histogram of pairwise diffusion distances between all points on the shape surface. These distances represent the probability of traveling from one point to another in a fixed number of random steps, the average intrinsic distances of all possible paths of a given number of steps between the two points. This signature is augmented by the histogram of the actual pairwise geodesic distances, as well as the distribution of the ratio between these two distances. These signatures are not only geometric but also invariant to bends. We further augment these signatures by the distribution of a curvature function and the distribution of a curvature weighted distance. These histograms are compared using the chi 2 or other common distance metrics for distributions. The presentation of the framework is accompanied by theoretical justification and state-of-the-art experimental results with the standard Princeton 3D shape benchmark and ISDB datasets, as well as a detailed analysis of the particular relevance of each one of the different histogram-based signatures. Finally, we briefly discuss a more local approach where the histograms are computed for a number of overlapping patches from the object rather than the whole shape, thereby opening the door to partial shape comparisons.

关键词： Histograms Shape measurement Geophysics computing Three-dimensional displays Object recognition Level measurement Distributed computing Shape control Digital multimedia broadcasting Data communication

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Reducing the feature divergence of RGB and near-infrared images using Switchable Normalization

Reducing the feature divergence of RGB and near-infrared ima...

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

作者： Siwei Yang Shaozuo Yu Bingchen Zhao Yin Wang Tongji University Shanghai China

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Visual pattern recognition over agricultural areas is an important application of aerial image processing. In this paper, we consider the multi-modality nature of agricultural aerial images and show that naively combining different modalities together without taking the feature divergence into account can lead to sub-optimal results. Thus, we apply a Switchable Normalization block to our DeepLabV3+ segmentation model to alleviate the feature divergence. Using the popular symmetric Kullback-Leibler divergence measure, we show that our model can greatly reduce the divergence between RGB and near-infrared channels. Together with a hybrid loss function, our model achieves nearly 10% improvements in mean IoU over previously published baseline.

关键词： Task analysis Computational modeling pattern recognition Switches computer vision Agriculture Semantics

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Spatially binned ROC: A comprehensive saliency metric

Spatially binned ROC: A comprehensive saliency metric

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2016 ieee conference on computer vision and pattern recognition, CVPR 2016

作者： Wloka, Calden Tstotsos, John Electrical Engineering and Computer Science Department York University Toronto Canada

ISBN: (纸本)9781467388511

A recent trend in saliency algorithm development is large-scale benchmarking and algorithm ranking with ground truth provided by datasets of human fixations. In order to accommodate the strong bias humans have toward central fixations, it is common to replace traditional ROC metrics with a shuffled ROC metric which uses randomly sampled fixations from other images in the database as the negative set. However, the shuffled ROC introduces a number of problematic elements, including a fundamental assumption that it is possible to separate visual salience and image spatial arrangement. We argue that it is more informative to directly measure the effect of spatial bias on algorithm performance rather than try to correct for it. To capture and quantify these known sources of bias, we propose a novel metric for measuring saliency algorithm performance: the spatially binned ROC (spROC). This metric provides direct insight into the spatial biases of a saliency algorithm without sacrificing the intuitive raw performance evaluation of traditional ROC measurements. By quantitatively measuring the bias in saliency algorithms, researchers will be better equipped to select and optimize the most appropriate algorithm for a given task. We use a baseline measure of inherent algorithm bias to show that Adaptive Whitening Saliency (AWS) [14], Attention by Information Maximization (AIM) [8], and Dynamic Visual Attention (DVA) [20] provide the least spatially biased results, suiting them for tasks in which there is no information about the underlying spatial bias of the stimuli, whereas algorithms such as Graph Based Visual Saliency (GBVS) [18] and Context-Aware Saliency (CAS) [15] have a significant inherent central bias.

关键词： computer vision

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