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Shape centered interest points for feature grouping

Shape centered interest points for feature grouping

引用

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

作者： David Engel Cristóbal Curio Max-Planck Institute of Biological Cybernetics Tubingen Germany

Image encoding using interest points is a common technique in computer vision. In this paper we present a scale and rotation invariant shape centered interest point (SCIP) detector. By means of detecting singularities in Gradient Vector Flow (GVF) fields we find points of high symmetry in the image. Due to the nature of the underlying GVF field we can employ our features to group together edge-based interest points such as SIFTs. This feature grouping provides a strong descriptor for SCIPs and can help to encode valuable information about the image for computer vision tasks. We demonstrate the main properties of our features such as scale and rotation invariance and further robustness against noise and clutter in a series of experiments. We show that the information they encode is to a certain degree complementary to SIFT. Furthermore, we evaluate them in an edge map reconstruction task to assess the amount of image information they encode. Finally, we show the power of feature grouping with our framework in a multi-category classification task on natural images from the StreetScenes database.

关键词： Shape computer vision Image coding Noise robustness Image reconstruction Noise shaping Biological information theory Cybernetics Detectors Image edge detection

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Face recognition under varying pose

Face recognition under varying pose

引用

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

作者： Beymer MIT Artificial Intelligence Laboratory Cambridge MA USA

Researchers in computer vision and pattern recognition have worked on automatic techniques for recognizing human faces for the last 20 years. While some systems, especially template-based ones, have been quite successful on expressionless, frontal views of faces with controlled lighting, not much work has taken face recognizers beyond these narrow imaging conditions. Our goal is to build a face recognizer that works under varying pose, the difficult part of which is to handle face relations in depth. Building on successful template-based systems, our basic approach is to represent faces with templates from multiple model views that cover different poses from the viewing sphere. To recognize a novel view, the recognizer locates the eyes and nose features, uses these locations to geometrically register the input with model views, and then uses correlation on model templates to find the best match in the data base of people. Our system has achieved a recognition rate of 98% on a data base of 62 people containing 10 testing and 15 modeling views per person.< >

关键词： Machine vision

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Body plans

Body plans

引用

conference on computer vision and pattern recognition (CVPR)

作者： D.A. Forsyth M.M. Fleck Computer Science Division University of California Berkeley Berkeley CA USA Department of Computer Science University of Iowa Iowa IA USA

This paper describes a representation for people and animals, called a body plan, which is adapted to segmentation and to recognition in complex environments. The representation is an organized collection of grouping hints obtained from a combination of constraints on color and texture and constraints on geometric properties such as the structure of individual parts and the relationships between parts. Body plans can be learned from image data, using established statistical learning techniques. The approach is illustrated with two examples of programs that successfully use body plans for recognition: one example involves determining whether a picture contains a scantily clad human, using a body plan built by hand; the other involves determining whether a picture contains a horse, using a body plan learned from image data. In both cases, the system demonstrates excellent performance on large, uncontrolled test sets and very large and diverse control sets.

关键词： Image segmentation Animals Content based retrieval Assembly computer science Horses Object recognition computer vision Information retrieval Image retrieval

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“Has this Person Been Encountered Before?”: Modeling an Anonymous Identification System

“Has this Person Been Encountered Before?”: Modeling an An...

引用

ieee computer society conference on computer vision and pattern recognition Workshops

作者： Brian DeCann Arun Ross Lane Department of Computer Science and Electrical Engineering West Virginia University

ISBN: (纸本)9781467316118

We consider the problem of anonymous identification where a biometric system answers the question “Has this person been encountered before?” without actually deducing the person’s identity. In such a system, identity profiles are created dynamically as and when the system encounters an input probe. Consequently, multiple probes of the same identity may be mistakenly placed in different identity profiles, while probes from different identities may be mistakenly placed in the same identity profile. In this work, we model the matching performance of an anonymous identification system and develop terminology as well as expressions for predicting decision errors. Further, we demonstrate that the sequential order in which the probes are encountered by the system has a great impact on its matching performance. Experimental analysis based on face, fingerprint and iris scores confirms the validity of the designed error prediction model, as well as demonstrates that traditional metrics for biometric recognition fail to completely characterize the error dynamics of an anonymous identification system.

关键词： Identification performance anonymous IDENTIFICATION SYSTEMS Persons Probes personal identity Biometric recognition terminology

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A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration

A robust and convergent iterative approach for determining t...

引用

conference on computer vision and pattern recognition (CVPR)

作者： P. Fornland C. Schnorr Computational Vision and Active Perception Num. Analysis and Computer Science Royal Institute of Technology Stockholm Sweden AB Kogs FB Informatik University of Hamburg Hamburg Germany

A robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. Neither camera calibration nor stereo correspondence is required. Recently L. Cohen (1996) formalized a framework guaranteeing (local) convergence of iterative two-step methods. In this paper, the framework is adopted, with a global step using tentative matches to estimate the planar projectivity, and a local step attempting to solve the stereo correspondence. A detected point in the first image is matched to an auxiliary point in the second image, on the line joining the transformed first image point, and its closest detected second image point. Convergence is assured, while achieving robustness to both mismatching and non-coplanar points.

关键词： Robustness Iterative methods Convergence Calibration Cameras Robot kinematics computer vision Layout Robot vision systems Image reconstruction

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Using closed captions to train activity recognizers that improve video retrieval

Using closed captions to train activity recognizers that imp...

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

作者： Sonal Gupta Raymond J. Mooney Department of Computer Sciences University of Texas Austin Austin TX USA

Recognizing activities in real-world videos is a difficult problem exacerbated by background clutter, changes in camera angle & zoom, rapid camera movements etc. Large corpora of labeled videos can be used to train automated activity recognition systems, but this requires expensive human labor and time. This paper explores how closed captions that naturally accompany many videos can act as weak supervision that allows automatically collecting `labeled' data for activity recognition. We show that such an approach can improve activity retrieval in soccer videos. Our system requires no manual labeling of video clips and needs minimal human supervision. We also present a novel caption classifier that uses additional linguistic information to determine whether a specific comment refers to an on-going activity. We demonstrate that combining linguistic analysis and automatically trained activity recognizers can significantly improve the precision of video retrieval.

关键词： Humans Cameras Labeling Content based retrieval Multimedia communication Broadcasting Games Multimedia systems Training data DVD

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Nonlinear Mean Shift for Clustering over Analytic Manifolds

Nonlinear Mean Shift for Clustering over Analytic Manifolds

引用

conference on computer vision and pattern recognition (CVPR)

作者： R. Subbarao P. Meer Department of Electrical and Computer Engineering Rutgers University Piscataway NJ USA

The mean shift algorithm is widely applied for nonparametric clustering in Euclidean spaces. Recently, mean shift was generalized for clustering on matrix Lie groups. We further extend the algorithm to a more general class of nonlinear spaces, the set of analytic manifolds. As examples, two specific classes of frequently occurring parameter spaces, Grassmann manifolds and Lie groups, are considered. When the algorithm proposed here is restricted to matrix Lie groups the previously proposed method is obtained. The algorithm is applied to a variety of robust motion segmentation problems and multibody factorization. The motion segmentation method is robust to outliers, does not require any prior specification of the number of independent motions and simultaneously estimates all the motions present.

关键词： computer vision Motion segmentation Clustering algorithms Robustness Manifolds Algorithm design and analysis Motion estimation Clustering methods Image segmentation Cameras

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Modeling clutter and context for target detection in infrared images

Modeling clutter and context for target detection in infrare...

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conference on computer vision and pattern recognition (CVPR)

作者： S. Rong B. Bhanu College of Engineering University of California Riverside CA USA

In order to reduce false alarms and to improve the target detection performance of an automatic target detection and recognition system operating in a cluttered environment, it is important to develop the models not only for man-made targets bat also of natural background clutters. Because of the high complexity of natural clutters, this clutter model can only be reliably built through learning from real examples. If available, contextual information that characterizes each training example can be used to further improve the learned clutter model. In this paper, we present such a clutter model aided target detection system. Emphases are placed on two topics: (1) learning the background clutter model from sensory data through a self-organizing process, (2) reinforcing the learned clutter model using contextual information.

关键词： Context modeling Object detection Infrared imaging Infrared detectors Target recognition computer vision Image recognition Educational institutions Data preprocessing Application software

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SPECTRE: Visual Speech-Informed Perceptual 3D Facial Expression Reconstruction from Videos

SPECTRE: Visual Speech-Informed Perceptual 3D Facial Express...

引用

2023 ieee/CVF conference on computer vision and pattern recognition Workshops, CVPRW 2023

作者： Filntisis, Panagiotis P. Retsinas, George Paraperas-Papantoniou, Foivos Katsamanis, Athanasios Roussos, Anastasios Maragos, Petros Athena Research Center Institute of Robotics Maroussi15125 Greece National Technical University of Athens School of Electrical & Computer Engineering Greece Greece University of Exeter College of Engineering Mathematics and Physical Sciences United Kingdom Imperial College London United Kingdom Institute for Language and Speech Processing Athena R.C. Greece

ISBN: (纸本)9798350302493

The recent state of the art on monocular 3D face reconstruction from image data has made some impressive advancements, thanks to the advent of Deep Learning. However, it has mostly focused on input coming from a single RGB image, overlooking the following important factors: a) Nowadays, the vast majority of facial image data of interest do not originate from single images but rather from videos, which contain rich dynamic information. b) Furthermore, these videos typically capture individuals in some form of verbal communication (public talks, teleconferences, audiovisual human-computer interactions, interviews, monologues/dialogues in movies, etc). When existing 3D face reconstruction methods are applied in such videos, the artifacts in the reconstruction of the shape and motion of the mouth area are often severe, since they do not match well with the speech *** overcome the aforementioned limitations, we present the first method for visual speech-informed perceptual reconstruction of 3D mouth expressions. We do this by proposing a "lipreading"loss, which guides the fitting process so that the elicited perception from the 3D reconstructed talking head resembles that of the original video footage. We demonstrate that, interestingly, the lipreading loss is better suited for 3D reconstruction of mouth movements compared to traditional landmark losses, and even direct 3D supervision. Furthermore, the devised method does not rely on any text transcriptions or corresponding audio, rendering it ideal for training in unlabeled datasets. We verify the efficiency of our method through objective evaluations on three large-scale datasets, as well as subjective evaluation with two web-based user studies. Project webpage: https://***/spectre/ © 2023 ieee.

关键词： Human computer interaction

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Gradient vector flow: a new external force for snakes

Gradient vector flow: a new external force for snakes

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conference on computer vision and pattern recognition (CVPR)

作者： Chenyang Xu J.L. Prince Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

Snakes, or active contours, are used extensively in computer vision and image processing applications, particularly to locate object boundaries. Problems associated with initialization and poor convergence to concave boundaries, however, have limited their utility. This paper develops a new external force for active contours, largely solving both problems. This external force, which we call gradient vector flow (GVF) is computed as a diffusion of the gradient vectors of a gray-level or binary edge map derived from the image. The resultant field has a large capture range and forces active contours into concave regions. Examples on simulated images and one real image are presented.

关键词： Active contours Vectors Image processing Application software Convergence Computational modeling Image edge detection Shape Image segmentation Tracking

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