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Overcomplete steerable pyramid filters and rotation invariance

Overcomplete steerable pyramid filters and rotation invarian...

引用

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

作者： Greenspan Belongie Goodman Perona Rakshit Anderson Department of Electrical Engineering California Institute of Technology Pasadena CA USA Department of Anatomy and Neurobiology Washington University School of Medicine Saint Louis MO USA

A given (overcomplete) discrete oriented pyramid may be converted into a steerable pyramid by interpolation. We present a technique for deriving the optimal interpolation functions (otherwise called 'steering coefficients'). The proposed scheme is demonstrated on a computationally efficient oriented pyramid, which is a variation on the Burt and Adelson (1983) pyramid. We apply the generated steerable pyramid to orientation-invariant texture analysis in order to demonstrate its excellent rotational isotropy. High classification rates and precise rotation identification are demonstrated.< >

关键词： Filtering Machine vision Image texture analysis Image orientation analysis

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Surface classification using conformal structures

Surface classification using conformal structures

引用

Proceedings: Ninth ieee International conference on computer vision

作者： Gu, Xianfeng Yau, Shing-Tung Department of CISE University of Florida Gainesville FL 32611 United States Department of Mathematics Harvard University Cambridge MA 02138 United States

ISBN: (纸本)0769519504

3D surface classification is a fundamental problem in computer vision and computational geometry. Surfaces can be classified by different transformation groups. Traditional classification methods mainly use topological transformation groups and Euclidean transformation groups. This paper introduces a novel method to classify surfaces by conformal transformation groups. Conformal equivalent class is refiner than topological equivalent class and coarser than isometric equivalent class, making it suitable for practical classification purposes. For general surfaces, the gradient fields of conformal maps form a vector space, which has a natural structure invariant under conformal transformations. We present an algorithm to compute this conformal structure, which can be represented as matrices, and use it to classify surfaces. The result is intrinsic to the geometry, invariant to triangulation and insensitive to resolution. To the best of our knowledge, this is the first paper to classify surfaces with arbitrary topologies by global conformal invariants. The method introduced here can also be used for surface matching problems.

关键词： pattern matching

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Illumination planning for object recognition in structured environments

Illumination planning for object recognition in structured e...

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

作者： Murase Nayar NTT Basic Research Laboratories Atsugi Kanagawa Japan Department of Computer Science Columbia University New York NY USA

This paper addresses the problem of illumination planning for robust object recognition in structured environments. Given a set of objects, the goal is to determine the illumination for which the objects are most distinguishable in appearance from each other. For each object, a large number of images is automatically obtained by varying pose and illumination. Images of all objects, together, constitute the planning image set. The planning set is compressed using the Karhunen-Loeve transform to obtain a low-dimensional subspace. For any given illumination, objects are represented as parametrized manifolds in the subspace. The minimum distance between the manifolds of too objects represents the similarity between the objects in the correlation sense. The optimal illumination is therefore one that maximizes the shortest distance between object manifolds. Results produced by the illumination planner heave been used to enhance the performance of an object recognition system.< >

关键词： Machine vision Object recognition Lighting

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Depth from focus with one image

Depth from focus with one image

引用

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

作者： Jahne Geissler Scripps Institution of Oceanography University of California San Diego CA USA Institute for Environmental Physics University of Heidelberg Heidelberg Germany

A novel depth-from-focus technique is introduced that needs only a single image. It is based on a precise knowledge of the 3-D point spread function and requires objects of uniform brightness and simple shapes. Using adequate low-level image processing techniques, the true area of the object and the distance from the focal plane is obtained from parameters such as the apparent (blurred) area of the object and the mean brightness in this area. The technique has been applied to measure the size distribution of bubbles submerged by breaking waves. A depth criterion is used to define a virtual measuring volume that is roughly proportional to the size of the bubbles.< >

关键词： Machine vision

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Integrating LDV Audio and IR Video for Remote Multimodal Surveillance

Integrating LDV Audio and IR Video for Remote Multimodal Sur...

引用

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

作者： Zhigang Zhu Weihong Li G. Wolberg Department of Computer Science City College and Graduate Center City University of New York NY USA

This paper describes a multimodal surveillance system for human signature detection. The system consists of three types of sensors: infrared (IR) cameras, pan/tilt/zoom (PTZ) color cameras and laser Doppler vibrometers (LDVs). The LDV is explored as a new non-contact remote voice detector. We have found that voice energy vibrates most objects and the vibrations can be detected by an LDV. Since signals captured by the LDV are very noisy, we have designed algorithms with Gaussian bandpass filtering and adaptive volume scaling to enhance the LDV voice signals. The enhanced voice signals are intelligible from targets without retro-reflective finishes at short or medium distances (<100m). By using retroreflective tapes, the distance could be as far as 300 meters. However, the manual operation to search and focus the laser beam on a target with both vibration and reflection is very difficult at medium and large distances. Therefore, infrared (IR) imaging for target selection and localization is also discussed. Future work remains in automatic LDV targeting and intelligent refocusing for long range LDV listening.

关键词： Surveillance Infrared sensors Cameras Humans Sensor systems Vibrometers Detectors Object detection Gaussian noise Signal design

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Towards Automated Understanding of Student-Tutor Interactions Using Visual Deictic Gestures

Towards Automated Understanding of Student-Tutor Interaction...

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

作者： Suchitra Sathayanarayana Ravi Kumar Satzoda Amber Carini Monique Lee Linda Salamanca Judy Reilly Deborah Forster Marian Bartlett Gwen Littlewort University of California San Diego La Jolla CA US University of California San Diego San Diego State University

In this paper, we present techniques for automated understanding of tutor-student behavior through detecting visual deictic gestures, in the context of one-to-one mathematics tutoring. To the best knowledge of the authors, this is the first work in the area of intelligent tutoring systems, which focuses on spatial localization of deictic gestural activity, i.e. where the deictic gesture is pointing on the workspace. A new dataset called SDMATH is first introduced. The motivation for detecting deictic gestures and their spatial properties is established, followed by techniques for automatic localization of deictic gestures in a workspace. The techniques employ computer vision and machine learning steps such as GBVS saliency, binary morphology and HOG-SVM classification. It is shown that the method localizes the deictic tip with an accuracy of over 85 % accuracy for a cut off distance of 12 pixels. Furthermore, a detailed discussion using examples from the proposed dataset is presented on high-level inferences about the student-tutor interactions that can be derived from the integration of spatial and temporal localization of the deictic gestural activity using the proposed techniques.

关键词： Speech Visualization Cameras Support vector machines Mathematics Bismuth computer vision

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Single-image crowd counting via multi-column convolutional neural network

Single-image crowd counting via multi-column convolutional n...

引用

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

作者： Zhang, Yingying Zhou, Desen Chen, Siqin Gao, Shenghua Ma, Yi Shanghaitech University China

ISBN: (纸本)9781467388511

This paper aims to develop a method than can accurately estimate the crowd count from an individual image with arbitrary crowd density and arbitrary perspective. To this end, we have proposed a simple but effective Multi-column Convolutional Neural Network (MCNN) architecture to map the image to its crowd density map. The proposed MCNN allows the input image to be of arbitrary size or resolution. By utilizing filters with receptive fields of different sizes, the features learned by each column CNN are adaptive to variations in people/head size due to perspective effect or image resolution. Furthermore, the true density map is computed accurately based on geometry-adaptive kernels which do not need knowing the perspective map of the input image. Since exiting crowd counting datasets do not adequately cover all the challenging situations considered in our work, we have collected and labelled a large new dataset that includes 1198 images with about 330,000 heads annotated. On this challenging new dataset, as well as all existing datasets, we conduct extensive experiments to verify the effectiveness of the proposed model and method. In particular, with the proposed simple MCNN model, our method outperforms all existing methods. In addition, experiments show that our model, once trained on one dataset, can be readily transferred to a new dataset.

关键词： computer vision

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Deep canonical time warping

Deep canonical time warping

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

作者： Trigeorgis, George Nicolaou, Mihalis A. Zafeiriou, Stefanos Schuller, Björn W. Imperial College London United Kingdom Goldsmiths University of London United Kingdom Center for Machine Vision and Signal Analysis University of Oulu Finland

ISBN: (纸本)9781467388511

Machine learning algorithms for the analysis of timeseries often depend on the assumption that the utilised data are temporally aligned. Any temporal discrepancies arising in the data is certain to lead to ill-generalisable models, which in turn fail to correctly capture the properties of the task at hand. The temporal alignment of time-series is thus a crucial challenge manifesting in a multitude of applications. Nevertheless, the vast majority of algorithms oriented towards the temporal alignment of time-series are applied directly on the observation space, or utilise simple linear projections. Thus, they fail to capture complex, hierarchical non-linear representations which may prove to be beneficial towards the task of temporal alignment, particularly when dealing with multi-modal data (e.g., aligning visual and acoustic information). To this end, we present the Deep Canonical Time Warping (DCTW), a method which automatically learns complex non-linear representations of multiple time-series, generated such that (i) they are highly correlated, and (ii) temporally in alignment. By means of experiments on four real datasets, we show that the representations learnt via the proposed DCTW significantly outperform state-of-the-art methods in temporal alignment, elegantly handling scenarios with highly heterogeneous features, such as the temporal alignment of acoustic and visual features.

关键词： Alignment

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Automated pose estimation in 3D point clouds applying annealing particle filters and inverse kinematics on a GPU

Automated pose estimation in 3D point clouds applying anneal...

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

作者： Nicolas H. Lehment Dejan Arsić Moritz Kaiser Gerhard Rigoll Institute for Human Machine Communication Technische Universität München München Germany

Current experiments with HCIs have shown a high demand for more natural interaction paradigms. Gestures are thereby considered the most important cue besides speech. In order to recognize gestures it is necessary to extract meaningful motion features from the body. Up to now mostly marker based tracking systems are used in virtual reality environments, since these were traditionally more reliable than purely image based detection methods. However, markers tend to be distracting and cumbersome. Following recent advances in processing power, it becomes possible to use a camera system in order to obtain a depth image of the test subject, match it to a pre-defined body model, and thus track the body parts over time. We will present a full-body system based on APF which enables full body tracking utilizing point clouds recorded with a 3D sensor. Further refinement is provided by a specially adapted inverse kinematics system. A GPU based implementation speeds up processing significantly and allows near real time performance.

关键词： Clouds Annealing Particle filters Kinematics Human computer interaction Speech Feature extraction Virtual reality Power system reliability Cameras

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Optimization of Iris Codes for Improved recognition

Optimization of Iris Codes for Improved Recognition

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

作者： Nitin Mahadeo Andrew Paplinski Sid Ray Clayton School of Information Technology Monash University

The texture of the iris is commonly represented as an iris code in iris recognition systems. While several approaches have been presented for generating iris codes, relatively few comparison techniques have been proposed. In this paper, we take advantage of the availability of several frames from an iris video to create a single optimized iris code. This is achieved by performing both row-wise and column-wise optimization of iris codes. Inconsistent bits are accurately detected and masked in the final iris code. Our experiments demonstrate that by exploiting variations within the comparison scores of different rows and columns of N frames, we are able to derive the number of consistent bits in the final iris code thereby resulting in significant improvement in recognition performance. We compare our algorithm with well-known methods, namely, Fragile bit masking, Signal fusion and, two Score Fusion techniques. Experimental results on a dataset of 986 iris videos show that the proposed method is encouraging and comparable to the best algorithms in the current literature. To our knowledge, this is the first work that makes use of the best rows and columns from different frames in an iris video to improve performance.

关键词： Iris Iris recognition High definition video Optimization Error analysis pattern recognition Encoding

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