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A Geometry-Based Local Descriptor for Range Data

A Geometry-Based Local Descriptor for Range Data

引用

Proceedings of the Digital Image Computing: Technqiues and Applications (DICTA)

作者： Fredrik Viksten Klas Nordberg Image Coding Group Department of Electrical Engineering Linköping university Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping university Sweden

ISBN: (纸本)9781424431618;9780769530673

We present a novel local descriptor for range data that can describe one or more planes or lines in a local region. It is possible to recover the geometry of the described local region and extract the size, position and orientation of each local plane or line-like structure from the descriptor. This gives the descriptor a property that other popular local descriptors for range data, such as spin images or point signatures, does not have. The estimation of the descriptor is dependant on estimation of surface normals but does not depend on the specific normal estimation method used. It is shown that is possible to extract how many planar surface regions the descriptor represents and that this could be used as a point-of-interest detector.

关键词： Tensile stress Layout computer vision Geometry Data mining Digital images computer applications Application software Image coding Laboratories

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Energy-Based Models in Document recognition and computer vision

Energy-Based Models in Document Recognition and Computer Vis...

引用

International Conference on Document Analysis and recognition

作者： Y. LeCun S. Chopra M. Ranzato F.-J. Huang The Courant Institute of Mathematical Sciences New York University USA

The machine learning and pattern recognition communities are facing two challenges: solving the normalization problem, and solving the deep learning problem. The normalization problem is related to the difficulty of training probabilistic models over large spaces while keeping them properly normalized. In recent years, the ML and natural language communities have devoted considerable efforts to circumventing this problem by developing "un-normalized" learning models for tasks in which the output is highly structured (e.g. English sentences). This class of models was in fact originally developed during the 90's in the handwriting recognition community, and includes graph transformer networks, conditional random fields, hidden Markov SVMs, and maximum margin Markov networks. We describe these models within the unifying framework of "energy-based models" (EBM). The deep learning problem is related to the issue of training all the levels of a recognition system (e.g. segmentation, feature extraction, recognition, etc) in an integrated fashion. We first consider " traditional" methods for deep learning, such as convolutional networks and back-propagation, and show that, although they produce very low error rates for handwriting and object recognition, they require many training samples. We show that using unsupervised learning to initialize the layers of a deep network dramatically reduces the required number of training samples, particularly for such tasks as the recognition of everyday objects at the category level.

关键词： computer vision Hidden Markov models Machine learning pattern recognition Natural languages Handwriting recognition Markov random fields Feature extraction Error analysis Object recognition

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A simple tracking approach for line extraction

A simple tracking approach for line extraction

引用

International Conference on Wavelet Analysis and pattern recognition (ICWAPR)

作者： Liang Chen Lei Guo Ya-Ping Wang Shi-Wei Gao Yong Yu College of Automation Northwest Polytechnical University Xi'an Shaanxi China

Line extraction plays an important role in many applications as mid-level feature description; the problem is how to get accurate location of line effectively. In this paper, the extraction is simplified by edge tracking with adding some constraints which ensure the edge be linked near to the line. It is a strategy that made a feasible way to obtain lines. Following edge detecting, the proposed approach obtains lines by tracking edge with priority direction, inertia tracking, judging and rectifying linear relation real-time. After such constraints on the tracking process, the edge is linked to a line. Experiments show varied results of proposed approach and the contrast to Hough transform, and it can be seen that the line extracted is more effective than latter.

关键词： Image edge detection Joining processes computer vision pattern analysis pattern recognition Eigenvalues and eigenfunctions Pixel Wavelet analysis Educational institutions

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Object Localisation Using Generative Probability Model for Spatial Constellation and Local Image Features

Object Localisation Using Generative Probability Model for S...

引用

International Conference on computer vision (ICCV)

作者： J.-K. Kamarainen M. Hamouz J. Kittler P. Paalanen J. Ilonen A. Drobchenko Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK Machine Vision and Pattern Recognition Research Group Lappeenranta University of Technology Finland

In this paper we apply state-of-the-art approach to object detection and localisation by incorporating local descriptors and their spatial configuration into a generative probability model. In contrast to the recent semi- supervised methods we do not utilise interest point detectors, but apply a supervised approach where local image features (landmarks) are annotated in a training set and therefore their appearance and spatial variation can be learnt. Our method enables working in purely probabilistic search spaces providing a MAP estimate of object location, and in contrast to the recent methods, no background class needs to be formed. Using the training set we can estimate pdfs for both spatial constellation and local feature appearance. By applying an inference bias that the largest pdf mode has probability one, we are able to combine prior information (spatial configuration of the features) and observations (image feature appearance) into posterior distribution which can be generatively sampled, e.g. using MCMC techniques. The MCMC methods are sensitive to initialisation, but as a solution, we also propose a very efficient and accurate RANSAC-based method for finding good initial hypotheses of object poses. The complete method can robustly and accurately detect and localise objects under any homography.

关键词： Object detection Robustness Solid modeling Speech processing Signal processing Machine vision pattern recognition Signal generators Detectors Phase detection

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Facial Template Synthesis based on SIFT Features

Facial Template Synthesis based on SIFT Features

引用

IEEE Workshop on Automatic Identification Advanced Technologies

作者： Ajita Rattani D. R. Kisku Andrea Lagorio Massimo Tistarelli Department of Computer Science and Engineering Indian Institute of Technology Kanpur Kanpur India Computer Vision Laboratory University of Sassari Alghero Italy

This paper proposes a procedure for facial template synthesis based on features extracted from multiple facial instances with varying pose. The proposed system extracts the rotation, scale and translation invariant SIFT features, also having high discrimination ability, from the frontal and half left and right profiles of an individual face images. These affine invariant features obviate the need of ad-hoc algorithms to register features of side profiles against frontal profiles for feature-set augmentation. An augmented feature set is then formed from the fusion of features from frontal and side profiles of an individual, after removing feature redundancy. The augmented feature sets of database and query images are matched using the Euclidean distance and Point pattern matching techniques. The experimental results are compared with the system using only frontal face images for both the matching strategies. The reported results prove the efficacy of the proposed system.

关键词： Face recognition Feature extraction Iterative algorithms pattern matching Biometrics Fingerprint recognition computer science computer vision Laboratories Redundancy

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The Relationship Between Kernel and Classifier Fusion in Kernel-Based Multi-Modal pattern recognition: An Experimental Study

The Relationship Between Kernel and Classifier Fusion in Ker...

引用

International Conference on Machine Learning and Cybernetics (ICMLC)

作者： David Windridge Vadim Mottl Alexander Tatarchuk Andrey Eliseyev Centre for Vision Speech and Signal Processing University of Surrey Guildford UK Computing Center Russian Academy of Science Moscow Russia Moscow Institute of Physics and Technology Moscow Russia

Two distinct principles of multi-modal kernel-based pattern recognition, kernel and classifier fusion, are demonstrated to share common underlying characteristics via the use of a novel kernel-based technique for combining modalities under fully general conditions, namely, the neutral-point method. This method presents a conservative kernel-based strategy for dealing with missing and disjoint training data in independent measurement modalities that can be theoretically shown to default to the sum rule classification scheme. Results of comparative experiments indicate that the neutral-point technique loses relatively little classification information with respect to coincident training data, and is in fact preferable for independent kernels produced by different physical modalities due to its better error-cancellation properties.

关键词： Kernel pattern recognition Training data Sensor fusion Machine learning Cybernetics Speech processing Biomedical signal processing computer vision Physics computing

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A Discriminative Framework for Object recognition

A Discriminative Framework for Object Recognition

引用

International Conference on Mechatronics (ICM)

作者： Hongwei Li Jian Cheng Hanqing Lu National Laboratory Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper, we present a discriminative approach to learn and recognize the object classes from unsegmented cluttered scenes in a scale invariant manner. A multiscale algorithm for the selection of salient regions of an image is used to select keypoints and their scale in the image. The PCA-SIFT method is used to describe these keypoints in a compact form. For each object class the probability of local features is modeled by a conditional random fields (CRF). In the learning stage, the parameters of CRF are estimated from feature vectors given the labels in a maximum likelihood framework. In the recognition stage, we take the label for the image to the most likely class under the CRF models. This method achieves good classification results on motorbikes and airplanes database.

关键词： Object recognition Detectors Entropy computer vision pattern recognition Layout Maximum likelihood estimation Mechatronics Automation Image recognition

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A System for Off-Line Oriya Handwritten Character recognition Using Curvature Feature

A System for Off-Line Oriya Handwritten Character Recognitio...

引用

International Conference on Information Technology (ICIT)

作者： U. Pal T. Wakabayashi F. Kimura Computer Vision and Pattern Recognition Unit Indian Statistical Institute Kolkata India Graduate School of Engineering Mie University Tsu Mie Japan

In this paper we present a system towards the recognition of off-line Oriya handwritten characters. Since most of the Oriya characters have curve-like stroke, we use curvature feature for the recognition purpose. To get the feature, at first, the input image is size normalized and segmented into 49times49 blocks. Curvature is then computed using bi-quadratic interpolation method and quantized into 3 levels according to concave, linear and convex regions. Next direction of gradient is quantized into 32 levels with pi/16 intervals, and strength of the gradient is accumulated in each of the 32 directions and in each of the 3 curvature levels of every block. A spatial resolution is made to get 7times7 blocks from 49times49 blocks and a directional resolution is made to get 8 directions from 32 directions. Using curvature features for 3 levels we get 1176 (7times7 blocks times 8 directions times 3 levels) dimensional features. Finally using principal component analysis we reduce the dimension 1176 to 392 and this 392 dimensional feature vector is fed to a quadratic classifier for recognition. We tested 18190 samples of Oriya handwritten samples and obtained 94.60% accuracy from our proposed system.

关键词： Character recognition Shape Handwriting recognition computer vision Spatial resolution Speech recognition Information technology pattern recognition Image segmentation Interpolation

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Object Extraction by Spatio-Temporal Assembling

Object Extraction by Spatio-Temporal Assembling

引用

IEEE International Conference on Image Processing

作者： Xiaoke Qin Liang Tang Jie Zhou China Hewlett Packard Laboratories China

Among various algorithms for vision-based traffic monitoring, spatio-temporal (ST) slice analysis is attractive by computing over a larger temporal scale. However, it is unsuitable for further pattern recognition, since the conventional ST slice cannot preserve the spatial relationship of the original object image. In this paper, we propose a novel algorithm for accurate traffic object extraction. Compared with previous ST algorithms depending on one line per frame, we assemble the object based on foreground strips obtained from each frame and carefully designed motion estimation. Thus, both the spatial and temporal information is used more effectively. Applications in real canal traffic scenes show the advantages of our algorithm.

关键词： Assembly Algorithm design and analysis Monitoring computer vision pattern recognition Data mining Strips Motion estimation Irrigation Layout

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Rotation Invariant Shape Contexts based on Feature-space Fourier Transformation

Rotation Invariant Shape Contexts based on Feature-space Fou...

引用

International Conference on Image and Graphics (ICIG)

作者： Su Yang Yuanyuan Wang Department of Computer Science and Engineering Fudan University Shanghai China Department of Electronic Engineering Fudan University Shanghai China

We propose a new pixel-level shape descriptor. First, shape contexts are computed. Then, 2D FFT is performed on each 2D histogram from shape contexts. Such a scheme solves the rotation-invariance problem of shape contexts based on the shift theorem of Fourier transformation while does not increase the computational complexity. Theoretical proof and experimental validation are provided.

关键词： Shape Computational complexity Robustness Histograms pattern matching computer vision pattern recognition computer graphics computer science Pixel

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