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2011 International Conference on computers, Communications, Control and Automation

作者： Quan Longzhe Jiang enchen College of Engineering Northeast Agricultural University

ISBN: (纸本)9781612841021

Maize identification plays a crucial role in seed quality testing and breeding. There are totally 8 varieties maize,which were chosen as samples from the northeast of China in recent years, and Images of 500 maize kernels of every kind of maize were obtained by machine vision. Advanced image processing and pattern recognition technology are used to classify maize. Firstly, labeling algorithm and multi-scale wavelet analysis were used to acquire single maize kernel image;Secondly, computer programs were coded to automatically extract 22 individual morphologic appearance characteristics belonging to 4 categories, which is size, shape, texture and color;At last, kernel principle component analysis(KPCA) and least square support vector machines(LS-SVM) with a binary tree were applied to classify corn maize cultivars. This method gives a recognition rate 87.2%.

关键词： corn kernel image process KPCA LS-SVM

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Reaching near neighbors with far and random proxies

Reaching near neighbors with far and random proxies

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2011 8th International Conference on Electrical Engineering, Computing Science and Automatic Control, CCE 2011

作者： Chávez, Edgar Ludueña, Veronica Reyes, Nora Roggero, Patricia Universidad Michoacana Morelia Mexico Departamento de Informática Universidad Nacional de San Luis Argentina

ISBN: (纸本)9781457710117

Proximity searching is an algorithmic abstraction covering a large number of applications in areas such as machine learning, statistics, multimedia information retrieval, computer vision and pattern recognition, to name a few. The algorithmic problem consist in preprocessing a set of objects to quickly find the objects near a given query. One of the nicest algorithmic constructions in the proximity searching literature is the Spatial Approximation Tree (SAT), built with the primary design goal of approximating to the query spatially instead of using a divide and conquer approach. A key aspect in building the SAT is the order of insertion of nodes in the tree. In the plain version the nodes are inserted in increasing order of distance to the root, and this order is recursively used in the construction. In this paper we introduce the SAT + which generalizes the SAT by using an arbitrary insertion order. We tested two alternative insertion strategies improving the efficiency of the SAT at searching time. © 2011 IEEE.

关键词： pattern recognition

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Multi-view reconstruction preserving weakly-supported surfaces

Multi-view reconstruction preserving weakly-supported surfac...

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作者： Jancosek, Michal Pajdla, Tomas Department of Cybernetics Faculty of Elec. Eng. Czech Technical University Prague Czech Republic

ISBN: (纸本)9781457703942

We propose a novel method for the multi-view reconstruction problem. Surfaces which do not have direct support in the input 3D point cloud and hence need not be photo-consistent but represent real parts of the scene (e.g. low-textured walls, windows, cars) are important for achieving complete reconstructions. We augmented the existing Labatut CGF 2009 method with the ability to cope with these difficult surfaces just by changing the t-edge weights in the construction of surfaces by a minimal s-t cut. Our method uses Visual-Hull to reconstruct the difficult surfaces which are not sampled densely enough by the input 3D point cloud. We demonstrate importance of these surfaces on several real-world data sets. We compare our improvement to our implementation of the Labatut CGF 2009 method and show that our method can considerably better reconstruct difficult surfaces while preserving thin structures and details in the same quality and computational time1. © 2011 IEEE.

关键词： computer vision

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Multiview registration via graph diffusion of dual quaternions

Multiview registration via graph diffusion of dual quaternio...

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作者： Torsello, Andrea Rodolà, Emanuele Albarelli, Andrea Dipartimento di Scienze Ambientali Informatica e Statistica Università Ca' Foscari Venezia Italy

ISBN: (纸本)9781457703942

Surface registration is a fundamental step in the reconstruction of three-dimensional objects. While there are several fast and reliable methods to align two surfaces, the tools available to align multiple surfaces are relatively limited. In this paper we propose a novel multiview registration algorithm that projects several pairwise alignments onto a common reference frame. The projection is performed by representing the motions as dual quaternions, an algebraic structure that is related to the group of 3D rigid transformations, and by performing a diffusion along the graph of adjacent (i.e., pairwise alignable) views. The approach allows for a completely generic topology with which the pair-wise motions are diffused. An extensive set of experiments shows that the proposed approach is both orders of magnitude faster than the state of the art, and more robust to extreme positional noise and outliers. The dramatic speedup of the approach allows it to be alternated with pairwise alignment resulting in a smoother energy profile, reducing the risk of getting stuck at local minima. © 2011 IEEE.

关键词： computer vision

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What makes a chair a chair?

What makes a chair a chair?

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作者： Grabner, Helmut Gall, Juergen Van Gool, Luc Computer Vision Laboratory ETH Zurich Switzerland ESAT PSI/IBBT K.U. Leuven Belgium

ISBN: (纸本)9781457703942

Many object classes are primarily defined by their functions. However, this fact has been left largely unexploited by visual object categorization or detection systems. We propose a method to learn an affordance detector. It identifies locations in the 3d space which "support" the particular function. Our novel approach "imagines" an actor performing an action typical for the target object class, instead of relying purely on the visual object appearance. So, function is handled as a cue complementary to appearance, rather than being a consideration after appearance-based detection. Experimental results are given for the functional category "sitting". Such affordance is tested on a 3d representation of the scene, as can be realistically obtained through SfM or depth cameras. In contrast to appearance-based object detectors, affordance detection requires only very few training examples and generalizes very well to other sittable objects like benches or sofas when trained on a few chairs. © 2011 IEEE.

关键词： Object detection

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Learning effective human pose estimation from inaccurate annotation

Learning effective human pose estimation from inaccurate ann...

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作者： Johnson, Sam Everingham, Mark School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781457703942

The task of 2-D articulated human pose estimation in natural images is extremely challenging due to the high level of variation in human appearance. These variations arise from different clothing, anatomy, imaging conditions and the large number of poses it is possible for a human body to take. Recent work has shown state-of-the-art results by partitioning the pose space and using strong nonlinear classifiers such that the pose dependence and multi-modal nature of body part appearance can be captured. We propose to extend these methods to handle much larger quantities of training data, an order of magnitude larger than current datasets, and show how to utilize Amazon Mechanical Turk and a latent annotation update scheme to achieve high quality annotations at low cost. We demonstrate a significant increase in pose estimation accuracy, while simultaneously reducing computational expense by a factor of 10, and contribute a dataset of 10,000 highly articulated poses. © 2011 IEEE.

关键词： computer vision

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Structural Decomposition Trees

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computer GRAPHICS FORUM 2011年第3期30卷 921-930页

作者： Engel, D. Rosenbaum, R. Hamann, B. Hagen, H. Univ Kaiserslautern D-67663 Kaiserslautern Germany Univ Calif Davis Inst Data Anal & Visualizat Davis CA 95616 USA

Researchers and analysts in modern industrial and academic environments are faced with a daunting amount of multi-dimensional data. While there has been significant development in the areas of data mining and knowledge discovery, there is still the need for improved visualizations and generic solutions. The state-of-the-art in visual analytics and exploratory data visualization is to incorporate more profound analysis methods while focusing on fast interactive abilities. The common trend in these scenarios is to either visualize an abstraction of the data set or to better utilize screen-space. This paper presents a novel technique that combines clustering, dimension reduction and multi-dimensional data representation to form a multivariate data visualization that incorporates both detail and overview. This amalgamation counters the individual drawbacks of common projection and multi-dimensional data visualization techniques, namely ambiguity and clutter. A specific clustering criterion is used to decompose a multi-dimensional data set into a hierarchical tree structure. This decomposition is embedded in a novel Dimensional Anchor visualization through the use of a weighted linear dimension reduction technique. The resulting Structural Decomposition Tree (SDT) provides not only an insight of the data set's inherent structure, but also conveys detailed coordinate value information. Further, fast and intuitive interaction techniques are explored in order to guide the user in highlighting, brushing, and filtering of the data.

关键词： I.4.4 [IMAGE PROCESSING AND computer vision]: Image Representation Multidimensional I.5.4 [pattern recognition]: Clustering Similarity measures

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Advanced textural representaton of materals appearance

Advanced textural representaton of materals appearance

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SIGGRAPH Asia 2011 Courses, SA'11

作者： Filip, Jiří Haindl, Michal Pattern Recognition Department Institute of Information Theory and Automation of the AS CR Pod vodarenskou vezi 4 182 08 Praha 8 Czech Republic

ISBN: (纸本)9781450311359

Multidimensional visual texture is the appropriate paradigm for physically correct material visual properties representation. The course will present recent advances in texture modelling methodology as applied in computer vision, pattern recognition, computer graphics, and virtual/augmented reality applications. Contrary to previous courses on material appearance, we will focus on materials whose nature allows the exploitation of texture modeling approaches. This topic is introduced in the wider and complete context of pattern recognition and image processing. It comprehends modeling of multi-spectral images and videos which can be accomplished either with multi-dimensional mathematical models or sophisticated sampling methods from the original measurements. The key aspects of the topic, i.e., different multi-dimensional data models with their corresponding benefits and drawbacks, optimal model selection, parameter estimation and model synthesis techniques, are discussed. These methods produce compact parametric sets that not only faithfully reproduce material appearance, but are also vital for visual scene analysis, e.g. texture segmentation, classification, and retrieval. Special attention is devoted to recent advanced trends towards Bidirectional Texture Function (BTF) modeling, used for materials that do not obey Lambertian law, and whose reflectance has non-trivial illumination and viewing direction dependency. BTFs represent the best known effectively applicable textural representation of most real-world materials' visual properties. The techniques covered include efficient Markov random field-based algorithms, intelligent sampling algorithms, spatially-varying reflectance models and challenges with their possible implementation on GPU. The course also deals with proper data measurement, visualization of texture models in virtual scenes, visual quality evaluation feedback, as well as description of key industrial and research applications. We will discuss opti

关键词： Quality control

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Sparse acquisition and reconstruction for some computer vision problems

Sparse acquisition and reconstruction for some computer visi...

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作者： Reddy, Nagilla Dikpal University of Maryland College Park

学位级别：Ph.D.

Sparse representation, acquisition and reconstruction of signals guided by theory of Compressive Sensing (CS) has become an active research research topic over the last few years. Sparse representations effectively capture the idea of parsimony enabling novel acquisition schemes including sub-Nyquist sampling. Ideas from CS have had significant impact on well established fields such as signal acquisition, machine learning and statistics and have also inspired new areas of research such as low rank matrix completion. In this dissertation we apply CS ideas to low-level computer vision problems. The contribution of this dissertation is to show that CS theory is an important addition to the existing computational toolbox in computer vision and pattern recognition, particularly in data representation and processing. Additionally, in each of the problems we show how sparse representation helps in improved modeling of the underlying data leading to novel applications and better understanding of existing problems. In our work, the impact of CS is most felt in the acquisition of videos with novel camera designs. We build prototype cameras with slow sensors capable of capturing at an order of magnitude higher temporal resolution. First, we propose sub-Nyquist acquisition of periodic events and then generalize the idea to capturing regular events. Both the cameras operate by first acquiring the video at a slower rate and then computationally recovering the desired higher temporal resolution frames. In our camera, we sense the light with a slow sensor after modulating it with a fluttering shutter and then reconstruct the high speed video by enforcing its sparsity. Our cameras offer a significant advantage in light efficiency and cost by obviating the need to sense, transfer and store data at a higher frame rate. Next, we explore the applicability of compressive cameras for computer vision applications in bandwidth constrained scenarios. We design a compressive camera capable of

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16th Iberoamerican Congress on pattern recognition, CIARP 2011

作者： De Diego, Isaac Martín Siordia, Oscar S. Conde, Cristina Cabello, Enrique Face Recognition and Artificial Vision Group Universidad Rey Juan Carlos C. Tulipán S/N 28934 Móstoles Spain

ISBN: (纸本)9783642250842

The aim of this paper is to present a novelty methodology to develop similarity measures for classification of time series. First, a linear segmentation algorithm to obtain a section-wise representation of the series is presented. Then, two similarity measures are defined from the differences between the behavior of the series and the level of the series, respectively. The method is applied to subjective-data on time series generated through the evaluations of the driving risk from a group of traffic safety experts. These series are classified using the proposed similarities as kernels for the training of a Support Vector Machine. The results are compared with other classifiers using our similarities, their linear combination and the raw data. The proposed methodology has been successfully evaluated on several databases. © 2011 Springer-Verlag.

关键词： Time series

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