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Straight homogeneous generalized cylinders: Differential geometry and uniqueness results.

Straight homogeneous generalized cylinders: Differential geo...

引用

Proceedings - CVPR '88: computer society conference on computer vision and pattern recognition

作者： Ponce, Jean Stanford Univ Stanford CA USA

ISBN: (纸本)0818608625

The author studies the differential geometry of straight homogeneous generalized cylinders (SHGCs). He derives a necessary and sufficient condition that an SHGC must verify to parameterize a regular surface, computer the Gaussian curvature of a regular SHGC, and prove that the parabolic lines of an SHGC are either meridians or parallels. Using these results, he addresses the following problem: under which conditions can a given surface have several descriptions by SHGCs? He proves several results. In particular, he proves that two SHGCs with the same cross-section plane and axis direction are necessarily deduced from each other through inverse scalings of their cross-sections and sweeping rule curve. He extends Shafer's pivot and slant theorems. Finally, he proves that a surface with at least two parabolic lines has at most three different SHGC descriptions, and that a surface with at leat four parabolic lines has at most a unique SHGC description.

关键词： vision

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Integrated cellular and gene interaction model for cell migration in embryonic development

Integrated cellular and gene interaction model for cell migr...

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2010 ieee International conference on Bioinformatics and Biomedicine workshops, BIBMW 2010

作者： Nguyen, Hien Song, Mingzhou Joe Department of Computer Science New Mexico State University Las Cruces NM 88003 United States

ISBN: (纸本)9781424483044

The relationship between cellular behaviors and protein concentrations is central for embryonic development. An integrated cellular and gene interaction model is proposed to reveal this relationship. Protein concentrations vary spatiotemporally based on locations of cell, gene-gene interactions and the diffusion mechanism. On the other hand, cellular behaviors differentiate spatially, driven by cell-cell communication and protein concentrations in each cell. The model integrates a variation of the reaction-diffusion equation at the gene expression level and a particle-based cellular model based on the differential adhesion hypothesis for cell sorting at the cellular level. Cell sorting based on the adhesion hypothesis and patterns of inhibitory genes were simulated to illustrate the model. This model provides a more comprehensive basis to explain pattern formation during embryogenesis, than existing approaches that ignore the cellular components. ©2010 ieee.

关键词： Adhesion

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Exploiting the Complementarity of 2D and 3D Networks to Address Domain-Shift in 3D Semantic Segmentation

Exploiting the Complementarity of 2D and 3D Networks to Addr...

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

作者： Adriano Cardace Pierluigi Zama Ramirez Samuele Salti Luigi Di Stefano Department of Computer Science and Engineering (DISI) University of Bologna Italy

3D semantic segmentation is a critical task in many real-world applications, such as autonomous driving, robotics, and mixed reality. However, the task is extremely challenging due to ambiguities coming from the unstructured, sparse, and uncolored nature of the 3D point clouds. A possible solution is to combine the 3D information with others coming from sensors featuring a different modality, such as RGB cameras. Recent multi-modal 3D semantic segmentation networks exploit these modalities relying on two branches that process the 2D and 3D information independently, striving to maintain the strength of each modality. In this work, we first explain why this design choice is effective and then show how it can be improved to make the multi-modal semantic segmentation more robust to domain shift. Our surprisingly simple contribution achieves state-of-the-art performances on four popular multi-modal unsupervised domain adaptation benchmarks, as well as better results in a domain generalization scenario.

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An Embedded Solution to Visual Mapping for Consumer Drones

An Embedded Solution to Visual Mapping for Consumer Drones

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

作者： Guyue Zhou Ang Liu Kang Yang Tao Wang Zexiang Li Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong PR China Dajiang Innovations Technology Co. Ltd Shenzhen P.R. China

In this paper, we propose a real-time visual mapping scheme which can be implemented on a low-cost embedded system for consumer-level ratio control (RC) drones. In our work, a 3-dimensional occupancy grid map is obtained based on an estimated trajectory from data fusion of multiple on-board sensors, composed of two downward-facing cameras, two forward-facing cameras, a GPS receiver, a magnetic compass and an inertial measurement unit (IMU) with 3-axis accelerometers and gyroscopes. Taking the advantages of the low-cost FPGA and ARM NEON intrinsics, we run our visual odometry and mapping algorithms at 10Hz on board. Meanwhile, we also present a hierarchical multi-sensor fusion algorithm to provide a robust trajectory for mapping usage. Finally, we verify the feasibility of our approaches and serval potential applications with experimental results in complex indoor/outdoor environments.

关键词： Visualization Cameras Sensors Field programmable gate arrays Global Positioning System Hardware Unmanned aerial vehicles

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Gait Analysis with Trinocular computer vision Using Deep Learning

Gait Analysis with Trinocular Computer Vision Using Deep Lea...

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2023 ieee International conference on Image Processing Challenges and workshops, ICIPCW 2023

作者： Stavrakakis, Odysseas Mastrogeorgiou, Athanasios Smyrli, Aikaterini Papadopoulos, Evangelos School of Mechanical Engineering National Technical University of Athens Control Systems Lab Greece

ISBN: (纸本)9798350302585

The recent years' progress in deep learning (DL) technology has resulted in convolutional neural networks (CNNs) capable of producing fast and accurate results, with minimal data preprocessing. Currently, gait analysis (GA) is attracting the attention of the field of deep learning due to its seamless integration applicability. Our approach focuses on CNN-based GA application on healthcare and orthopaedics. Using CNNs and visual fiducial systems for recognition and 3D mesh re-construction of the human form and the floor respectively, we can virtually recreate the human-floor interaction, which can be particularly useful in the study of gait dynamics, through the per-frame gait phase classification. However, the current state-of-the-art (SOTA) is that most CNN mesh reconstruction software produces a mesh from a monocular input. The use of photogrammetry could alternatively be implemented, but would require multiple cameras and expensive equipment. Our approach aims to create a refined mesh obtained from trinocular footage, along with an interactive and easy-to-use interface. © 2023 ieee.

关键词： Floors

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Investigating how and when perceptual organization cues improve boundary detection in natural images

Investigating how and when perceptual organization cues impr...

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

作者： Leandro A. Loss George Bebis Mircea Nicolescu Alexei Skurikhin Computer Vision Laboratory University of Nevada Reno USA Space and Remote Sensing Sciences Los Alamos National Laboratory USA

Boundary detection in natural images represents an important but also challenging problem in computer vision. Motivated by studies in psychophysics claiming that humans use multiple cues for segmentation, several promising methods have been proposed which perform boundary detection by optimally combining local image measurements such as color, texture, and brightness. Very interesting results have been reported by applying these methods on challenging datasets such as the Berkeley segmentation benchmark. Although combining different cues for boundary detection has been shown to outperform methods using a single cue, results can be further improved by integrating perceptual organization cues with the boundary detection process. The main goal of this study is to investigate how and when perceptual organization cues improve boundary detection in natural images. In this context, we investigate the idea of integrating with segmentation the iterative multi-scale tensor voting (IMSTV), a variant of tensor voting (TV) that performs perceptual grouping by analyzing information at multiple-scales and removing background clutter in an iterative fashion, preserving salient, organized structures. The key idea is to use IMSTV to post-process the boundary posterior probability (PB) map produced by segmentation algorithms. Detailed analysis of our experimental results reveals how and when perceptual organization cues are likely to improve or degrade boundary detection. In particular, we show that using perceptual grouping as a post-processing step improves boundary detection in 84% of the grayscale test images in the Berkeley segmentation dataset.

关键词： Image segmentation Tensile stress Voting computer vision Psychology Humans Performance evaluation Brightness TV Information analysis

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Semantic Visual Understanding of Indoor Environments: From Structures to Opportunities for Action

Semantic Visual Understanding of Indoor Environments: From S...

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

作者： Grace Tsai Collin Johnson Benjamin Kuipers Dept. of Electrical Engineering and Computer Science University of Michigan

We present a two-layer representation of the locally sensed 3D indoor environment. Our representation moves one step forward from capturing the geometric structure of the environment to reason about the navigation opportunities for an agent in the environment. The first layer is the Planar Semantic Model (PSM), a geometric representation in terms of meaningful planes (ground and walls). PSM captures more semantics of the indoor environment than a pure planar model because it represents a richer set of relationships among planar segments. In the second layer, we present the Action Opportunity Star (AOS), which describes the set of qualitatively distinct opportunities for robot action available in the neighborhood of the robot. Our two-layer representation is a concise representation of indoor environments, semantically meaningful to both robot and to human. It is capable of capturing incomplete knowledge of the local environment so that unknown areas can be incrementally learned as observations become available. Experimental results on a variety of indoor environments demonstrate the expressive power of our representation.

关键词： Logic gates Robots Three-dimensional displays Semantics Indoor environments Navigation Trajectory

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Indirect Symbolic Correlation Approach to Unsegmented Text recognition

Indirect Symbolic Correlation Approach to Unsegmented Text R...

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

作者： G. Nagy S. C. Seth S. K. Mehta Y. Lin University of Nebraska Lincoln USA Indian Institute of Technology Kanpur India Rensselaer Polytechnic Institute Troy NY USA

The new non-parametric approach to unsegmented text recognition builds two bipartite graphs that result from the feature-level and lexical comparisons of the same word against a reference string which need not include the query word. The lexical graph preserves the relative order of edges in the feature graph corresponding to correctly recognized features. This observation leads to a subgraph-matching formulation of the recognition problem. An initial implementation proves the robustness of the approach for up-to 20% noise introduced in the feature-level graph.

关键词： pattern recognition Wool Bipartite graph conferences computer vision Correlation Gain

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Embedded fall detection with a neural network and bio-inspired stereo vision

Embedded fall detection with a neural network and bio-inspir...

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

作者： Martin Humenberger Stephan Schraml Christoph Sulzbachner Ahmed Nabil Belbachir Ágoston Srp Ferenc Vajda Austrian Institute of Technology GmbH Vienna Austria Budapest University슠of슠Technology슠and슠Economics Budapest Hungary

In this paper, we present a bio-inspired, purely passive, and embedded fall detection system for its application towards safety for elderly at home. Bio-inspired means the use of two optical detector chips with event-driven pixels that are sensitive to relative light intensity changes only. The two chips are used as stereo configuration which enables a 3D representation of the observed area with a stereo matching technique. In contrast to conventional digital cameras, this image sensor delivers asynchronous events instead of synchronous intensity or color images, thus, the privacy issue is systematically solved. Another advantage is that stationary installed fall detection systems have a better acceptance for independent living compared to permanently worn devices. The fall detection is done by a trained neural network. First, a meaningful feature vector is calculated from the point clouds, then the neural network classifies the actual event as fall or non-fall. All processing is done on an embedded device consisting of an FPGA for stereo matching and a DSP for neural network calculation achieving several fall evaluations per second. The results evaluation showed that our fall detection system achieves a fall detection rate of more than 96% with false positives below 5% for our prerecorded dataset consisting of 679 fall scenarios.

关键词： Training Neural networks Optical sensors Vectors Monitoring Feature extraction

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A new robust operator for computer vision: theoretical analysis

A new robust operator for computer vision: theoretical analy...

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

作者： Stewart Department of Computer Science Rensselaer Polytechnic Institute Troy NY USA

MINPRAN, a new robust operator, finds good fits in data sets where more than 50% of the points are outliers. Unlike other techniques that handle large outlier percentages, MINPRAN does not rely on a known error bound for the good data. Instead it assumes that the bad data are randomly (uniformly) distributed within the dynamic range of the sensor. Based on this, MINPRAN uses random sampling to search for the fit and the number of inliers to the fit that are least likely to have occurred randomly. It runs in time O(N/sup 2/+SNlogN), where S is the number of random samples and N is the number of data points. We demonstrate analytically and experimentally that MINPRAN distinguishes good fits from fits to random data, and that MINPRAN finds accurate fits and nearly the correct number of inliers, regardless of the percentage of true inliers.< >

关键词： Machine vision Statistics

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