Results 31 to 40 of about 791,315 (305)
Algorithms for Mobile Robot Localization and Mapping, Incorporating Detailed Noise Modeling and Multi-scale Feature Extraction [PDF]
, 2006 Mobile robot localization and mapping in unknown environments is a fundamental requirement for effective autonomous navigation. Three different approaches to localization and mapping are presented.
Pfister, Samuel Thomas
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DWANet: Focus on Foreground Features for More Accurate Location
IEEE Access, 2022 Object detection can locate objects in an image using bounding boxes, which can facilitate classification and image understanding, resulting in a wide range of applications.
Jiwei Hu +3 more
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Details of our multi-scale spatial focus features enhancement strategy.
, 2022 The input of the module is the output of the last convolutional layer. First, a soft attention mechanism with SPP is used to obtain the multi-scale spatial features.
Guoan Yang (602228) +6 more
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Double Attention for Multi-Label Image Classification
IEEE Access, 2020 Multi-label image classification is an essential task in image processing. How to improve the correlation between labels by learning multi-scale features from images is a very challenging problem.
Haiying Zhao +3 more
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Direct numerical simulation of complex viscoelastic flows via fast lattice-Boltzmann solution of the Fokker–Planck equation [PDF]
, 2013 Micro–macro simulations of polymeric solutions rely on the coupling between macroscopic conservation equations for the fluid flow and stochastic differential equations for kinetic viscoelastic models at the microscopic scale.
Izquierdo, S. +6 more
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Efficient Lightweight Attention Network for Face Recognition
IEEE Access, 2022 Although face recognition has achieved great success due to deep learning, many factors may affect the quality of faces in the wild, such as pose changes, age variations, and light changes, which can seriously affect the performance of face recognition ...
Peng Zhang +3 more
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Robust Multi-scale Extraction of Blob Features [PDF]
, 2003 This paper presents a method for detection of homogeneous regions in grey-scale images, representing them as blobs. In order to be fast, and not to favour one scale over others, the method uses a scale pyramid. In contrast to most multi-scale methods this one is non-linear, since it employs robust estimation rather than averaging to move through scale ...
Per-Erik Forssén, Gösta H. Granlund
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Multi‐scale Feature Extraction on Point‐Sampled Surfaces [PDF]
Computer Graphics Forum, 2003 Abstract We present a new technique for extracting line‐type features on point‐sampled geometry. Given an unstructuredpoint cloud as input, our method first applies principal component analysis on local neighborhoods toclassify points according to the likelihood that they belong to a feature.
Mark Pauly +2 more
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Disparity Using Feature Points in Multi Scale [PDF]
, 2002 In this paper we describe a statistical framework for binocular disparity estimation. We use a bank of Gabor filters to compute multiscale phase signatures at detected feature points. Using a von Mises distribution,w e calculate correspondence probabilities for the feature points in different images using the phase differences at different scales.
Ilkay Ulusoy +2 more
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Shape Matching Based on Multi-Scale Invariant Features
IEEE Access, 2019 Shape matching has been extensively used in various fields. The local feature-based or global feature-based algorithms can hardly describe the shape comprehensively due to their inherent defects.
Zhang Kun, Ma Xiao, Li Xinguo
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