Results 21 to 30 of about 494,722 (259)
A multi-scale features-based method to detect Oplegnathus
Information Processing in Agriculture, 2021 It is of great significance to use underwater video and image processing technology to detect and analyze fish behaviors. In this paper, an Oplegnathus image dataset for fish behaviors study by deep learning algorithm is constructed, and the data is ...
Jun Yue +6 more
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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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Efficient Multi-Scale Feature Generation Adaptive Network [PDF]
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021 Recently, an early exit network, which dynamically adjusts the model complexity during inference time, has achieved remarkable performance and neural network efficiency to be used for various applications. So far, many researchers have been focusing on reducing the redundancy of input sample or model architecture.
Gwanghan Lee +3 more
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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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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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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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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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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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Multi-Time-Scale Features for Accurate Respiratory Sound Classification
Applied Sciences, 2020 The COVID-19 pandemic has amplified the urgency of the developments in computer-assisted medicine and, in particular, the need for automated tools supporting the clinical diagnosis and assessment of respiratory symptoms.
Alfonso Monaco +5 more
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IEEE Access, 2021 The width of convolutional neural networks (CNNs) is crucial for improving performance. Many wide CNNs use a convolutional layer to fuse multiscale features or fuse the preceding features to subsequent features.
Xiaohong Yu +4 more
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