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RE-YOLOv5: Enhancing Occluded Road Object Detection via Visual Receptive Field Improvements. [PDF]
Sensors (Basel)Li T +8 more
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Proceedings of the 2011 SIGGRAPH Asia Conference on - SA '11, 2011
We present a novel approach for rapid numerical approximation of convolutions with filters of large support. Our approach consists of a multiscale scheme, fashioned after the wavelet transform, which computes the approximation in linear time. Given a specific large target filter to approximate, we first use numerical optimization to design a set of ...
Raanan Fattal +2 more
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We present a novel approach for rapid numerical approximation of convolutions with filters of large support. Our approach consists of a multiscale scheme, fashioned after the wavelet transform, which computes the approximation in linear time. Given a specific large target filter to approximate, we first use numerical optimization to design a set of ...
Raanan Fattal +2 more
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Journal of Pediatric Health Care, 2007
1. What differentials should be considered in this case? The list of differential diagnoses for vomiting in children is considerable and beyond the scope of this article. Possibilities should be included or excluded based on careful history and examination, other temporal associations (i.e., K.
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1. What differentials should be considered in this case? The list of differential diagnoses for vomiting in children is considerable and beyond the scope of this article. Possibilities should be included or excluded based on careful history and examination, other temporal associations (i.e., K.
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Aequationes Mathematicae, 1999
We show that every nonnegative measurable solution of the convolution inequality¶¶\(\phi (t)\ge \int_E^{}\phi (t+s)d\mu (s),\qquad t \in E, \)¶(where E is a closed additive subgroup of R and μ a suitable measure) is equal almost everywhere to an exponential function.
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We show that every nonnegative measurable solution of the convolution inequality¶¶\(\phi (t)\ge \int_E^{}\phi (t+s)d\mu (s),\qquad t \in E, \)¶(where E is a closed additive subgroup of R and μ a suitable measure) is equal almost everywhere to an exponential function.
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Convolution and Trimming via Convolution
2012What is Trimming? It is identical to convolution in results. Then what are differences? Convolution occurs in a technical system as a phenomenon; it is part of TS evolutionary wave. Trimming is a methodology purposefully applied to a TS to increase its ideality by achieving specific gain in MUF and/or to decrease one or all of M, D, or E from MDE by a ...
Saurabh Kwatra, Yuri Salamatov
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ACM SIGGRAPH Computer Graphics, 1991
Smoothly blended articulated models are often difficult to construct using current techniques. Our solution in this paper is to extend the surfaces introduced by Blinn [Blinn 1982] by using three-dimensional convolution with skeletons composed of polygons or curves.
Ken Shoemake, Jules Bloomenthal
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Smoothly blended articulated models are often difficult to construct using current techniques. Our solution in this paper is to extend the surfaces introduced by Blinn [Blinn 1982] by using three-dimensional convolution with skeletons composed of polygons or curves.
Ken Shoemake, Jules Bloomenthal
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Coupled convolution layer for convolutional neural network
Neural Networks, 2016We propose a coupled convolution layer comprising multiple parallel convolutions with mutually constrained filters. Inspired by biological human vision mechanism, we constrain the convolution filters such that one set of filter weights should be geometrically rotated, mirrored, or be the negative of the other.
Masayuki Tanaka +3 more
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