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Attractor Networks for Shape Recognition
Neural Computation, 2001We describe a system of thousands of binary perceptrons with coarse-oriented edges as input that is able to recognize shapes, even in a context with hundreds of classes. The perceptrons have randomized feed-forward connections from the input layer and form a recurrent network among themselves.
Amit, Yali, Mascaro, Massimo
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Learning Multi-View Representation With LSTM for 3-D Shape Recognition and Retrieval
IEEE transactions on multimedia, 2019Shape representation for 3-D models is an important topic in computer vision, multimedia analysis, and computer graphics. Recent multiview-based methods demonstrate promising performance for 3-D shape recognition and retrieval.
Chao Ma, Yulan Guo, Jungang Yang, W. An
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PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
ACM Multimedia, 20183D object recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent proliferation of deep learning, various deep models with different representations have achieved the state-of-the-art performance.
Haoxuan You, Yifan Feng, R. Ji, Yue Gao
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MMJN: Multi-Modal Joint Networks for 3D Shape Recognition
ACM Multimedia, 20193D shape recognition has attracted wide research attention in the field of multimedia and computer vision. With the recent advance of deep learning, various deep models with different representations have achieved the state-of-the-art performances. Among
Wei-zhi Nie +4 more
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Optical Society of America Annual Meeting, 1990
Machine-vision research shows that accurate three-dimensional (3-D) reconstruction of objects is difficult. Human-vision research shows that human judgments about 3-D features (depth and volume) are not accurate. This converging evidence, along with the known human ability to recognize shapes, suggests that accurate recognition of 3-D shapes is ...
Zygmunt Pizlo, Azriel Rosenfeld
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Machine-vision research shows that accurate three-dimensional (3-D) reconstruction of objects is difficult. Human-vision research shows that human judgments about 3-D features (depth and volume) are not accurate. This converging evidence, along with the known human ability to recognize shapes, suggests that accurate recognition of 3-D shapes is ...
Zygmunt Pizlo, Azriel Rosenfeld
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Shape Recognition with Spectral Distances
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011Recent works have shown the use of diffusion geometry for various pattern recognition applications, including nonrigid shape analysis. In this paper, we introduce spectral shape distance as a general framework for distribution-based shape similarity and show that two recent methods for shape similarity due to Rustamov and Mahmoudi and Sapiro are ...
Michael M, Bronstein +1 more
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Qualitative Geometry for Shape Recognition
Applied Intelligence, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dugat, Vincent +2 more
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Computer Vision and Pattern Recognition, 2019Computer graphics, 3D computer vision and robotics communities have produced multiple approaches to representing 3D geometry for rendering and reconstruction. These provide trade-offs across fidelity, efficiency and compression capabilities. In this work,
Jeong Joon Park +4 more
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Tactile sensor pad: shape recognition
Proceedings of Tenth International Symposium on Intelligent Control, 2002Most robot tactile sensing research has focused on object shape recognition with tactile sensors. In this paper we examine the model of a tactile pad sensor, assumed to be a finite dimension medium. The model of the tactile pad sensor is obtained using an integral relation between the boundary conditions and the stress tensor.
FAMULARO, Domenico, Muraca P.
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British Journal of Psychology, 1957
Reasons are given for doubting whether certain of Lashley's experiments, purporting to be investigations of shape discriminations, really are such. In particular his finding concerning square‐circle discrimination cannot be held to lend support to Deutsch's theory of shape recognition.
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Reasons are given for doubting whether certain of Lashley's experiments, purporting to be investigations of shape discriminations, really are such. In particular his finding concerning square‐circle discrimination cannot be held to lend support to Deutsch's theory of shape recognition.
openaire +2 more sources