Event-Based Feature Detection, Recognition and Classification
暂无分享,去创建一个
[1] F. Massey. The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .
[2] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[3] T. Greville,et al. Some Applications of the Pseudoinverse of a Matrix , 1960 .
[4] H. Lilliefors. On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown , 1967 .
[5] J. MacQueen. Some methods for classification and analysis of multivariate observations , 1967 .
[6] B. Ajne,et al. A simple test for uniformity of a circular distribution , 1968 .
[7] S. Nagata,et al. An electronic model of the retina , 1970 .
[8] Hans P. Moravec. Obstacle avoidance and navigation in the real world by a seeing robot rover , 1980 .
[9] B. Heller. Circular Statistics in Biology, Edward Batschelet. Academic Press, London & New York (1981), 371, Price $69.50 , 1983 .
[10] J. H. Zar,et al. Biostatistical Analysis (5th Edition) , 1984 .
[11] Christopher G. Harris,et al. A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.
[12] Carver A. Mead,et al. Neuromorphic electronic systems , 1990, Proc. IEEE.
[13] Nicholas I. Fisher,et al. Statistical Analysis of Circular Data , 1993 .
[14] Carlo Tomasi,et al. Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.
[15] Misha A. Mahowald,et al. An Analog VLSI System for Stereoscopic Vision , 1994 .
[16] T. Lindeberg. Scale-Space Theory : A Basic Tool for Analysing Structures at Different Scales , 1994 .
[17] Patrick J. Grother,et al. NIST Special Database 19 Handprinted Forms and Characters Database , 1995 .
[18] John Wawrzynek,et al. A multi-sender asynchronous extension to the AER protocol , 1995, Proceedings Sixteenth Conference on Advanced Research in VLSI.
[19] Kwabena Boahen,et al. A retinomorphic vision system , 1996, IEEE Micro.
[20] X. Arreguit,et al. A CMOS motion detector system for pointing devices , 1996, 1996 IEEE International Solid-State Circuits Conference. Digest of TEchnical Papers, ISSCC.
[21] T Bonhoeffer,et al. Orientation selectivity in pinwheel centers in cat striate cortex. , 1997, Science.
[22] Gert Cauwenberghs,et al. An analog VLSI architecture for auditory based feature extraction , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[23] Kwabena Boahen,et al. Communicating neuronal ensembles between neuromorphic chips , 1998 .
[24] Andreas G. Andreou,et al. AER image filtering architecture for vision-processing systems , 1999 .
[25] Rodney J. Douglas,et al. A pulse-coded communications infrastructure for neuromorphic systems , 1999 .
[26] Gill Mould,et al. Directional statistics of the wind and waves , 2000 .
[27] Dariu Gavrila,et al. Pedestrian Detection from a Moving Vehicle , 2000, ECCV.
[28] Kwabena Boahen,et al. Point-to-point connectivity between neuromorphic chips using address events , 2000 .
[29] Gert Cauwenberghs,et al. Analog VLSI spiking neural network with address domain probabilistic synapses , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).
[30] Tobi Delbrück,et al. Orientation-Selective aVLSI Spiking Neurons , 2001, NIPS.
[31] S. R. Jammalamadaka,et al. Topics in Circular Statistics , 2001 .
[32] A. van Schaik. Building blocks for electronic spiking neural networks. , 2001, Neural networks : the official journal of the International Neural Network Society.
[33] Eugenio Culurciello,et al. High dynamic range, arbitrated address event representation digital imager , 2001, ISCAS 2001. The 2001 IEEE International Symposium on Circuits and Systems (Cat. No.01CH37196).
[34] Simon Haykin,et al. GradientBased Learning Applied to Document Recognition , 2001 .
[35] Jörg Kramer,et al. An on/off transient imager with event-driven, asynchronous read-out , 2002, 2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).
[36] Charles M. Higgins,et al. A biologically inspired modular VLSI system for visual measurement of self-motion , 2002 .
[37] James J. Little,et al. Mobile Robot Localization and Mapping with Uncertainty using Scale-Invariant Visual Landmarks , 2002, Int. J. Robotics Res..
[38] F. Heitger,et al. A 100×100 pixel silicon retina for gradient extraction with steering filter capabilities and temporal output coding , 2002, IEEE J. Solid State Circuits.
[39] Guang-Bin Huang,et al. Learning capability and storage capacity of two-hidden-layer feedforward networks , 2003, IEEE Trans. Neural Networks.
[40] E. Culurciello,et al. A biomorphic digital image sensor , 2003, IEEE J. Solid State Circuits.
[41] F. Werblin,et al. Rapid global shifts in natural scenes block spiking in specific ganglion cell types , 2003, Nature Neuroscience.
[42] Nikos A. Vlassis,et al. The global k-means clustering algorithm , 2003, Pattern Recognit..
[43] F. Heitger,et al. A 128 × 128 pixel 120-dB dynamic-range vision-sensor chip for image contrast and orientation extraction , 2003, IEEE J. Solid State Circuits.
[44] Inderjit S. Dhillon,et al. Generative model-based clustering of directional data , 2003, KDD '03.
[45] G LoweDavid,et al. Distinctive Image Features from Scale-Invariant Keypoints , 2004 .
[46] Shih-Chii Liu,et al. Temporal coding in a silicon network of integrate-and-fire neurons , 2004, IEEE Transactions on Neural Networks.
[47] Chris Eliasmith,et al. Neural Engineering: Computation, Representation, and Dynamics in Neurobiological Systems , 2004, IEEE Transactions on Neural Networks.
[48] Yan Ke,et al. PCA-SIFT: a more distinctive representation for local image descriptors , 2004, CVPR 2004.
[49] Ivan Laptev,et al. Local Descriptors for Spatio-temporal Recognition , 2004, SCVMA.
[50] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[51] Kwabena Boahen,et al. Optic nerve signals in a neuromorphic chip II: testing and results , 2004, IEEE Transactions on Biomedical Engineering.
[52] Pietro Perona,et al. Learning Generative Visual Models from Few Training Examples: An Incremental Bayesian Approach Tested on 101 Object Categories , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.
[53] Kwabena Boahen,et al. Optic nerve signals in a neuromorphic chip I: Outer and inner retina models , 2004, IEEE Transactions on Biomedical Engineering.
[54] Inderjit S. Dhillon,et al. Clustering on the Unit Hypersphere using von Mises-Fisher Distributions , 2005, J. Mach. Learn. Res..
[55] Serge J. Belongie,et al. Behavior recognition via sparse spatio-temporal features , 2005, 2005 IEEE International Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance.
[56] Timothy K. Horiuchi,et al. An ultrasonic filterbank with spiking neurons , 2005, 2005 IEEE International Symposium on Circuits and Systems.
[57] Bill Triggs,et al. Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[58] R. Etienne-Cummings,et al. Temporal change threshold detection imager , 2005, ISSCC. 2005 IEEE International Digest of Technical Papers. Solid-State Circuits Conference, 2005..
[59] Ramakant Nevatia,et al. Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[60] Ralf Engbert. Microsaccades: A microcosm for research on oculomotor control, attention, and visual perception. , 2006, Progress in brain research.
[61] Luc Van Gool,et al. Object Detection by Contour Segment Networks , 2006, ECCV.
[62] Cordelia Schmid,et al. Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.
[63] T. Delbruck,et al. A 128 128 120 dB 15 s Latency Asynchronous Temporal Contrast Vision Sensor , 2006 .
[64] Tobi Delbrück,et al. A 128 X 128 120db 30mw asynchronous vision sensor that responds to relative intensity change , 2006, 2006 IEEE International Solid State Circuits Conference - Digest of Technical Papers.
[65] Aly A. Farag,et al. CSIFT: A SIFT Descriptor with Color Invariant Characteristics , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).
[66] H. Sompolinsky,et al. The tempotron: a neuron that learns spike timing–based decisions , 2006, Nature Neuroscience.
[67] Eugene M. Izhikevich,et al. Polychronization: Computation with Spikes , 2006, Neural Computation.
[68] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[69] Chong-Wah Ngo,et al. Evaluating bag-of-visual-words representations in scene classification , 2007, MIR '07.
[70] Craig T. Jin,et al. An Address-Event Vision Sensor for Multiple Transient Object Detection , 2007, IEEE Transactions on Biomedical Circuits and Systems.
[71] Tobi Delbrück,et al. Using FPGA for visuo-motor control with a silicon retina and a humanoid robot , 2007, 2007 IEEE International Symposium on Circuits and Systems.
[72] Larry S. Davis,et al. Hierarchical Part-Template Matching for Human Detection and Segmentation , 2007, 2007 IEEE 11th International Conference on Computer Vision.
[73] Thomas Serre,et al. Robust Object Recognition with Cortex-Like Mechanisms , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[74] A. Chun,et al. On the brain , 2007, Nature Nanotechnology.
[75] Yannis Stylianou,et al. Stochastic Modeling and Quantization of Harmonic Phases in Speech using Wrapped Gaussian Mixture Models , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.
[76] George Loizou,et al. Computer vision and pattern recognition , 2007, Int. J. Comput. Math..
[77] Thomas Kubiak,et al. Applying Circular Statistics to the Analysis of Monitoring Data , 2007 .
[78] Ralph Etienne-Cummings,et al. AER Auditory Filtering and CPG for Robot Control , 2007, 2007 IEEE International Symposium on Circuits and Systems.
[79] André van Schaik,et al. AER EAR: A Matched Silicon Cochlea Pair With Address Event Representation Interface , 2005, IEEE Transactions on Circuits and Systems I: Regular Papers.
[80] Timothée Masquelier,et al. Unsupervised Learning of Visual Features through Spike Timing Dependent Plasticity , 2007, PLoS Comput. Biol..
[81] Mubarak Shah,et al. A 3-dimensional sift descriptor and its application to action recognition , 2007, ACM Multimedia.
[82] Romain Brette,et al. Neuroinformatics Original Research Article Brian: a Simulator for Spiking Neural Networks in Python , 2022 .
[83] Richard Szeliski,et al. Skeletal graphs for efficient structure from motion , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.
[84] Bernabé Linares-Barranco,et al. Advanced Vision Processing Systems: Spike-Based Simulation and Processing , 2009, ACIVS.
[85] C. Posch,et al. A Microbolometer Asynchronous Dynamic Vision Sensor for LWIR , 2009, IEEE Sensors Journal.
[86] Christopher Hunt,et al. Notes on the OpenSURF Library , 2009 .
[87] Yannis Stylianou,et al. Wrapped Gaussian Mixture Models for Modeling and High-Rate Quantization of Phase Data of Speech , 2009, IEEE Transactions on Audio, Speech, and Language Processing.
[88] Tobi Delbrück,et al. An embedded AER dynamic vision sensor for low-latency pole balancing , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.
[89] Jean-Luc Nagel,et al. An SoC combining a 132dB QVGA pixel array and a 32b DSP/MCU processor for vision applications , 2009, 2009 IEEE International Solid-State Circuits Conference - Digest of Technical Papers.
[90] Pierre Yger,et al. PyNN: A Common Interface for Neuronal Network Simulators , 2008, Front. Neuroinform..
[91] M.J. Dominguez-Morales,et al. Performance study of synthetic AER generation on CPUs for Real-Time Video based on Spikes , 2009, 2009 International Symposium on Performance Evaluation of Computer & Telecommunication Systems.
[92] Philipp Berens,et al. The circular statistics toolbox for Matlab , 2009 .
[93] Tobi Delbrück,et al. Event-based 64-channel binaural silicon cochlea with Q enhancement mechanisms , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[94] Murat Efe Guney,et al. On the limits of GPU acceleration , 2010 .
[95] Matti Pietikäinen,et al. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, TPAMI-2008-09-0620 1 WLD: A Robust Local Image Descriptor , 2022 .
[96] Eugenio Culurciello,et al. Activity-driven, event-based vision sensors , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[97] Andrzej J. Kasinski,et al. Supervised Learning in Spiking Neural Networks with ReSuMe: Sequence Learning, Classification, and Spike Shifting , 2010, Neural Computation.
[98] Stephan Schraml,et al. Dynamic stereo vision system for real-time tracking , 2010, Proceedings of 2010 IEEE International Symposium on Circuits and Systems.
[99] Ling Shao,et al. Recent advances and trends in visual tracking: A review , 2011, Neurocomputing.
[100] C. P. A C O R E T,et al. Asynchronous event-based high speed vision for microparticle tracking , 2011 .
[101] S. R. Jammalamadaka,et al. Directional Statistics, I , 2011 .
[102] Ryad Benosman,et al. Asynchronous Event-Based Hebbian Epipolar Geometry , 2011, IEEE Transactions on Neural Networks.
[103] Erkki Oja,et al. GPU-accelerated and parallelized ELM ensembles for large-scale regression , 2011, Neurocomputing.
[104] Dianhui Wang,et al. Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..
[105] Giacomo Indiveri,et al. Frontiers in Neuromorphic Engineering , 2011, Front. Neurosci..
[106] Gary R. Bradski,et al. ORB: An efficient alternative to SIFT or SURF , 2011, 2011 International Conference on Computer Vision.
[107] Fernando Díaz del Río,et al. AER Spiking Neuron Computation on GPUs: The Frame-to-AER Generation , 2011, ICONIP.
[108] Daniel Matolin,et al. A QVGA 143 dB Dynamic Range Frame-Free PWM Image Sensor With Lossless Pixel-Level Video Compression and Time-Domain CDS , 2011, IEEE Journal of Solid-State Circuits.
[109] H. B. Barlow,et al. Possible Principles Underlying the Transformations of Sensory Messages , 2012 .
[110] Ralph Etienne-Cummings,et al. Real Time Compressive Sensing Video Reconstruction in Hardware , 2012, IEEE Journal on Emerging and Selected Topics in Circuits and Systems.
[111] Suvrit Sra,et al. A short note on parameter approximation for von Mises-Fisher distributions: and a fast implementation of Is(x) , 2012, Comput. Stat..
[112] Chiara Bartolozzi,et al. Asynchronous frameless event-based optical flow , 2012, Neural Networks.
[113] Stefan Schliebs,et al. Span: Spike Pattern Association Neuron for Learning Spatio-Temporal Spike Patterns , 2012, Int. J. Neural Syst..
[114] Ryad Benosman,et al. Asynchronous Event-Based Visual Shape Tracking for Stable Haptic Feedback in Microrobotics , 2012, IEEE Transactions on Robotics.
[115] Stephan Schraml,et al. CARE: A dynamic stereo vision sensor system for fall detection , 2012, 2012 IEEE International Symposium on Circuits and Systems.
[116] Craig T. Jin,et al. Neuromorphic Audio–Visual Sensor Fusion on a Sound-Localizing Robot , 2012, Front. Neurosci..
[117] Vincent Lepetit,et al. BRIEF: Computing a Local Binary Descriptor Very Fast , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[118] Hossein Ebrahimnezhad,et al. Accurate object detection using local shape descriptors , 2013, Pattern Analysis and Applications.
[119] Hongming Zhou,et al. Silicon spiking neurons for hardware implementation of extreme learning machines , 2013, Neurocomputing.
[120] André van Schaik,et al. Learning the pseudoinverse solution to network weights , 2012, Neural Networks.
[121] Nitish V. Thakor,et al. A spiking neural network architecture for visual motion estimation , 2013, 2013 IEEE Biomedical Circuits and Systems Conference (BioCAS).
[122] Tobi Delbruck,et al. Robotic goalie with 3 ms reaction time at 4% CPU load using event-based dynamic vision sensor , 2013, Front. Neurosci..
[123] Fei Han,et al. An improved evolutionary extreme learning machine based on particle swarm optimization , 2013, Neurocomputing.
[124] Ryad Benosman,et al. Event-based 3D reconstruction from neuromorphic retinas , 2013, Neural Networks.
[125] Bernabé Linares-Barranco,et al. Mapping from Frame-Driven to Frame-Free Event-Driven Vision Systems by Low-Rate Rate Coding and Coincidence Processing--Application to Feedforward ConvNets , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[126] Jim D. Garside,et al. SpiNNaker: A 1-W 18-Core System-on-Chip for Massively-Parallel Neural Network Simulation , 2013, IEEE Journal of Solid-State Circuits.
[127] Tobi Delbruck,et al. Real-time classification and sensor fusion with a spiking deep belief network , 2013, Front. Neurosci..
[128] Gregory Cohen,et al. Synthesis of neural networks for spatio-temporal spike pattern recognition and processing , 2013, Front. Neurosci..
[129] J. Long,et al. Peak tornado activity is occurring earlier in the heart of “Tornado Alley” , 2014 .
[130] Chiara Bartolozzi,et al. Event-Based Visual Flow , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[131] Rodrigo Alvarez-Icaza,et al. Neurogrid: A Mixed-Analog-Digital Multichip System for Large-Scale Neural Simulations , 2014, Proceedings of the IEEE.
[132] Gert Cauwenberghs,et al. Event-driven contrastive divergence for spiking neuromorphic systems , 2013, Front. Neurosci..
[133] Ryad Benosman,et al. Accelerated frame-free time-encoded multi-step imaging , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).
[134] Nitish V. Thakor,et al. Gait event detection through neuromorphic spike sequence learning , 2014, 5th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics.
[135] Tobi Delbrück,et al. Real-Time Gesture Interface Based on Event-Driven Processing From Stereo Silicon Retinas , 2014, IEEE Transactions on Neural Networks and Learning Systems.
[136] Huajun Chen,et al. MR-ELM: a MapReduce-based framework for large-scale ELM training in big data era , 2014, Neural Computing and Applications.
[137] Tobi Delbrück,et al. Retinomorphic Event-Based Vision Sensors: Bioinspired Cameras With Spiking Output , 2014, Proceedings of the IEEE.
[138] Bernabe Linares-Barranco,et al. On the use of orientation filters for 3D reconstruction in event-driven stereo vision , 2014, Front. Neurosci..
[139] Giacomo Indiveri,et al. PyNCS: a microkernel for high-level definition and configuration of neuromorphic electronic systems , 2014, Front. Neuroinform..
[140] Ryad Benosman,et al. Simultaneous Mosaicing and Tracking with an Event Camera , 2014, BMVC.
[141] Tobi Delbrück,et al. Live demonstration: The “DAVIS” Dynamic and Active-Pixel Vision Sensor , 2014, 2014 IEEE International Symposium on Circuits and Systems (ISCAS).
[142] R. Service. The brain chip. , 2014, Science.
[143] André van Schaik,et al. Racing to learn: statistical inference and learning in a single spiking neuron with adaptive kernels , 2014, Front. Neurosci..
[144] Bernabé Linares-Barranco,et al. Poker-DVS and MNIST-DVS. Their History, How They Were Made, and Other Details , 2015, Front. Neurosci..
[145] André van Schaik,et al. Online and adaptive pseudoinverse solutions for ELM weights , 2015, Neurocomputing.
[146] Ryad Benosman,et al. Asynchronous Event-Based Multikernel Algorithm for High-Speed Visual Features Tracking , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[147] Sio-Hoi Ieng,et al. Spatiotemporal features for asynchronous event-based data , 2015, Front. Neurosci..
[148] Steve B. Furber,et al. Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms , 2015, Front. Neurosci..
[149] Chiara Bartolozzi,et al. An Asynchronous Neuromorphic Event-Driven Visual Part-Based Shape Tracking , 2015, IEEE Transactions on Neural Networks and Learning Systems.
[150] Steve B. Furber,et al. A framework for plasticity implementation on the SpiNNaker neural architecture , 2015, Front. Neurosci..
[151] Gregory Cohen,et al. ELM solutions for event-based systems , 2014, Neurocomputing.
[152] Gregory Cohen,et al. Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades , 2015, Front. Neurosci..
[153] Alistair A. Young,et al. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , 2017, MICCAI 2017.