IJON 2008 - PDF Preprint Papers

IJON 2008

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IJON
2008

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Computational analysis and learning for a biologically motivated model of boundary detection

14 years 13 days ago

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In this work we address the problem of boundary detection by combining ideas and approaches from biological and computational vision. Initially, we propose a simple and efficient ...

Iasonas Kokkinos, Rachid Deriche, Olivier D. Fauge...

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Id	Views	Title	Status

1	186	Computational analysis and learning for a biologically motivated model of boundary detection	claim
2	184	Automatic medical image annotation and retrieval	claim
3	177	An asynchronous recurrent linear threshold network approach to solving the traveling salesman problem	claim
4	173	Support vector machine classification for large data sets via minimum enclosing ball clustering	claim
5	168	Spatial relationship representation for visual object searching	claim
6	158	An adaptive stereo basis method for convolutive blind audio source separation	claim
7	158	Blind separation of convolutive image mixtures	claim
8	156	Analysis of the dynamical behavior of a feedback auto-associative memory	claim
9	156	Structural identifiability of generalized constraint neural network models for nonlinear regression	claim
10	152	Topos: Spiking neural networks for temporal pattern recognition in complex real sounds	claim
11	133	A multi-objective approach to RBF network learning	claim
12	128	Independent arrays or independent time courses for gene expression time series data analysis	claim
13	124	A dynamic algorithm for blind separation of convolutive sound mixtures	claim
14	123	Bayesian estimation of time delays between unevenly sampled signals	claim
15	121	Locality sensitive semi-supervised feature selection	claim
16	118	Incremental extreme learning machine with fully complex hidden nodes	claim
17	116	Evolutionary ensemble of diverse artificial neural networks using speciation	claim
18	116	Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint	claim
19	114	A robust model for spatiotemporal dependencies	claim
20	109	Unsupervised learning neural network with convex constraint: Structure and algorithm	claim
21	106	Approximation to the Fisher-Rao metric for the focus of expansion	claim
22	105	A comprehensive review of current local features for computer vision	claim
23	105	Estimating the number of components in a mixture of multilayer perceptrons	claim
24	101	Learning dynamics and robustness of vector quantization and neural gas	claim
25	100	EvOL-Neuron: Neuronal morphology generation	claim
26	98	Factorisation and denoising of 0-1 data: A variational approach	claim
27	96	Causal reasoning by evaluating the complexity of conditional densities with kernel methods	claim
28	95	A support vector machine with integer parameters	claim
29	94	Incorporating prior knowledge in support vector machines for classification: A review	claim
30	93	Morphologically constrained ICA for extracting weak temporally correlated signals	claim
31	93	Wavelet-based separation of nonlinear show-through and bleed-through image mixtures	claim
32	92	Nonnegative matrix factorization with quadratic programming	claim
33	88	Neural network construction and training using grammatical evolution	claim
34	84	Convergence of BP algorithm for product unit neural networks with exponential weights	claim
35	84	Prediction error of a fault tolerant neural network	claim
36	81	Rate versus time representation of high-frequency spectral notches in the peripheral auditory system: A computational modeling study	claim
37	79	Delay learning and polychronization for reservoir computing	claim
38	77	Level set image segmentation with Bayesian analysis	claim
39	73	Third-order generalization: A new approach to categorizing higher-order generalization	claim
40	72	Limited receptive area neural classifier for texture recognition of mechanically treated metal surfaces	claim
41	72	Modeling and stability analysis of grey-fuzzy predictive control	claim
42	72	Efficient Bayesian inference for harmonic models via adaptive posterior factorization	claim
43	69	Neurocomputing for vision research	claim
44	66	A new extension of kernel feature and its application for visual recognition	claim
45	65	Batch kernel SOM and related Laplacian methods for social network analysis	claim
46	64	Neural networks: Algorithms and applications	claim
47	63	A theoretical framework for multiple neural network systems	claim
48	60	Spike-timing-dependent plasticity in small-world networks	claim
49	60	Improving reservoirs using intrinsic plasticity	claim
50	60	Syntactic systematicity in sentence processing with a recurrent self-organizing network	claim
51	55	The HCM for perceptual image segmentation	claim
52	49	Processing visual stimuli using hierarchical spiking neural networks	claim

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