1,664 research outputs found
A survey of visual preprocessing and shape representation techniques
Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention)
Hierarchical inference of disparity
Disparity selective cells in V1 respond to the correlated receptive fields of the left and right retinae, which do not necessarily correspond to the same object in the 3D scene, i.e., these cells respond equally to both false and correct stereo matches. On the other hand, neurons in the extrastriate visual area V2 show much stronger responses to correct visual matches [Bakin et al, 2000]. This indicates that a part of the stereo correspondence problem is solved during disparity processing in these two areas. However, the mechanisms employed by the brain to accomplish this task are not yet understood. Existing computational models are mostly based on cooperative computations in V1 [Marr and Poggio 1976, Read and Cumming 2007], without exploiting the potential benefits of the hierarchical structure between V1 and V2. Here we propose a two-layer graphical model for disparity estimation from stereo. The lower layer matches the linear responses of neurons with Gabor receptive fields across images. Nodes in the upper layer infer a sparse code of the disparity map and act as priors that help disambiguate false from correct matches. When learned on natural disparity maps, the receptive fields of the sparse code converge to oriented depth edges, which is consistent with the electrophysiological studies in macaque [von der Heydt et al, 2000]. Moreover, when such a code is used for depth inference in our two layer model, the resulting disparity map for the Tsukuba stereo pair [middlebury database] has 40% less false matches than the solution given by the first layer. Our model offers a demonstration of the hierarchical disparity computation, leading to testable predictions about V1-V2 interactions
Two-dimensional shape recognition using sparse distributed memory
Researchers propose a method for recognizing two-dimensional shapes (hand-drawn characters, for example) with an associative memory. The method consists of two stages: first, the image is preprocessed to extract tangents to the contour of the shape; second, the set of tangents is converted to a long bit string for recognition with sparse distributed memory (SDM). SDM provides a simple, massively parallel architecture for an associative memory. Long bit vectors (256 to 1000 bits, for example) serve as both data and addresses to the memory, and patterns are grouped or classified according to similarity in Hamming distance. At the moment, tangents are extracted in a simple manner by progressively blurring the image and then using a Canny-type edge detector (Canny, 1986) to find edges at each stage of blurring. This results in a grid of tangents. While the technique used for obtaining the tangents is at present rather ad hoc, researchers plan to adopt an existing framework for extracting edge orientation information over a variety of resolutions, such as suggested by Watson (1987, 1983), Marr and Hildreth (1980), or Canny (1986)
Feature detection using spikes: the greedy approach
A goal of low-level neural processes is to build an efficient code extracting
the relevant information from the sensory input. It is believed that this is
implemented in cortical areas by elementary inferential computations
dynamically extracting the most likely parameters corresponding to the sensory
signal. We explore here a neuro-mimetic feed-forward model of the primary
visual area (VI) solving this problem in the case where the signal may be
described by a robust linear generative model. This model uses an over-complete
dictionary of primitives which provides a distributed probabilistic
representation of input features. Relying on an efficiency criterion, we derive
an algorithm as an approximate solution which uses incremental greedy inference
processes. This algorithm is similar to 'Matching Pursuit' and mimics the
parallel architecture of neural computations. We propose here a simple
implementation using a network of spiking integrate-and-fire neurons which
communicate using lateral interactions. Numerical simulations show that this
Sparse Spike Coding strategy provides an efficient model for representing
visual data from a set of natural images. Even though it is simplistic, this
transformation of spatial data into a spatio-temporal pattern of binary events
provides an accurate description of some complex neural patterns observed in
the spiking activity of biological neural networks.Comment: This work links Matching Pursuit with bayesian inference by providing
the underlying hypotheses (linear model, uniform prior, gaussian noise
model). A parallel with the parallel and event-based nature of neural
computations is explored and we show application to modelling Primary Visual
Cortex / image processsing.
http://incm.cnrs-mrs.fr/perrinet/dynn/LaurentPerrinet/Publications/Perrinet04tau
Learning sparse representations of depth
This paper introduces a new method for learning and inferring sparse
representations of depth (disparity) maps. The proposed algorithm relaxes the
usual assumption of the stationary noise model in sparse coding. This enables
learning from data corrupted with spatially varying noise or uncertainty,
typically obtained by laser range scanners or structured light depth cameras.
Sparse representations are learned from the Middlebury database disparity maps
and then exploited in a two-layer graphical model for inferring depth from
stereo, by including a sparsity prior on the learned features. Since they
capture higher-order dependencies in the depth structure, these priors can
complement smoothness priors commonly used in depth inference based on Markov
Random Field (MRF) models. Inference on the proposed graph is achieved using an
alternating iterative optimization technique, where the first layer is solved
using an existing MRF-based stereo matching algorithm, then held fixed as the
second layer is solved using the proposed non-stationary sparse coding
algorithm. This leads to a general method for improving solutions of state of
the art MRF-based depth estimation algorithms. Our experimental results first
show that depth inference using learned representations leads to state of the
art denoising of depth maps obtained from laser range scanners and a time of
flight camera. Furthermore, we show that adding sparse priors improves the
results of two depth estimation methods: the classical graph cut algorithm by
Boykov et al. and the more recent algorithm of Woodford et al.Comment: 12 page
Dictionary learning with large step gradient descent for sparse representations
This is the accepted version of an article published in Lecture Notes in Computer Science Volume 7191, 2012, pp 231-238. The final publication is available at link.springer.com
http://www.springerlink.com/content/l1k4514765283618
The Archival Challenges and Choices of a Small Non-profit Organization Attempting to Preserve Its Unique Past
Holden, located in Washington’s Cascade Mountains above the West side of Lake Chelan, originated as a copper-mining company town in 1937. In 1960, three years after the operation shut down, it was transformed into a non-profit Lutheran retreat center, now known as Holden Village. Both the mining and Lutheran communities have generated substantial archival collections, the three largest of which are currently housed at the University of Washington, Pacific Lutheran University, and Holden itself. Making use of finding aids, reports and summaries written by past and present Holden archivists, and the personal recollections of former residents, this paper traces the history of these collections, including episodes of both institutional neglect and concerted preservation efforts. To some extent, this history serves as a case study for the archival challenges faced by small, remote communities, but it also demonstrates ways in which Holden may be unique
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