7144646
Learning Winner-take-all Competition Between Groups of Neurons in Lateral Inhibitory Networks
It has long been known that lateral inhibition in neural networks can lead to a winner-take-all competition, so that only a single neuron is active at a steady state. Here we show how to organize lateral inhibition so that groups of neurons compete to be active. Given a collection of potentially overlapping groups, the inhibitory connectivity is set by a formula that can be interpreted as arising from a simple learning rule. Our analysis demonstrates that such inhibition generally results in winner-take-all competition between the given groups, with the exception of some degenerate cases. In a broader context, the network serves as a particular illustration of the general distinction between permitted and forbidden sets, which was introduced recently. From this viewpoint, the computational function of our network is to store and retrieve memories as permitted sets of coactive neurons.
2399803
Exact solution of the nonlinear dynamics of recurrent neural mechanisms for direction selectivity
Abstract Different theoretical models have tried to investigate the feasibility of recurrent neural mechanisms for achieving direction selectivity in the visual cortex. The mathematical analysis of such models has been restricted so far to the case of purely linear networks. We present an exact analytical solution of the nonlinear dynamics of a class of direction selective recurrent neural models with threshold nonlinearity. Our mathematical analysis shows that such networks have form-stable stimulus-locked traveling pulse solutions that are appropriate for modeling the responses of direction selective cortical neurons. Our analysis shows also that the stability of such solutions can break down giving raise to a different class of solutions (“lurching activity waves”) that are characterized by a specific spatio-temporal periodicity. These solutions cannot arise in models for direction selectivity with purely linear spatio-temporal filtering.
6927574
A Charged Active Contour Based on Electrostatics
We propose a novel active contour model by incorporating particle based electrostatic interactions into the geometric active contour framework. The proposed active contour, embedded in level sets, propagates under the joint influence of a boundary attraction force and a boundary competition force. Unlike other contour models, the proposed vector field dynamically adapts by updating itself when a contour reaches a boundary. The model is then more invariant to initialisation and possesses better convergence abilities. Analytical and comparative results are presented on synthetic and real images.
12048322
Inspecting Colour Tonality on Textured Surfaces
We present a multidimensional histogram method to inspect tonality on colour textured surfaces, e.g. ceramic tiles. Comparison in the noise dominated chromatic channels is error prone. We perform vector- ordered colour smoothing and generate a PCA-based reconstruction of a query tile based on a reference tile eigenspace. Histograms of local feature vectors are then compared for tonality defect detection. The proposed method is compared and evaluated on a data set with groundtruth.
15110783
Detecting dominant locations from search queries
Accurately and effectively detecting the locations where search queries are truly about has huge potential impact on increasing search relevance. In this paper, we define a search query's dominant location (QDL) and propose a solution to correctly detect it. QDL is geographical location(s) associated with a query in collective human knowledge, i.e., one or few prominent locations agreed by majority of people who know the answer to the query. QDL is a subjective and collective attribute of search queries and we are able to detect QDLs from both queries containing geographical location names and queries not containing them. The key challenges to QDL detection include false positive suppression (not all contained location names in queries mean geographical locations), and detecting implied locations by the context of the query. In our solution, a query is recursively broken into atomic tokens according to its most popular web usage for reducing false positives. If we do not find a dominant location in this step, we mine the top search results and/or query logs (with different approaches discussed in this paper) to discover implicit query locations. Our large-scale experiments on recent MSN Search queries show that our query location detection solution has consistent high accuracy for all query frequency ranges.
1573020
Automatic browsing of large pictures on mobile devices
Pictures have become increasingly common and popular in mobile communications. However, due to the limitation of mobile devices, there is a need to develop new technologies to facilitate the browsing of large pictures on the small screen. In this paper, we propose a novel approach which is able to automate the scrolling and navigation of a large picture with a minimal amount of user interaction on mobile devices. An image attention model is employed to illustrate the information structure within an image. An optimal image browsing path is then calculated based on the image attention model to simulate the human browsing behaviors. Experimental evaluations of the proposed mechanism indicate that our approach is an effective way for viewing large images on small displays.
22043660
Colour tonality inspection using eigenspace features
In industrial quality inspection of colour texture surfaces, such as ceramic tiles or fabrics, it is important to maintain a consistent colour shade or tonality during production. We present a multidimensional histogram method using a novelty detection scheme to inspect the surfaces. The image noise, introduced by the imaging system, is found mainly to affect the chromatic channels. For colour tonality inspection, the difference between images is very subtle and comparison in the noise dominated chromatic channels is error prone. We perform vector-ordered colour smoothing and extract a localised feature vector at each pixel. The resulting histogram represents an encapsulation of local and global information. Principal component analysis (PCA) is performed on this multidimensional feature space of an automatically selected reference image to obtain reliable colour shade features, which results in a reference eigenspace. Then unseen product images are projected onto this eigenspace and compared for tonality defect detection using histogram comparison. The proposed method is compared and evaluated on a data set with groundtruth.