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» Implicit spatial inference with sparse local features
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BMVC
1998
13 years 7 months ago
ORASSYLL: Object Recognition with Autonomously Learned and Sparse Symbolic Representations Based on Local Line Detectors
We introduce an object recognition system in which objects are represented as a sparse and spatially organized set of local (bent) line segments. The line segments correspond to b...
Norbert Krüger, Niklas Lüdtke
BMCBI
2010
182views more  BMCBI 2010»
13 years 6 months ago
Beyond co-localization: inferring spatial interactions between sub-cellular structures from microscopy images
Background: Sub-cellular structures interact in numerous direct and indirect ways in order to fulfill cellular functions. While direct molecular interactions crucially depend on s...
Jo A. Helmuth, Grégory Paul, Ivo F. Sbalzar...
GISCIENCE
2010
Springer
215views GIS» more  GISCIENCE 2010»
13 years 6 months ago
Microtheories for Spatial Data Infrastructures - Accounting for Diversity of Local Conceptualizations at a Global Level
The categorization of our environment into feature types is an essential prerequisite for cartography, geographic information retrieval, routing applications, spatial decision supp...
Stephanie Duce, Krzysztof Janowicz
ICCV
2003
IEEE
14 years 8 months ago
Feature Selection for Unsupervised and Supervised Inference: the Emergence of Sparsity in a Weighted-based Approach
The problem of selecting a subset of relevant features in a potentially overwhelming quantity of data is classic and found in many branches of science. Examples in computer vision...
Lior Wolf, Amnon Shashua
ECCV
2004
Springer
14 years 8 months ago
A Statistical Model for General Contextual Object Recognition
We consider object recognition as the process of attaching meaningful labels to specific regions of an image, and propose a model that learns spatial relationships between objects....
Peter Carbonetto, Nando de Freitas, Kobus Barnard