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» Contour-Based Learning for Object Detection
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AVSS
2006
IEEE
14 years 1 months ago
An Online Discriminative Approach to Background Subtraction
We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that ...
Li Cheng, Shaojun Wang, Dale Schuurmans, Terry Cae...
CVPR
2009
IEEE
15 years 2 months ago
Efficient Representation of Local Geometry for Large Scale Object Retrieval
State of the art methods for image and object re- trieval exploit both appearance (via visual words) and local geometry (spatial extent, relative pose). In large scale problems,...
Michal Perdoch (Czech Technical University), Ondre...
ISBI
2009
IEEE
14 years 2 months ago
Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging
In live-cell fluorescence microscopy imaging, quantitative analysis of biological image data generally involves the detection of many subresolution objects, appearing as diffract...
Ihor Smal, Marco Loog, Wiro J. Niessen, Erik H. W....
CVPR
2009
IEEE
15 years 2 months ago
Recognition using Regions
This paper presents a unified framework for object detection, segmentation, and classification using regions. Region features are appealing in this context because: (1) they enco...
Chunhui Gu, Joseph J. Lim, Pablo Arbelaez, Jitendr...
ICDE
2008
IEEE
141views Database» more  ICDE 2008»
14 years 9 months ago
SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
In this paper, we present a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique ...
Ji Zhang, Qigang Gao, Hai H. Wang