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CVPR
2011
IEEE
14 years 10 months ago
Kernelized Structural SVM Learning for Supervised Object Segmentation
Object segmentation needs to be driven by top-down knowledge to produce semantically meaningful results. In this paper, we propose a supervised segmentation approach that tightly ...
Luca Bertelli, Tianli Yu, Diem Vu, Salih Gokturk
EMMCVPR
2011
Springer
14 years 6 months ago
Branch and Bound Strategies for Non-maximal Suppression in Object Detection
In this work, we are concerned with the detection of multiple objects in an image. We demonstrate that typically applied objectives have the structure of a random field model, but...
Matthew B. Blaschko
VLDB
2002
ACM
113views Database» more  VLDB 2002»
16 years 6 months ago
Searching in metric spaces by spatial approximation
We propose a new data structure to search in metric spaces. A metric space is formed by a collection of objects and a distance function de ned among them, which satis es the trian...
Gonzalo Navarro
PLDI
1997
ACM
15 years 10 months ago
Generational Garbage Collection and the Radioactive Decay Model
If a fixed exponentially decreasing probability distribution function is used to model every object’s lifetime, then the age of an object gives no information about its future ...
William D. Clinger, Lars Thomas Hansen
CVPR
2003
IEEE
16 years 8 months ago
Object Segmentation Using Graph Cuts Based Active Contours
In this paper we present a graph cuts based active contours (GCBAC) approach to object segmentation problems. Our method is a combination of active contours and the optimization t...
Ning Xu, Ravi Bansal, Narendra Ahuja