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ICMLA
2009
13 years 5 months ago
Exploring Scale-Induced Feature Hierarchies in Natural Images
Recently there has been considerable interest in topic models based on the bag-of-features representation of images. The strong independence assumption inherent in the bag-of-feat...
Jukka Perkiö, Tinne Tuytelaars, Wray L. Bunti...
MM
2006
ACM
130views Multimedia» more  MM 2006»
14 years 1 months ago
An unsupervised method for clustering images based on their salient regions of interest
We have developed a biologically-motivated, unsupervised way of grouping together images whose salient regions of interest (ROIs) are perceptually similar regardless of the visual...
Gustavo B. Borba, Humberto R. Gamba, Oge Marques, ...
IROS
2008
IEEE
180views Robotics» more  IROS 2008»
14 years 1 months ago
Aligning point cloud views using persistent feature histograms
— In this paper we investigate the usage of persistent point feature histograms for the problem of aligning point cloud data views into a consistent global model. Given a collect...
Radu Bogdan Rusu, Nico Blodow, Zoltan Csaba Marton...
SMC
2010
IEEE
132views Control Systems» more  SMC 2010»
13 years 5 months ago
Selection of SIFT feature points for scene description in robot vision
This paper presents a method for selection of SIFT(Scale-Invariant Feature Transform) feature points using OC-SVM (One Class-Support Vector Machines). We proposed the method for au...
Yuya Utsumi, Masahiro Tsukada, Hirokazu Madokoro, ...
ICIAR
2010
Springer
14 years 8 days ago
Adaptation of SIFT Features for Robust Face Recognition
Abstract. The Scale Invariant Feature Transform (SIFT) is an algorithm used to detect and describe scale-, translation- and rotation-invariant local features in images. The origina...
Janez Krizaj, Vitomir Struc, Nikola Pavesic