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» Feature Selection for Automatic Image Annotation
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MM
2009
ACM
185views Multimedia» more  MM 2009»
15 years 6 months ago
Deep exploration for experiential image retrieval
Experiential image retrieval systems aim to provide the user with a natural and intuitive search experience. The goal is to empower the user to navigate large collections based on...
Bart Thomee, Mark J. Huiskes, Erwin M. Bakker, Mic...
BILDMED
2011
313views Algorithms» more  BILDMED 2011»
14 years 3 months ago
Automatic Multi-modal ToF/CT Organ Surface Registration
Abstract. In the field of image-guided liver surgery (IGLS), the initial registration of the intra-operative organ surface with preoperative tomographic image data is performed on...
Kerstin Müller, Sebastian Bauer, Jakob Wasza,...
RITA
2006
302views more  RITA 2006»
14 years 11 months ago
Content-Based Image Retrieval: Theory and Applications
: Advances in data storage and image acquisition technologies have enabled the creation of large image datasets. In this scenario, it is necessary to develop appropriate informatio...
Ricardo da Silva Torres, Alexandre X. Falcã...
IJCV
2008
192views more  IJCV 2008»
14 years 11 months ago
Learning to Locate Informative Features for Visual Identification
Object identification (OID) is specialized recognition where the category is known (e.g. cars) and the algorithm recognizes an object's exact identity (e.g. Bob's BMW). ...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
IAAI
2003
15 years 1 months ago
Searching for Hidden Messages: Automatic Detection of Steganography
Steganography is the field of hiding messages in apparently innocuous media (e.g. images), and steganalysis is the field of detecting these covert messages. Almost all steganalysi...
George Berg, Ian Davidson, Ming-Yuan Duan, Goutam ...