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IVC
2008
69views more  IVC 2008»
13 years 7 months ago
Unsupervised texture classification: Automatically discover and classify texture patterns
In this paper, we present a novel approach to classify texture collections. This approach does not require experts to provide annotated training set. Given the image collection, w...
Lei Qin, Qingfang Zheng, Shuqiang Jiang, Qingming ...
TMI
2010
156views more  TMI 2010»
13 years 2 months ago
Snakules: A Model-Based Active Contour Algorithm for the Annotation of Spicules on Mammography
Abstract--We have developed a novel, model-based active contour algorithm, termed "snakules", for the annotation of spicules on mammography. At each suspect spiculated ma...
Gautam S. Muralidhar, Alan Conrad Bovik, J. David ...
CVPR
2007
IEEE
14 years 9 months ago
OPTIMOL: automatic Online Picture collecTion via Incremental MOdel Learning
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
Li-Jia Li, Gang Wang, Fei-Fei Li 0002
CVPR
2007
IEEE
14 years 9 months ago
3D Probabilistic Feature Point Model for Object Detection and Recognition
This paper presents a novel statistical shape model that can be used to detect and localise feature points of a class of objects in images. The shape model is inspired from the 3D...
Sami Romdhani, Thomas Vetter
CVPR
2012
IEEE
11 years 10 months ago
The use of on-line co-training to reduce the training set size in pattern recognition methods: Application to left ventricle seg
The use of statistical pattern recognition models to segment the left ventricle of the heart in ultrasound images has gained substantial attention over the last few years. The mai...
Gustavo Carneiro, Jacinto C. Nascimento