Fast Ray features for learning irregular shapes
- 1 September 2009
- conference paper
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
We introduce a new class of image features, the Ray feature set, that consider image characteristics at distant contour points, capturing information which is difficult to represent with standard feature sets. This property allows Ray features to efficiently and robustly recognize deformable or irregular shapes, such as cells in microscopic imagery. Experiments show Ray features clearly outperform other powerful features including Haar-like features and Histograms of Oriented Gradients when applied to detecting irregularly shaped neuron nuclei and mitochondria. Ray features can also provide important complementary information to Haar features for other tasks such as face detection, reducing the number of weak learners and computational cost. Ray features can be efficiently precomputed to reduce cost, just as precomputing integral images reduces the overall cost of Haar features. While Rays are slightly more expensive to precompute, their computational cost is less than that of Haar features for scanning an AdaBoost-based detector window across an image at run-time.Keywords
This publication has 11 references indexed in Scilit:
- Detecting People in Images: An Edge Density ApproachPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- SUPPORT VECTOR MACHINES FOR AUTOMATIC DETECTION OF TUBERCULOSIS BACTERIA IN CONFOCAL MICROSCOPY IMAGESPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Automatic detection of unstained viable cells in bright field images using a support vector machine with an improved training procedureComputers in Biology and Medicine, 2006
- Histograms of Oriented Gradients for Human DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- WaldBoost — Learning for Time Constrained Sequential DetectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Robust Real-Time Face DetectionInternational Journal of Computer Vision, 2004
- Distance sets for shape filters and shape recognitionIEEE Transactions on Image Processing, 2003
- An extended set of Haar-like features for rapid object detectionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Shape matching and object recognition using shape contextsIeee Transactions On Pattern Analysis and Machine Intelligence, 2002
- Shape Quantization and Recognition with Randomized TreesNeural Computation, 1997