Yali Amit Professor Department of Statistics and the College Find the corpus callosum Show the anatomies Image Analysis Object Detection and Neural Architectures for Visual Selection Visual selection and object detection Objects are represented through flexible star type planar arrangements of binary local features, which are in turn star type planar arrangements of oriented edges. Candidate locations are detected over a range of scales and other deformations. The flexibility of the arrangements provides the required invariance.
Training therefore requires only small data sets.
Detection is achieved through a hierarchical Hough transform. The first level is for detecting all local features in the image, the second level for detecting candidate locations for the object. The features are not derived from analytic properties of geomteric objects as in classical Hough transform applications, rather from local statistics of the training data. Some detection examples , for more details see A computational model for visual selection . A parallel neural net architecture for this algorithm and connections to the visual system are discussed in A neural network architecture for visual selction .