A NON-PARAMETRIC TRAINABLE OBJECT-DETECTION MODEL USING A CONCEPT OF RETINOTOPIC SAMPLING

摘要

A retina has a space-variant sampling mechanism and an orientation-sensitive mechanism. The space-variant sampling mechanism of the retina is called Retinotopic Sampling (RS). With these mechanisms, the object-detection is formulated as finding an appropriate coordinate transformation from a coordinate system on the input image to the retina. The appropriate coordinate transformation is found using maximum likelihood method. By using the model based on RS, we formulate a kernel function as an analytical function of the information on the input image, the position and the size of the object in the input image. Then the object-detection is realised as a gradient decent method for a discriminant function trained by Support Vector Machine (SVM). This detection mechanism realises faster detection than exploring a visual scene in raster-like fashion. The discriminant function outperforms results of SVMs using a kernel function using intensities of all pixels (based on independently published results), in face detection experiments over test images in the MIT-CBCL face database.