A Trainable Object-Detection Method Using Equivalent Retinotopical Sampling and Fisher Kernel [chapter]

Hirotaka Niitsuma
2003 Lecture Notes in Computer Science  
In this paper, two object detection techniques in computer vision are proposed. The first method is a trainable objecttracking method, based on maximum likelihood. The second method is an extension of support vector machines (SVMs). The first method is an extension of Retinotopical Sampling (RS). RS is a Gaussian filter with object detection mechanism. The concept of RS was inspired by human saccadic eye movements. However, when the object size is inferred by RS the result tends to gravitate
more » ... ards zero. In this paper, Equivalent Retinotopical Sampling(ERS), which is an extension of RS, is proposed. ERS is reformulated RS by introducing an amount of information from each sampled point. The second method is an extension of discriminant function trained by SVMs for object recognition in an image. The discriminant function is formulated as an analytical function of the object position and the object size in an image. The extension is introducing ERS to SVMs.
doi:10.1007/978-3-540-45226-3_22 fatcat:u7iixlj3ybcr5glwqlslvx76e4