Object interpretation: extending and validating object recognition

Object Recognition based on Local Steering Kernel and SVM

The proposed method is to recognize objects based on application of Local Steering Kernels (LSK) as Descriptors to the image patches. In order to represent the local properties of the images, patch is to be extracted where the variations occur in an image. To find the interest point, Wavelet based Salient Point detector is used. Local Steering Kernel is then applied to the resultant pixels, in ...

Extending Pictorial Structures for Object Recognition

The goal of this paper is to recognize various deformable objects from images. To this end we extend the class of generative probabilistic models known as pictorial structures. This class of models is particularly suited to represent articulated structures, and has previously been used by Felzenszwalb and Huttenlocher for pose estimation of humans. We extend pictorial structures in three ways: ...

Representations and Techniques for 3D Object Recognition and Scene Interpretation

Object Reading: Text Recognition for Object Recognition

We propose to use text recognition to aid in visual object class recognition. To this end we first propose a new algorithm for text detection in natural images. The proposed text detection is based on saliency cues and a context fusion step. The algorithm does not need any parameter tuning and can deal with varying imaging conditions. We evaluate three different tasks: 1. Scene text recognition...

a computational visual neuroscience model for object recognition

in this study with the inspirations from both neuroscience and computer science, a combinatorial framework for object recognition was proposed having benefited from the advantages of both biologically-inspired hmax_s architecture model for feature extraction and extreme learning machine (elm) as a classifier. hmax model is a feed-forward hierarchical structure resembling the ventral pathway in ...