Robust Feature Rectification of Pretrained Vision Models for Object Recognition

A novel Local feature descriptor using the Mercator projection for 3D object recognition

Point cloud processing is a rapidly growing research area of computer vision. Introducing of cheap range sensors has made a great interest in the point cloud processing and 3D object recognition. 3D object recognition methods can be divided into two categories: global and local feature-based methods. Global features describe the entire model shape whereas local features encode the neighborhood ...

Robust Feature Detection for 3D Object Recognition and Matching

Salient surface features play a central role in tasks related to 3D object recognition and matching. There is a large body of psychophysical evidence demonstrating the perceptual signiicance of surface features such as local minima of principal curvatures in the decomposition of objects into a hierarchy of parts. Many recognition strategies employed in machine vision also directly use features ...

Feature Extraction from Vrml Models for View-based Object Recognition

View-based object recognition offers approaches to recognize three-dimensional objects from arbitrary two-dimensional views. In this paper we suggest different simple feature extraction methods based on three-dimensional object models. These are suited for both generating symbolic object descriptions and comparison with shape features extracted from images. We present a concept for improving vi...

Hierarchical feed forward models for robust object recognition

Multiple Feature Integration for Robust Object Localization

This paper presents a methodology for localization of manmade objects in complex scenes by learning multiple feature models in images. The methodology is based on a modular structure consisting of multiple classifiers, each of which solves the problem independently based on its input observations. Each classifier module is trained to detect manmade object regions and a higher order decision int...