Intelligent Reference Curation for Visual Place Recognition Via Bayesian Selective Fusion

A Selective Weighted Late Fusion for Visual Concept Recognition

Visual place recognition using Bayesian filtering with Markov chains

We present a novel idea to use Bayesian filtering in the case of place recognition. More precisely, our system combines global image characterization, Learned Vector Quantization, Markov chains and Bayesian filtering. The goal is to integrate several images seen by a robot during exploration of the environment and the dependency between them. We present our system and the new Bayesian filtering...

Visual Place Recognition for Autonomous

Visual Place Recognition for Autonomous Robots Hemant D. Tagare Department of Diagnostic Radiology and Department of Electrical Engineering Drew McDermott Hong Xiao Department of Computer Science Yale University ftagare, mcdermott,[email protected] Abstract| The problem of place recognition is central to robot map learning. A robot needs to be able to recognize when it has returned to a previou...

Fusion for Visual Recognition

In the past decade, the popularity of the Internet and digital cameras has led to a flourishing of images and videos. Surveillance videos are increasing explosively with the huge amounts of surveillance cameras. Compared with traditional datasets in computer vision, which host only thousands of images, these largescale datasets in the era of the Internet have grown beyond the wildest imaginatio...

A Bayesian approach for place recognition

This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small trainin...