Selective Search for Object Recognition

Unsupervised Selective Transfer Learning for Object Recognition

We propose a novel unsupervised transfer learning framework that utilises unlabelled auxiliary data to quantify and select the most relevant transferrable knowledge for recognising a target object class from the background given very limited training target samples. Unlike existing transfer learning techniques, our method does not assume that auxiliary data are labelled, nor the relationships b...

Probabilistic Search for Object Segmentation and Recognition

The problem of searching for a model-based scene interpretation is analyzed within a probabilistic framework. Object models are formulated as generative models for range data of the scene. A new statistical criterion, the truncated object probability, is introduced to infer an optimal sequence of object hypotheses to be evaluated for their match to the data. The truncated probability is partly ...

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 ...

BONSAI: 3D object recognition using constrained search

Computer vision systems that identify and localize instances of predefined 3-D models in images offer many potential benefits to industrial and other environments. In many of these areas, solid models of the parts to be recognized already exist, and redesign of the part geometry for vision tasks should be avoided. This paper describes BONSAI, which is a model-based 3-D object recognition system...

A random-ray model for Visual Search and Object Recognition