Optimal Object Categorization under Application Specific Conditions

Steven Puttemans, Toon Goedemé


Day-to-day industrial computer vision applications focusing on object detection have the need of robust, fast and accurate object detection techniques. However, current state-of-the-art object categorization techniques only reach about 85% detection rate when performing in the wild detections who try to cope with as much scene and object variation as possible. However several industrial applications show many known characteristics like constant lighting, known camera position, constant background, … giving lead to several constraints on the actual algorithms. With a complete new universal object categorization framework, we want to prove the detection rate of these object categorization algorithms by exploiting the application specific knowledge which can help to reach a robust detector with detection rates of 99.9% or higher. We will use the same constraints to effectively reduce the number of false positive detections. Furthermore we will introduce an innovative active learning system based on this application specific knowledge that will drastically reduce the amount of positive and negative training samples, leading to a shorter and more effective annotation and training phase.


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Paper Citation

in Harvard Style

Puttemans S. and Goedemé T. (2014). Optimal Object Categorization under Application Specific Conditions . In Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014) ISBN Not Available, pages 25-34

in Bibtex Style

author={Steven Puttemans and Toon Goedemé},
title={Optimal Object Categorization under Application Specific Conditions},
booktitle={Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)},
isbn={Not Available},

in EndNote Style

JO - Doctoral Consortium - DCVISIGRAPP, (VISIGRAPP 2014)
TI - Optimal Object Categorization under Application Specific Conditions
SN - Not Available
AU - Puttemans S.
AU - Goedemé T.
PY - 2014
SP - 25
EP - 34
DO -