Seiko Epson Corporation

Lihui Chen

Yang Yang

Guoyi Fu

Eunice Poon

Ian Clarke

Mikhail Brusnitsyn

Arash Abadpour

Ivo Moravec

Yury Yakubovich

Dan Rico

An adequate solution for computer vision applications is arrived at more efficiently and, with more automation, enables users with limited or no special image processing and pattern recognition knowledge to create reliable vision systems for their applications. Computer rendering of CAD models is used to automate the dataset acquisition process and labeling process. In order to speed up the training data preparation while maintaining the data quality, a number of processed samples are generated from one or a few seed images.

Method and apparatus for improved training of object detecting system

The present invention uses dynamic grouping to divide up training samples to train different classification nodes. At the beginning of the training, all samples are in the same group. A clustering process is applied in the feature space of the selected feature vectors with cluster indexes accumulated. The average of all the accumulated cluster indexes is used as the threshold for splitting the samples into two groups. When the splitting criterion is met, samples are split into two groups based on their similarity in the feature space.

Method of automatically training a classifier hierarchy by dynamic grouping the training samples

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Thornhill, Canada

Lihui Chen