Kabushiki Kaisha Toshiba

Pablo Alcantarilla

Roberto Cipolla

Bjorn Stenger

Frank Perbet

Oliver Woodford

Minh-Tri Pham

Sam Johnson

Riccardo Gherardi

Simon Stent

German Ros Sanchez

A source deconvolutional network is adaptively trained to perform semantic segmentation. Image data is then input to the source deconvolutional network and outputs of the S-Net are measured. The same image data and the measured outputs of the source deconvolutional network are then used to train a target deconvolutional network. The target deconvolutional network is defined by a substantially fewer numerical parameters than the source deconvolutional network.

Training constrained deconvolutional networks for road scene semantic segmentation

A method of object recognition and/or registration includes receiving a point cloud, arranging the points of the point cloud into a hierarchical search tree, and determining geometric information of the points located within a region, by identifying a highest level tree nodes where all of descendent leaf nodes are contained within the region and selecting the leaf nodes for the points where no sub-tree is entirely contained within the region, such that the points falling within the region are represented by the smallest number of nodes and performing statistical operations on the nodes representing the points in the region. The geometric information includes descriptors of features in the point cloud. The method further includes comparing the feature descriptors with a database of feature descriptors for a plurality of objects.

Image analysis method by analyzing point cloud using hierarchical search tree

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Cambridge, Bulgaria

Pablo Alcantarilla