Toshiba Cambridge Research Laboratory
Cambridge Research Laboratory > Computer Vision

Computer Vision

CRL’s Computer Vision Group (CVG) conducts cutting-edge research in 3D computer vision and machine learning with the aim to improve the ability of software to understand and interpret visual data. In our work we support Toshiba’s overall mission to create modern technology with a positive impact on society. Our main focus is on fundamental and academically oriented research, and consequently we also have ongoing collaborations with top UK universities.

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Research

 

Online Structure from Motion

The problem of online structure from motion is also known as simultaneous localisation and mapping. It involves a system that estimates a sensor’s pose and the structure of the environment in real-time. We develop methods for visual odometry, loop closure detection and pose graph optimization.

 

Visual Text-to-Speech

In collaboration with CRL’s Speech Technology Group we produced a complete system for expressive visual text-to-speech. Our system is able to producing expressive verbal and visual output in the form of a 'talking head', given an input text and a set of continuous expression weights.

 

Optimization Methods

Energy-minimization methods are ubiquitous in computer vision and related fields. In our work we design efficient low-level optimization strategies at pixel level, and robust optimization methods for 3D vision problems with high complexity.

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Publications

pOSE: Pseudo Object Space Error for Initialization-Free Bundle Adjustment
J. H. Hong and C. Zach
CVPR, in print
Generalized fusion moves for continuous label optimization
C. Zach
CVIU, in print
ContextNet: Exploring Context and Detail for Semantic Segmentation in Real-time
R. P. K. Poudel, U. Bonde, S. Liwicki and C. Zach
arXiv, May 2018 arXiv free
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
P. Purkait and C. Zach
WACV, March 2018 arXiv
SPP-Net: Deep Absolute Pose Regression with Synthetic Views
P. Purkait, C. Zhao and C. Zach
arXiv, December 2017 arXiv free

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