Deep Learning, GPU Parallelism, Cloud-based Computing


Intogral Limited harnesses cutting-edge research into GPU image processing algorithms, enabling real-time solutions with unprecedented accuracy to problems that otherwise can be only poorly approximated and/or require huge computational time. With many of our customers relying heavily on automation and high-throughput, both speed and accuracy are essential. Moreover, Intogral Limited maintains strong links with Durham University’s Image Informatics research group allowing Intogral Limited to maintain a close connection to current and future state-of-the-art research and cutting-edge solutions.

Selected Publications

1. Chris G. Willcocks, P. T. Jackson, C. J. Nelson, A. Nasrulloh, and B. Obara, “Interactive GPU Active Contours for Segmenting Inhomogeneous Objects,” Journal of Real-time Image Processing, Dec. 2017, ISSN: 1861-8200, DOI: 10.1007/s11554-017-0740-1

2. Chris G. Willcocks, P. T. Jackson, C. J. Nelson, and B. Obara, “Extracting 3D Parametric Curves from 2D Images of Helical Objects,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 9, PP. 1757–1769, Sep. 2017, ISSN: 0162-8828. DOI: 10.1109/TPAMI.2016.2613866.

3. S. Ackay, M. Kundegorski, Chris G. Willcocks, and T. P. Breckon, “On Using Deep Convolutional Neural Network Architectures for Automated Object Detection and Classification within X-ray Baggage Security Imagery,” IEEE Transactions on Information Forensics and Security, Oct. 2017

4. A. V. Nasrulloh, C. G. Willcocks, P. T. G. Jackson, C. Geenen, M. S. Habib, D. H. W. Steel, and B. Obara, “Multi-scale Segmentation and Surface Fitting for Measuring 3D Macular Holes,” IEEE Transactions on Medical Imaging, vol. PP, no. 99, PP. 1–1, 2017, ISSN: 0278-0062. DOI: 10.1109/TMI.2017.2767908