J
Jamie Sherrah
Researcher at Defence Science and Technology Organisation
Publications - 36
Citations - 1782
Jamie Sherrah is an academic researcher from Defence Science and Technology Organisation. The author has contributed to research in topics: Facial recognition system & Feature extraction. The author has an hindex of 18, co-authored 33 publications receiving 1616 citations. Previous affiliations of Jamie Sherrah include Defence Science and Technology Organization & Australian Department of Defence.
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Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery
TL;DR: To make better use of image features, a pre-trained CNN is fine-tuned on remote sensing data in a hybrid network context, resulting in superior results compared to a network trained from scratch.
Journal ArticleDOI
3D symmetry detection using the extended Gaussian image
Changming Sun,Jamie Sherrah +1 more
TL;DR: Simulated and real images have been tested in a variety of formats, and the results show that the symmetry can be determined using the Gaussian image.
Proceedings ArticleDOI
Effective semantic pixel labelling with convolutional networks and Conditional Random Fields
TL;DR: An effective semantic pixel labelling using CNN features, hand-crafted features and Conditional Random Fields (CRFs) is proposed and applied to the ISPRS 2D semantic labelling challenge dataset with competitive classification accuracy.
Journal ArticleDOI
Support vector machine based multi-view face detection and recognition
TL;DR: A novel algorithm for face detection is developed by combining the Eigenface and SVM methods which performs almost as fast as theEigenface method but with a significant improved speed.
Journal ArticleDOI
Semantic Labeling of Aerial and Satellite Imagery
TL;DR: This paper proposes an effective approach to semantic pixel labeling of aerial and satellite imagery using both CNN features and hand-crafted features which outperforms all existing algorithms on the International Society of Photogrammetry and Remote Sensing two-dimensional Semantic Labeling Challenge dataset.