S Se, D Lowe, J Little. The international Journal of robotics Research 21 (8), 735-758, 2002. 1235: 2002: Local feature view clustering for 3D object recognition. DG Lowe. Computer Vision and Pattern Recognition, 2001. CVPR 2001.

2002年8月6日 · Abstract: An object recognition system has been developed that uses a new class of local image features. The features are invariant to image scaling, translation, and rotation, and partially invariant to illumination changes and affine or 3D projection.

David Lowe is Professor Emeritus with the Computer Science Department of the University of British Columbia. From 2015-2018, David Lowe was a Senior Research Scientist with Google in the Machine Intelligence Group.

Image matching is a fundamental aspect of many problems in computer vision, including object or scene recognition, solving for 3D structure from multiple images, stereo correspon-dence, and motion tracking. This paper describes image features that have many properties that make them suitable for matching differing images of an object or scene.

David G. Lowe is a Canadian computer scientist working for Google as a senior research scientist. He was a former professor in the computer science department at the University of British Columbia and New York University .

Final verification of each match is achieved by finding a low-residual least-squares solution for the unknown model parameters. Experimental results show that robust object recognition can be achieved in cluttered partially-occluded images with a computation time of under 2 seconds.

Image keys are created that allow for local ge-ometric deformations by representing blurred image gradi-ents in multiple orientation planes and at multiple scales. The keys are used as input to a nearest-neighbor indexing method that identifies candidate object matches.

ABSTRACT: Scale Invariant Feature Transform (SIFT) algorithm is a widely used computer vision algorithm that detects and extracts local feature descriptors from images. SIFT is computationally intensive, making it infeasible for single threaded im-plementation to extract local feature descriptors for high-resolution images in real time.

David lowe is a professor in the Computer Science Department of the University of British Columbia. He is a Fellow of the Canadian Institute for Advanced Research and a member of the Scientific Advisory Board of Evolution Robotics. Affiliations: [Google].

SIFT算法由加拿大英属哥伦比亚大学教授David Lowe 于 1999 年发表于会议 ICCV ，原论文Object recognition from local scale-invariant features ， David Lowe 是唯一作者. 而广为人知的被引用更多的是2004年发表于期刊 IJCV 的完善版 Distinctive image features from scale-invariant keypoints. 谷歌学术显示SIFT 2004‘ 已被引用55841次. David Lowe 教授是一个很有商业嗅觉的学者，发明了SIFT算法后发现这可是个好东西，赶紧申请了专利 US6711293B1。 专利申请 …