H
Hao Feng
Researcher at Beihang University
Publications - 18
Citations - 419
Hao Feng is an academic researcher from Beihang University. The author has contributed to research in topics: Image segmentation & Visual Word. The author has an hindex of 6, co-authored 18 publications receiving 358 citations. Previous affiliations of Hao Feng include Peking University.
Papers
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Journal ArticleDOI
Automatic Fastener Classification and Defect Detection in Vision-Based Railway Inspection Systems
TL;DR: An automatic visual inspection system for detecting partially worn and completely missing fasteners using probabilistic topic model that is able to simultaneously model diverse types of fasteners with different orientations and illumination conditions using unlabeled data is proposed.
Proceedings ArticleDOI
An architecture of optimised SIFT feature detection for an FPGA implementation of an image matcher
TL;DR: Compared with the existing SIFT FPGA implementation, which requires 33 milliseconds for an image of 320×240 pixels, a significant improvement has been achieved for the proposed architecture.
Proceedings ArticleDOI
An efficient hardware architecture of the optimised SIFT descriptor generation
TL;DR: This paper proposes an efficient hardware architecture based on the polar sampled descriptor, which has the highest processing speed for descriptor generation, compared with other existing architectures.
Proceedings ArticleDOI
SIFT-based Elastic sparse coding for image retrieval
TL;DR: Experimental results on Coil20 dataset demonstrate the consistent superiority of the proposed method over the state-of-the-art algorithms including original SIFT matching, conventional BoF strategy and BoF model based on l1-norm sparse coding.
Proceedings ArticleDOI
Pathological Image Retrieval for Breast Cancer with pLSA Model
TL;DR: A novel pathological image retrieval approach based on probabilistic latent semantic analysis (pLSA) model, which utilizes SIFT features after visual saliency detection, and block Gabor features for the construction of two semantic codebooks, which not only can characterize the salient local invariant features and texture information under different scales and orientations in the pathological images, but also consider the high-level semantic features.