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RISS 인기검색어
- 검색키워드
- 저자 : Jiangfeng Zheng (검색결과 4 건)
- 무료
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- 유료
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Action Recognition Using Polyhedron Neighborhood Features
Jiangfeng Yang,Zheng Ma 보안공학연구지원센터 2015 International Journal of Multimedia and Ubiquitous Vol.10 No.1
To utilize the geometry structure information and similarity information within the neighborhood surrounding a spatio-temporal interest point for human action recognition task, we employ the axes of a regular polyhedron as a reference locating system, and build a novel local feature named polyhedron neighborhood feature (PNF). Then, to reduce quantization error in the coding stage, locality-constrained linear coding method is used to encode the obtained PNFs. Next, multi-temporal-scale PNFs (MPNFs) are created for handling the problem of various action speeds. In classification, support vector machine (SVM) based on linear kernel is used as classifier taking time consumption into account. The experiments on the KTH and UCF sports datasets show that the recognition system based on PNFs achieves better performance than the competing local spatio-temporal feature-based human action recognition methods.
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Action Recognition Based on Multi-scale Oriented Neighborhood Features
Jiangfeng Yang,Zheng Ma,Mei Xie 보안공학연구지원센터(IJSIP) 2015 International Journal of Signal Processing, Image Vol.8 No.1
The spatio-temporal (ST) position information between local features plays an important role in action recognition task. To use the information, neighborhood-based features are built for describing local ST information around ST interest points. However, traditional methods of constructing neighborhood, such as sub-ST volumetric method and nearest-neighbor-based neighborhood method, ignore the orientation information of neighborhood. To make the neighborhood-based features more discriminative, we construct a novel, oriented neighborhood by imposing weights on the distance components. Specifically, in our scheme, firstly, local features are produced, and encoded by locality-constrained linear coding (LLC). Then, oriented neighborhoods are constructed by imposing weights on the distance components between features, and obtain single-scale oriented neighborhood features (SONFs). Next, multi-scale oriented neighborhood features (MONFs) are formed by concatenating SONFs. As a result, action video sequences are represented as a collection of MONFs. Finally, locality-constrained group sparse representation (LGSR) is used as classifier upon MONFs. Experimental results on the KTH and UCF Sports datasets show that our method achieves better performance than the competing local ST feature-based human action recognition methods.
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Action Recognition Based on Spatio-temporal Log-Euclidean Covariance Matrix
Shilei Cheng,Jiangfeng Yang,Zheng Ma,Mei Xie 보안공학연구지원센터 2016 International Journal of Signal Processing, Image Vol.9 No.2
In this paper, we handle the problem of human action recognition by combining covariance matrices as local spatio-temporal (ST) descriptors and local ST features extracted densely from action video. Unlike traditional methods that separately utilizing gradient-based feature and optical flow-based feature, we use covariance matrix to fuse the two types of feature. Since covariance matrices are Symmetric Positive Definite (SPD) matrices, which form a special type of Riemannian manifold. To measure the distance of SPDs while avoid computing the geodesic distance between them, covariance features are transformed to log-Euclidean covariance matrices (LECM) by matrix logarithm operation. After encoding LECM by Locality-constrained Linear Coding method, in order to provide position information to ST-LECM features, spatial pyramid is used to partition the video frames, and the average-pooling-on-absolute-value function is implemented over each sub-frames. Finally, non-linear support vector machine is used as classifier. Experiments on public human action datasets show that the proposed method obtains great improvements in recognition accuracy, in comparison to several state-of-the-art methods.
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Roasting and leaching process of iron sulfate to separate zinc and iron from blast furnace dust
Ruimeng Shi,Hao Wu,Huan Liu,Bixia Wang,Yuan She,Chong Zou,Jiangfeng Zheng,Qi Gao 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.5
The physical phase analysis and thermodynamic calculations of blast furnace dust were performed using Xraydiffraction fluorescence spectrometer, X-ray diffractometer, scanning electron microscope, energy spectrometer, andFactsage software. The leaching pattern and mechanism of zinc elements were studied by a roasting-leaching method. The results showed that the conversion of zinc ferrite to zinc sulfate could be realized when the roasting temperaturerange was 500-730 oC, which was convenient for zinc leaching. A better roasting condition could be obtained when theroasting temperature was 600 oC, roasting time was 60 min, and molar ratio of ferric sulfate was 1.2 : 1. Under theseconditions, the zinc and iron leaching rates were 84.57% and 24.51%, respectively, at a sulfuric acid concentration of110 gL1, liquid-solid ratio of 10 : 1 mLg1, leaching time of 60min, stirring speed of 400 rpm, and leaching temperatureof 80 oC. The leaching process of zinc from blast furnace dust sulfate roasting products agreed with the unreacted coremodel, and internal diffusion was the restrictive step. The kinetic equation of the leaching process was 12R/3(1R)2/3=0.47t, the apparent activation energy of the leaching reaction was 17.4 kJmol1, and the reaction order was 1.908.
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