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Hanhe Lin

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affiliation: Robert Gordon University, Aberdeen, UK
affiliation (former): University of Konstanz, Department of Computer and Information Science, Germany
affiliation (former): University of Otago, Department of Information Science, Dunedin, New Zealand

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2020 – today

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2024
[j14]
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Rui Guo, Xu Tian, Hanhe Lin, Stephen J. McKenna, Hong-Dong Li, Fei Guo, Jin Liu:
Graph-Based Fusion of Imaging, Genetic and Clinical Data for Degenerative Disease Diagnosis. IEEE ACM Trans. Comput. Biol. Bioinform. 21(1): 57-68 (2024)
[j13]
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Guanghui Yue, Yuanyan Li, Shangjie Wu, Bin Jiang, Tianwei Zhou, Weiqing Yan, Hanhe Lin, Tianfu Wang:
Dual-Domain Feature Interaction Network for Automatic Colorectal Polyp Segmentation. IEEE Trans. Instrum. Meas. 73: 1-12 (2024)
[j12]
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Jianxun Lou, Hanhe Lin, Philippa Young, Richard White, Zelei Yang, Susan Cheng Shelmerdine, David Marshall, Emiliano Spezi, Marco Palombo, Hantao Liu:
Predicting Radiologists' Gaze With Computational Saliency Models in Mammogram Reading. IEEE Trans. Multim. 26: 256-269 (2024)
[j11]
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Shaolin Su, Hanhe Lin, Vlad Hosu, Oliver Wiedemann, Jinqiu Sun, Yu Zhu, Hantao Liu, Yanning Zhang, Dietmar Saupe:
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model. IEEE Trans. Multim. 26: 2671-2685 (2024)
[j10]
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Sam Slade, Li Zhang, Haoqian Huang, Houshyar Asadi, Chee Peng Lim, Yonghong Yu, Dezong Zhao, Hanhe Lin, Rong Gao:
Neural Inference Search for Multiloss Segmentation Models. IEEE Trans. Neural Networks Learn. Syst. 35(11): 15113-15127 (2024)
[c30]
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Jin Liu, Dejiao Zeng, Ludi Li, Hanhe Lin, Xu Tian:
Source-Free Domain Adaptation for Millimeter Wave Radar Based Human Activity Recognition. ICASSP 2024: 7120-7124
2023
[j9]
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Xin Zhao, Jianxun Lou, Xinbo Wu, Yingying Wu, Lucie Lévêque, Xiaochang Liu, Pengfei Guo, Yipeng Qin, Hanhe Lin, Dietmar Saupe, Hantao Liu:
CUDAS: Distortion-Aware Saliency Benchmark. IEEE Access 11: 58025-58036 (2023)
[c29]
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Junbin Mao, Hanhe Lin, Xu Tian, Yi Pan, Jin Liu:
FedGST: Federated Graph Spatio-Temporal Framework for Brain Functional Disease Prediction. BIBM 2023: 1356-1361
[c28]
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Yijun Yan, Yinhe Li, Hanhe Lin, Md. Mostafa Kamal Sarker, Jinchang Ren, John McCall:
Underwater Object Detection for Smooth and Autonomous Operations of Naval Missions: A Pilot Dataset. BICS 2023: 113-122
[c27]
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Guangan Chen, Hanhe Lin, Oliver Wiedemann, Dietmar Saupe:
Localization of Just Noticeable Difference for Image Compression. QoMEX 2023: 61-66
[i16]
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  - journals/corr/abs-2303-03388
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Junbin Mao, Jin Liu, Hanhe Lin, Hulin Kuang, Shirui Pan, Yi Pan:
Multi-modal Multi-kernel Graph Learning for Autism Prediction and Biomarker Discovery. CoRR abs/2303.03388 (2023)
[i15]
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  - journals/corr/abs-2306-07678
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Guangan Chen, Hanhe Lin, Oliver Wiedemann, Dietmar Saupe:
Localization of Just Noticeable Difference for Image Compression. CoRR abs/2306.07678 (2023)
2022
[j8]
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Jianxun Lou, Hanhe Lin, David Marshall, Dietmar Saupe, Hantao Liu:
TranSalNet: Towards perceptually relevant visual saliency prediction. Neurocomputing 494: 455-467 (2022)
[j7]
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  - journals/tcsv/LinCJHRHS22
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Hanhe Lin, Guangan Chen, Mohsen Jenadeleh, Vlad Hosu, Ulf-Dietrich Reips, Raouf Hamzaoui, Dietmar Saupe:
Large-Scale Crowdsourced Subjective Assessment of Picturewise Just Noticeable Difference. IEEE Trans. Circuits Syst. Video Technol. 32(9): 5859-5873 (2022)
[c26]
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Jianxun Lou, Hanhe Lin, David Marshall, Richard White, Young Yang, Susan Cheng Shelmerdine, Hantao Liu:
Predicting Radiologist Attention During Mammogram Reading with Deep and Shallow High-Resolution Encoding. ICIP 2022: 961-965
[c25]
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Hanhe Lin, Hui Men, Yijun Yan, Jinchang Ren, Dietmar Saupe:
Crowdsourced Quality Assessment of Enhanced Underwater Images - a Pilot Study. QoMEX 2022: 1-4
[i14]
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  - journals/corr/abs-2207-04904
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Shaolin Su, Hanhe Lin, Vlad Hosu, Oliver Wiedemann, Jinqiu Sun, Yu Zhu, Hantao Liu, Yanning Zhang, Dietmar Saupe:
Going the Extra Mile in Face Image Quality Assessment: A Novel Database and Model. CoRR abs/2207.04904 (2022)
2021
[j6]
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  - journals/access/Gotz-HahnHLS21
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Franz Götz-Hahn, Vlad Hosu, Hanhe Lin, Dietmar Saupe:
KonVid-150k: A Dataset for No-Reference Video Quality Assessment of Videos in-the-Wild. IEEE Access 9: 72139-72160 (2021)
[j5]
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Hui Men, Hanhe Lin, Mohsen Jenadeleh, Dietmar Saupe:
Subjective Image Quality Assessment With Boosted Triplet Comparisons. IEEE Access 9: 138939-138975 (2021)
[c24]
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Shaolin Su, Vlad Hosu, Hanhe Lin, Yanning Zhang, Dietmar Saupe:
KonIQ++: Boosting No-Reference Image Quality Assessment in the Wild by Jointly Predicting Image Quality and Defects. BMVC 2021: 330
[c23]
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Hanhe Lin, Guangan Chen, Felix Wilhelm Siebert:
Positional Encoding: Improving Class-Imbalanced Motorcycle Helmet use Classification. ICIP 2021: 1194-1198
[i13]
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  - journals/corr/abs-2108-00201
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Hui Men, Hanhe Lin, Mohsen Jenadeleh, Dietmar Saupe:
Subjective Image Quality Assessment with Boosted Triplet Comparisons. CoRR abs/2108.00201 (2021)
[i12]
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  - journals/corr/abs-2110-03593
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Jianxun Lou, Hanhe Lin, David Marshall, Dietmar Saupe, Hantao Liu:
TranSalNet: Visual saliency prediction using transformers. CoRR abs/2110.03593 (2021)
2020
[j4]
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  - journals/access/LinDAS20
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Hanhe Lin, Jeremiah D. Deng, Deike Albers, Felix Wilhelm Siebert:
Helmet Use Detection of Tracked Motorcycles Using CNN-Based Multi-Task Learning. IEEE Access 8: 162073-162084 (2020)
[j3]
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  - journals/que/LinHF0MHS20
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Hanhe Lin, Vlad Hosu, Chunling Fan, Yun Zhang, Yuchen Mu, Raouf Hamzaoui, Dietmar Saupe:
SUR-FeatNet: Predicting the satisfied user ratio curve for image compression with deep feature learning. Qual. User Exp. 5(1) (2020)
[j2]
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  - journals/que/MenHLBS20
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Hui Men, Vlad Hosu, Hanhe Lin, Andrés Bruhn, Dietmar Saupe:
Subjective annotation for a frame interpolation benchmark using artefact amplification. Qual. User Exp. 5(1) (2020)
[j1]
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  - journals/tip/HosuLSS20
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Vlad Hosu, Hanhe Lin, Tamás Szirányi, Dietmar Saupe:
KonIQ-10k: An Ecologically Valid Database for Deep Learning of Blind Image Quality Assessment. IEEE Trans. Image Process. 29: 4041-4056 (2020)
[c22]
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  - conf/accv/RoziereTHLRZT20
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Baptiste Rozière, Fabien Teytaud, Vlad Hosu, Hanhe Lin, Jérémy Rapin, Mariia Zameshina, Olivier Teytaud:
EvolGAN: Evolutionary Generative Adversarial Networks. ACCV (4) 2020: 679-694
[c21]
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  - conf/gecco/RoziereRHLRTC20
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Baptiste Rozière, Nathanaël Carraz Rakotonirina, Vlad Hosu, Hanhe Lin, Andry Rasoanaivo, Olivier Teytaud, Camille Couprie:
Evolutionary super-resolution. GECCO Companion 2020: 151-152
[c20]
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  - conf/icip/ZhaoLGSL20
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Xin Zhao, Hanhe Lin, Pengfei Guo, Dietmar Saupe, Hantao Liu:
Deep Learning VS. Traditional Algorithms for Saliency Prediction of Distorted Images. ICIP 2020: 156-160
[c19]
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  - conf/icmcs/LinJCRHS20
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Hanhe Lin, Mohsen Jenadeleh, Guangan Chen, Ulf-Dietrich Reips, Raouf Hamzaoui, Dietmar Saupe:
Subjective Assessment of Global Picture-Wise Just Noticeable Difference. ICME Workshops 2020: 1-6
[c18]
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  - conf/icpr/RoziereRHRLCT20
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Baptiste Rozière, Nathanaël Carraz Rakotonirina, Vlad Hosu, Andry Rasoanaivo, Hanhe Lin, Camille Couprie, Olivier Teytaud:
Tarsier: Evolving Noise Injection in Super-Resolution GANs. ICPR 2020: 7028-7035
[c17]
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  - conf/qomex/MenHLBS20
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Hui Men, Vlad Hosu, Hanhe Lin, Andrés Bruhn, Dietmar Saupe:
Visual Quality Assessment for Interpolated Slow-Motion Videos Based on a Novel Database. QoMEX 2020: 1-6
[c16]
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  - conf/qomex/WiedemannHLS20
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Oliver Wiedemann, Vlad Hosu, Hanhe Lin, Dietmar Saupe:
Foveated Video Coding for Real-Time Streaming Applications. QoMEX 2020: 1-6
[i11]
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  - journals/corr/abs-2001-02002
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Hanhe Lin, Vlad Hosu, Chunling Fan, Yun Zhang, Yuchen Mu, Raouf Hamzaoui, Dietmar Saupe:
SUR-FeatNet: Predicting the Satisfied User Ratio Curvefor Image Compression with Deep Feature Learning. CoRR abs/2001.02002 (2020)
[i10]
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  - journals/corr/abs-2001-06409
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Hui Men, Vlad Hosu, Hanhe Lin, Andrés Bruhn, Dietmar Saupe:
Subjective Annotation for a Frame Interpolation Benchmark using Artifact Amplification. CoRR abs/2001.06409 (2020)
[i9]
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  - journals/corr/abs-2001-08113
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Hanhe Lin, Vlad Hosu, Dietmar Saupe:
DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning. CoRR abs/2001.08113 (2020)
[i8]
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  - journals/corr/abs-2009-12177
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Baptiste Rozière, Nathanaël Carraz Rakotonirina, Vlad Hosu, Andry Rasoanaivo, Hanhe Lin, Camille Couprie, Olivier Teytaud:
Tarsier: Evolving Noise Injection in Super-Resolution GANs. CoRR abs/2009.12177 (2020)
[i7]
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  - journals/corr/abs-2009-13311
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Baptiste Rozière, Fabien Teytaud, Vlad Hosu, Hanhe Lin, Jérémy Rapin, Mariia Zameshina, Olivier Teytaud:
EvolGAN: Evolutionary Generative Adversarial Networks. CoRR abs/2009.13311 (2020)

2010 – 2019

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2019
[c15]
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  - conf/qomex/FanLHZJHS19
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Chunling Fan, Hanhe Lin, Vlad Hosu, Yun Zhang, Qingshan Jiang, Raouf Hamzaoui, Dietmar Saupe:
SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning. QoMEX 2019: 1-6
[c14]
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  - conf/qomex/LinHS19
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Hanhe Lin, Vlad Hosu, Dietmar Saupe:
KADID-10k: A Large-scale Artificially Distorted IQA Database. QoMEX 2019: 1-3
[c13]
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  - conf/qomex/MenLHMBS19
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Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andrés Bruhn, Dietmar Saupe:
Visual Quality Assessment for Motion Compensated Frame Interpolation. QoMEX 2019: 1-6
[i6]
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  - journals/corr/abs-1901-05362
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Hui Men, Hanhe Lin, Vlad Hosu, Daniel Maurer, Andrés Bruhn, Dietmar Saupe:
Technical Report on Visual Quality Assessment for Frame Interpolation. CoRR abs/1901.05362 (2019)
[i5]
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  - journals/corr/abs-1908-06911
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Markus Wagner, Hanhe Lin, Shujun Li, Dietmar Saupe:
Algorithm Selection for Image Quality Assessment. CoRR abs/1908.06911 (2019)
[i4]
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  - journals/corr/abs-1910-06180
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Vlad Hosu, Hanhe Lin, Tamás Szirányi, Dietmar Saupe:
KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment. CoRR abs/1910.06180 (2019)
[i3]
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  - journals/corr/abs-1910-13232
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Felix Wilhelm Siebert, Hanhe Lin:
Detecting motorcycle helmet use with deep learning. CoRR abs/1910.13232 (2019)
[i2]
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  - journals/corr/abs-1912-07966
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Franz Götz-Hahn, Vlad Hosu, Hanhe Lin, Dietmar Saupe:
No-Reference Video Quality Assessment using Multi-Level Spatially Pooled Features. CoRR abs/1912.07966 (2019)
2018
[c12]
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Vlad Hosu, Hanhe Lin, Dietmar Saupe:
Expertise screening in crowdsourcing image quality. QoMEX 2018: 1-6
[c11]
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Hui Men, Hanhe Lin, Dietmar Saupe:
Spatiotemporal Feature Combination Model for No-Reference Video Quality Assessment. QoMEX 2018: 1-3
[c10]
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Oliver Wiedemann, Vlad Hosu, Hanhe Lin, Dietmar Saupe:
Disregarding the Big Picture: Towards Local Image Quality Assessment. QoMEX 2018: 1-6
[i1]
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  - journals/corr/abs-1803-08489
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Hanhe Lin, Vlad Hosu, Dietmar Saupe:
KonIQ-10k: Towards an ecologically valid and large-scale IQA database. CoRR abs/1803.08489 (2018)
2017
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Ahmad Shahi, Brendon J. Woodford, Hanhe Lin:
Dynamic Real-Time Segmentation and Recognition of Activities Using a Multi-feature Windowing Approach. PAKDD (Workshops) 2017: 26-38
[c8]
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Vlad Hosu, Franz Hahn, Mohsen Jenadeleh, Hanhe Lin, Hui Men, Tamás Szirányi, Shujun Li, Dietmar Saupe:
The Konstanz natural video database (KoNViD-1k). QoMEX 2017: 1-6
[c7]
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Hui Men, Hanhe Lin, Dietmar Saupe:
Empirical evaluation of no-reference VQA methods on a natural video quality database. QoMEX 2017: 1-3
2016
[c6]
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  - conf/mm/LinDWS16
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford, Ahmad Shahi:
Online Weighted Clustering for Real-time Abnormal Event Detection in Video Surveillance. ACM Multimedia 2016: 536-540
[c5]
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford:
Shot Boundary Detection Using Multi-instance Incremental and Decremental One-Class Support Vector Machine. PAKDD (1) 2016: 165-176
2015
[c4]
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  - conf/icip/LinDW15
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford:
Anomaly detection in crowd scenes via online adaptive one-class support vector machines. ICIP 2015: 2434-2438
2014
[c3]
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  - conf/pricai/LinDW14
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford:
Spatial-Temporal Pyramid Matching for Crowd Scene Analysis. MLSDA@PRICAI 2014: 12
2013
[c2]
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  - conf/ausai/LinDW13
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford:
Event Detection Using Quantized Binary Code and Spatial-Temporal Locality Preserving Projections. Australasian Conference on Artificial Intelligence 2013: 123-134
2012
[c1]
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  - conf/ivcnz/LinDW12
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Hanhe Lin, Jeremiah D. Deng, Brendon J. Woodford:
Video manifold modelling: finding the right parameter settings for anomaly detection. IVCNZ 2012: 168-173

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