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10th IEEE International Conference on Intelligent Systems, IS 2020

作者： Wang, Zifei Huang, Xiaolin Yang, Jie Kasabov, Nikola Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland New Zealand

ISBN: (纸本)9781728154565

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has become an important research area of machine learning. It has been known that many state-of-the-art DNNs suffer the risk of universal adversarial perturbations, which are image-agnostic and able to lead misclassifications with high probability. In this paper, we propose a novel method to create such universal adversarial perturbations. Our approach is the first to generate universal perturbations by attacking the attention heat maps with the interpretation method, Layer-wise Relevance Propagation. It is demonstrated that our method achieves high fooling ratios on image classification DNNs pre-trained by imageNet dataset. Moreover, our attack shows good transferability across different DNNs. © 2020 IEEE.

关键词： Deep neural networks

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Unsupervised Difference Learning for Noisy Rigid image Alignment

arXiv

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arXiv 2022年

作者： Chen, Yu-Xuan Feng, Dagan Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai 200240 China School of Computer Science University of Sydney Sydney2006 Australia

Rigid image alignment is a fundamental task in computer vision, while the traditional algorithms are either too sensitive to noise or time-consuming. Recent unsupervised image alignment methods developed based on spatial transformer networks show an improved performance on clean images but will not achieve satisfactory performance on noisy images due to its heavy reliance on pixel value comparations. To handle such challenging applications, we report a new unsupervised difference learning (UDL) strategy and apply it to rigid image alignment. UDL exploits the quantitative properties of regression tasks and converts the original unsupervised problem to pseudo supervised problem. Under the new UDL-based image alignment pipeline, rotation can be accurately estimated on both clean and noisy images and translations can then be easily solved. Experimental results on both nature and cryo-EM images demonstrate the efficacy of our UDL-based unsupervised rigid image alignment method. Copyright © 2022, The Authors. All rights reserved.

关键词： image enhancement

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All-Weather Real-Time Ore Detection Method Based on Near-Infrared Structured Light and Zero-Crossing Points Characteristics

SSRN

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SSRN 2023年

作者： Tian, Keyun Hong, Hanyu Zhang, Xiuhua Peng, Cong Wu, Yuanzhe Zhu, Cheng Hubei Provincial Key Laboratory of Optical Information and Pattern Recognition Wuhan Institute of Technology Wuhan430205 China Hubei Provincial Video Image and HD Projection Engineering Technology Research Center Wuhan Institute of Technology Wuhan430205 China School of Electrical Information Wuhan Institute of Technology Wuhan430205 China School of Mechanical and Electrical Engineering Wuhan Institute of Technology Wuhan430205 China

To address the low efficiency of ore detection methods caused by the complex lighting environment of mining sites, an all-weather real-time ore detection method based on near-infrared structured light and zero-crossing points characteristics was proposed. Firstly, an image acquisition method based on the physical model of infrared radiation is proposed to avoid structured light imaging being disturbed by a strong sunlight environment;Then, the centerline of the light bar is extracted quickly and accurately based on the zero-crossing point characteristics;Finally, the light bar neighborhood splicing detection method was proposed to realize the real-time detection and 3D measurement of the target ore. The experimental results show that the proposed method can effectively resist strong sunlight interference and faster detection speed, the average extraction time of light bar centerline per frame is below 1.8ms, and the error rate of ore size detection is less than 5%. © 2023, The Authors. All rights reserved.

关键词： Infrared radiation

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Discriminative Asymmetric Learning for Efficient Surgical Instrument Parsing

Discriminative Asymmetric Learning for Efficient Surgical In...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaqi Liu Yu Qiao Jie Yang Guang-Zhong Yang Yun Gu Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China

Semantic segmentation of surgical instruments provides essential priors for autonomous surgery. This task is however challenging since the fine-structure of surgical instruments requires the accurate segmentation of detailed regions in images. As the visual guidance for autonomous surgery, the algorithm should also be real-time and friendly to embedded systems. In this paper, a discriminative asymmetric learning framework is proposed to balance the efficiency and effectiveness of surgical instrument segmentation. Two convolutional neural networks with specific designs are deployed to extract the detail and semantic features of instruments. To reduce the redundancy of visual representation, the aggregator-discriminator mechanism is proposed to distinguish the features learned from different levels. Experiments demonstrate that the proposed method contributes to competitive segmentation accuracy and a higher efficiency compared to existing methods.

关键词： image segmentation Visualization Embedded systems Instruments Semantics Redundancy Surgery

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Semi-Supervised Skin Lesion Segmentation with Learning Model Confidence

Semi-Supervised Skin Lesion Segmentation with Learning Model...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Zhiqiang Xie Enmei Tu Hao Zheng Yun Gu Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728176055;9781728176062

Segmentation of skin lesions is important for disease diagnoses and treatment planning. Over the years, semi-supervised methods using pseudo labels have boosted the segmentation performance with limited labeled data and abundant unlabeled data. However, the unreliable targets in pseudo labels might lead to meaningless guidance for unlabeled data. In this paper, to solve this issue, we propose a novel confidence aware semi-supervised learning method based on a mean teacher scheme. Concretely, we design a confidence module to predict the model confidence guided by the True Class Probability. Then in the mean teacher framework, the student model gradually learns trustworthy targets from teacher model. To further improve the segmentation quality, we fine-tune the student model with reliable content in pseudo labels. We conduct extensive experiments on 2018 ISIC skin lesion segmentation dataset and our method outperforms other state-of-the-art semi-supervised approaches.

关键词： Conferences Semisupervised learning Signal processing Predictive models Skin Planning Lesions

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Depth Prior-Based Stable Tensor Decomposition for Video Snow Removal

SSRN

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SSRN 2023年

作者： Li, Yongji Wang, Luping Jia, Zhenhong Yang, Jie Kasabov, Nikola School of Electronics and Communication Engineering Sun Yat-sen University Guangzhou510275 China College of Information Science and Engineering Xinjiang University Urumqi830046 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200400 China Knowledge Engineering and Discovery Research Institute Auckland University of Technology Auckland1020 New Zealand

Heavy snow seriously degrades the performance of outdoor computer vision systems. Near- and far-field snowflakes in heavy snow videos exhibit distinctly disparate physical properties. To address this issue, this research proposes a video desnowing model that utilizes stable tensor decomposition with snow depth prior information. Initially, the depth of snowflakes is transformed by their speeds calculated from the optical flow field of snowfall. Next, the noise level in backgrounds is available by dense snow. In contrast to previous tensor decomposition (TD), the introduction of a noise term enhances the stability of the depth prior-based stable tensor decomposition (DP-STD) in addressing heavy snowfall. This leads to more efficient and accurate restoration of underlying nonsnow structure. Inspired by biomimicry, the snow on moving objects (MOs) is removed by the adaptive mimesis region of interest (AM-ROI). Both synthetic and real snowfall experimental results show that our proposed desnowing model is more effective than the current SOTA algorithm in removing heavy snow. © 2023, The Authors. All rights reserved.

关键词： Snow

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LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to Iteratively Refine the Segmentation of Thyroid Nodule in Ultrasound images 23rd

LRTHR-Net: A Low-Resolution-to-High-Resolution Framework to ...

引用

Anatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR images Challenge, ABCs 2020, Learn2Reg Challenge, L2R 2020 and Thyroid Nodule Segmentation and Classification in Ultrasound images Challenge, TN-SCUI 2020 held in conjunction with 23rd International Conference on Medical image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Chen, Huai Song, Shaoli Wang, Xiuying Wang, Renzhen Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China Department of Nuclear Medicine Fudan University Shanghai Cancer Center Shanghai China School of Computer Science The University of Sydney Sydney Australia School of Mathematics and Statistics Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9783030718268

The thyroid nodule is quickly increasing worldwide and the thyroid ultrasound is the key tool for the diagnosis of it. For the subtle difference between malignant and benign nodules, segmenting lesions is the crucial preliminary step for diagnosis. In this paper, we propose a low-resolution-to-high-resolution segmentation framework for TN-SCUI2020 challenge to alleviate the workload of clinicians and improve the efficiency of diagnosis. Specifically speaking, in order to integrate multi-scale information, several low-resolution segmenting results are obtained firstly and combined with a high-resolution image to refine them and obtain high-resolution results. Secondly, iterative-transfer is proposed to effectively initialize network based on previous trained one on small-scale images. Finally, ensemble refinement is introduced to utilize multiple models to refine the segmentation again. Experimental results showed the effectiveness of the proposed framework. And we won the 2nd place in the segmentation task of TN-SCUI2020. © 2021, Springer Nature Switzerland AG.

关键词： Ultrasonics

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Discovering the nuclear localization signal universe through a deep learning model with interpretable attention units

引用

patterns 2025年

作者： Li, Yi-Fan Pan, Xiaoyong Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS detection, NLSExplorer achieves superior performance with greater than 10% improvement in the F1 score compared with existing methods on benchmark datasets and highlights other nuclear transport segments. We applied NLSExplorer to the nucleus-localized proteins in the Swiss-Prot database to extract valuable segments. A comprehensive analysis of these segments revealed a potential NLS landscape and uncovered features of nuclear transport segments across 416 species. This study introduces a powerful tool for exploring the NLS universe and provides a versatile network that can efficiently detect characteristic domains and motifs. © 2025 The Author(s)

关键词： Deep learning

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A competition-based image saliency model 6

A competition-based image saliency model

引用

Optoelectronic Imaging and Multimedia Technology VI 2019

作者： Li, Yang Mou, Xuanqin Institute of Image Processing and Pattern Recognition Xian Jiaotong University China

ISBN: (数字)9781510630925

ISBN: (纸本)9781510630918

Competition for visual representation is an important mechanism for selective visual attention. The traditional global distinctiveness based saliency models usually compute the distinctiveness to measure saliency via comparing the difference of image patches in various spaces. In this paper, we propose to use an improved neural competition model to replace the comparison. The pairwise competition responses for a patch to all of the other patches are summed up to represent the distinctiveness of that patch. Particularly, the competition response is computed by a neural competition model with the dissimilarity bias and the gradient based feature inputs. Experimental results validate that the proposed model presents high effectiveness in saliency detection by outperforming nine state-of-the-art models. © 2019 SPIE.

关键词： Behavioral research

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Few-Shot Classification with Task-Adaptive Semantic Feature Learning

SSRN

引用

SSRN 2022年

作者： Pan, Mei-Hong Xin, Hong-Yi Xia, Chun-Qiu Shen, Hong-Bin Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China University of Michigan Shanghai Jiao Tong University Joint Institute Shanghai Jiaotong University Shanghai200240 China

Few-shot classification aims to learn a classifier that categorizes objects of unseen classes with limited samples. One general approach is to mine as much information as possible from limited samples. This can be achieved by incorporating data from multiple modals. However, existing multi-modality methods only use additional modality in support samples while adhering to a single modal in query samples. Such approach could lead to information imbalance between support and query samples, which confounds model generalization from support to query samples. Towards this problem, we propose a task-adaptive semantic feature learning mechanism to incorporate semantic features for both support and query samples. The semantic feature learner is trained episodic-wisely by regressing from the feature vectors of the support samples. It is utilized to predict semantic features for the query samples. Such method maintains a consistent training scheme between support and query samples and enables direct model transfer from support to query data, which significantly improves model generalization. We conduct extensive experiments on four benchmarks in both inductive and tranductive settings. Results show that the proposed TasNet outperforms state-of-the-art methods with an improvement of 1% to 5% in classification accuracy, demonstrating the superiority of our method. The exhaustive ablation studies further validate the effectiveness of our framework. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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