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17th Asian Conference on Computer Vision, ACCV 2024

作者： Song, Jiayan Pan, Renjie Zhou, Jun Yang, Hua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmission Shanghai200240 China

ISBN: (纸本)9789819609079

Current encoder-decoder methods for image captioning mai-nly consist of an object detection module (two-stage), or rely on big models with large-scale datasets to improve the effectiveness, which leads to increasing computation costs and cannot introduce new external knowledge. In this paper, we propose a novel end-to-end method Multi-grained Retrieval Augmentation Transformer (M-RAT) that innovatively fuses retrieved text derived from a changeable datastore with input visual feature through a Multi-modal Aligned Encoder, and introduce a specialized attention mechanism, Multi-MSA, to exploit both local and global interactions for delicate fine-grained details. Additionally, we enhance the decoder generation ability by employing low-level and high-level fused embeddings. Experiments demonstrate that M-RAT achieves comparable performance to state-of-the-art baselines with remarkable accuracy and details, as well as showing excellent domain adaptability for novel objects. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Decoding

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Mitosis detection and classification for breast cancer diagnosis: What we know and what is next

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Computers in Biology and Medicine 2025年 191卷 110057-110057页

作者： Khalil, Rafi Ullah Sajjad, Muhammad Dhahbi, Sami Bourouis, Sami Hijji, Mohammad Muhammad, Khan Digital Image Processing Lab Department of Computer Science Islamia College Peshawar Peshawar25000 Pakistan Applied College of Mahail Aseer King Khalid University Muhayil Aseer62529 Saudi Arabia Department of Information Technology College of Computers and Information Technology Taif University Taif21944 Saudi Arabia Faculty of Computers and Information Technology University of Tabuk Tabuk71491 Saudi Arabia Department of Applied AI School of Convergence College of Computing and Informatics Sungkyunkwan University Seoul03063 Korea Republic of

Breast cancer is the second most deadly malignancy in women, behind lung cancer. Despite significant improvements in medical research, breast cancer is still accurately diagnosed with histological analysis. During this procedure, pathologists examine a physical sample for the presence of mitotic cells, or dividing cells. However, the high resolution of histopathology images and the difficulty of manually detecting tiny mitotic nuclei make it particularly challenging to differentiate mitotic cells from other types of cells. Numerous studies have addressed the detection and classification of mitosis, owing to increasing capacity and developments in automated approaches. The combination of machine learning and deep learning techniques has greatly revolutionized the process of identifying mitotic cells by offering automated, precise, and efficient solutions. In the last ten years, several pioneering methods have been presented, advancing towards practical applications in clinical settings. Unlike other forms of cancer, breast cancer and gliomas are categorized according to the number of mitotic divisions. Numerous papers have been published on techniques for identifying mitosis due to easy access to datasets and open competitions. Convolutional neural networks and other deep learning architectures can precisely identify mitotic cells, significantly decreasing the amount of labor that pathologists must perform. This article examines the techniques used over the past decade to identify and classify mitotic cells in histologically stained breast cancer hematoxylin and eosin images. Furthermore, we examine the benefits of current research techniques and predict forthcoming developments in the investigation of breast cancer mitosis, specifically highlighting machine learning and deep learning. © 2025 Elsevier Ltd

关键词： Lung cancer

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Revolutionary entrapment model of uniformly distributed swarm robots in morphogenetic formation

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Defence Technology（防务技术） 2024年第1期31卷 496-509页

作者： Chen Wang Zhaohui Shi Minqiang Gu Weicheng Luo Xiaomin Zhu Zhun Fan Shantou University ShantouGuangdongChina National University of Defense Technology ChangshaChina Key Lab of Digital Signal and Image Processing of Guangdong Province China

This study proposes a method for uniformly revolving swarm robots to entrap multiple targets,which is based on a gene regulatory network,an adaptive decision mechanism,and an improved *** the gene regulatory network method,the robots can generate entrapping patterns according to the environmental input,including the positions of the targets and ***,an adaptive decision mechanism is proposed,allowing each robot to choose the most well-adapted capture point on the pattern,based on its *** robots employ an improved Vicsek-model to maneuver to the planned capture point smoothly,without colliding with other robots or *** proposed decision mechanism,combined with the improved Vicsek-model,can form a uniform entrapment shape and create a revolving effect around targets while entrapping *** study also enables swarm robots,with an adaptive pattern formation,to entrap multiple targets in complex *** robots can be deployed in the military field of unmanned aerial vehicles’(UAVs)entrapping multiple *** experiments demonstrate the feasibility and superiority of the proposed gene regulatory network method.

关键词： Swarm intelligence Revolutionary entrapment Flocking Robots Gene regulatory network Vicsek-model Entrapping multiple targets

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Separation of superimposed images with unknown motions using sparsity priors

Separation of superimposed images with unknown motions using...

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2011 IEEE Visual Communications and image processing, VCIP 2011

作者： Yan, Qing Yang, Xiaokang Xu, Yi Institute of Image Communication and Information Processing Shanghai Key Lab of Digital Media Processing and Transmission Shanghai China

ISBN: (纸本)9781457713200

When people take photos through a transparent surface, it is ubiquitous that the images are superimposed with two source images, the transmitted layer and the reflected layer. In this paper, we utilize the sparsity priors over image color distribution and image gradients, and we realize the separation of such images by exploiting the diversity (relative motions of source images) in different snapshots. Our approach can estimate the motion even when layer image is quite faint, extract layer gradients when layer intensity is unchanged, and obtain a reliable separation by considering the gradients as limitations. The effectiveness of our approach is shown in experiments on both synthetic images and real world photos. © 2011 IEEE.

关键词： Separation

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A maximum a posteriori super resolution algorithm based on multidimensional Lorentzian distribution

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2009年第12期10卷 1705-1713页

作者： Wen CHEN Xiang-zhong FANG Yan CHENG Shanghai Key Lab of Digital Media Processing and Transmissions Institute of Image Communication and Information Processing Shanghai Jiao Tong University Shanghai 200240 China College of Information Technology East China University of Political Science and Law Shanghai 201620 China

This paper presents a threshold-free maximum a posteriori (MAP) super resolution (SR) algorithm to reconstruct high resolution (HR) images with sharp edges. The joint distribution of directional edge images is modeled as a multidimensional Lorentzian (MDL) function and regarded as a new image prior. This model makes full use of gradient information to restrict the solution space and yields an edge-preserving SR algorithm. The Lorentzian parameters in the cost function are replaced with a tunable variable, and graduated nonconvexity (GNC) optimization is used to guarantee that the proposed multidimensional Lorentzian SR (MDLSR) algorithm converges to the global minimum. Simulation results show the effectiveness of the MDLSR algorithm as well as its superiority over conventional SR methods.

关键词： Edge preservation Multidimensional Lorentzian distribution (MDL) Super resolution Threshold

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Feature extraction methods for form recognition applications

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WSEAS Transactions on Information Science and Applications 2006年第3期3卷 666-671页

作者： Adams, Jae Yfantis, E.A. Curtis, Dean Pack, Trenton Digital Image Processing Lab. Department of Computer Science University of Nevada 4505 Maryland Parkway Las Vegas NV 89154 United States

The process being used to recognize a form includes extracting a wide variety of features that allow the recognizing procedure to sufficiently characterize a form. The first feature is based on the logo of the form. The logo is a distinct picture that appears on a form. The second feature is the form identification (to be called "id") which is unique to each form. The third feature is the structural lines. The fourth feature is a check box counting routine. The last feature is a word counting routine. Together, these features are utilized to extract vital characteristics that lead to a confident classification of the form. Current results within the form classification algorithm, which uses all the aforementioned features, yields a high accuracy.

关键词： Feature extraction

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CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search 32

CHASE: Robust Visual Tracking via Cell-Level Differentiable ...

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32nd British Machine Vision Conference, BMVC 2021

作者： Marvasti-Zadeh, Seyed Mojtaba Khaghani, Javad Cheng, Li Ghanei-Yakhdan, Hossein Kasaei, Shohreh Vision and Learning Lab University of Alberta Edmonton Canada Digital Image & Video Processing Lab Yazd University Yazd Iran Image Processing Lab Sharif University of Technology Tehran Iran

A strong visual object tracker nowadays relies on its well-crafted modules, which typically consist of manually-designed network architectures to deliver high-quality tracking results. Not surprisingly, the manual design process becomes a particularly challenging barrier, as it demands sufficient prior experience, enormous effort, intuition, and perhaps some good luck. Meanwhile, neural architecture search has gaining grounds in practical applications as a promising method in tackling the issue of automated search of feasible network structures. In this work, we propose a novel cell-level differentiable architecture search mechanism with early stopping to automate the network design of the tracking module, aiming to adapt backbone features to the objective of Siamese tracking networks during offline training. Besides, the proposed early stopping strategy avoids over-fitting and performance collapse problems leading to generalization improvement. The proposed approach is simple, efficient, and with no need to stack a series of modules to construct a network. Our approach is easy to be incorporated into existing trackers, which is empirically validated using different differentiable architecture search-based methods and tracking objectives. Extensive experimental evaluations demonstrate the superior performance of our approach over five commonly-used benchmarks. © 2021. The copyright of this document resides with its authors.

关键词： Network architecture

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Gait identification by sparse representation

Gait identification by sparse representation

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Gong, Minyan Xu, Yi Yang, Xiaokang Zhang, Wenjun Institute of Image Communication and Information Processing Shanghai China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai China

ISBN: (纸本)9781612841816

Gait recognition under variations of clothing and carrying condition is still a challenging task. In this paper, we present a gait identification method via sparse representation. We formulate the recognition problem as finding the coefficients of linear combination of the training samples plus an error term and discuss sparse signal representation theory that offers the solution to this problem. Based on the sparse representation computed by l1-minimization, we define a new distance metric to choose non-polluted area and propose a method for gait identification. Compared with the state-of-the-art methods on a large dataset, the proposed method achieves significant performance improvement in identification rates and it shows robustness to variations. © 2011 IEEE.

关键词： Pattern recognition

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Autocalibration for Creating 2D/3D Displays on Complex Geometric Surfaces

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Information Display 2022年第6期38卷 31-37页

作者： Arnold, Katherine Fieguth, Paul Dennison, Andrew Vision and Image Processing Lab University of Waterloo Canada Systems Design Engineering Department University of Waterloo Canada Software Solutions Development Department Christie Digital Systems

Geometric calibration methods tend to rely on precalibrated cameras or real calibration targets. Here we explore the chal-lenge of achieving a sufficiently accurate projector system calibration for complex projection mapping tasks that can rely on neither precalibrated cameras nor calibration targets. Virtual scene models can take the place of a real calibration target for the purpose of providing scene information that is used to refine geometric estimates. Refinement of the display parameters aims to reduce the reprojection error, or the error between the theoretical coordinate (generated from the projection of the scene estimate coordinate by the estimated parameters) and the actual coordinate (obtained from pixel cor-respondences). For Christie installations, the final reprojection error of each pixel should be less than 1 pixel. Mystique was not always so capable. Christie’s research department, Christie labs, has a history of creating solutions for new projection concepts. Part of that strategy has been a long partnership with the University of Waterloo, particularly the Vision and image processing (VIP) lab in Systems Design Engineering. Graduate students study computer vision and machine learning—topics related to problems often at the heart of techniques—which can be used to bring new ideas, meth-ods, and solutions to Mystique and, by extension, to Christie’s customers. Years of investment in research and development, including partnership with the VIP lab and others, have enabled Mystique to handle challenges beyond any other projec-tor-alignment solution. ID. © 2022, John Wiley and Sons Inc. All rights reserved.

关键词： Pixels

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Estimation of visual ERP signals with nonextensive entropy

Estimation of visual ERP signals with nonextensive entropy

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ICBPE 2006 - 2006 International Conference on Biomedical and Pharmaceutical Engineering

作者： Minfen, Shen Bin, Li Hongrong, Tang Key Lab. of Digital Signal and Image Processing of Guangdong Province Shantou University Shantou China

ISBN: (纸本)8190426249

Nonextensive entropy, the Tsallis-like time-dependent entropy (TDE), is developed to study the event-related potential signals ( ERPs ) in a cue, "E"-orientation discrimination task. The statistical characteristics of the TDE for different signal distributions are studied. Results indicated that with the appearance of P2 and N2, a significant decrease of TDE was correlated with the responses to small size cue which supports the previous theoretic analysis. This method provides a novel quantitative measurement for ERPs actives and may be a useful tool in signals monitoring. © 2006 Research Publishing Services.

关键词： Entropy

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