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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Yusheng Liu Yingjie Zhao Meihuan Wang Yichao Hao Xiuying Wang Lisheng Wang Department of Automation Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Computer Science The University of Sydney Sydney NSW Australia

In surgery-based renal cancer treatment, one of the most essential tasks is the three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images. In this paper, we propose an end-to-end convolutional neural network-based framework to segment multiple renal structures, including kidneys, kidney tumors, arteries, and veins from arterial-phase CT images. Our method consists of two collaborative modules: First, we propose an encoding-decoding network, named Multi-Branch Dilated Convolutional Network (MBD-Net), consisting of residual, hybrid dilated convolutional, and reduced-dimensional convolutional structures, which improves the feature extraction ability with relatively fewer network parameters. Given that renal tumors and cysts have confusing geometric structures, we also design the Cyst Discriminator to effectively distinguish tumors from cysts without labeling information via gray-scale curves and radiographic features. We have quantitatively evaluated our approach on a publicly available dataset from MICCAI 2022 Kidney Parsing for Renal Cancer Treatment Challenge (KiPA2022), with mean Dice similarity coefficient (DSC) as 96.18%, 90.99%, 88.66% and 80.35% for the kidneys, kidney tumors, arteries, and veins respectively, winning the stable and top performance in the *** relevance—The proposed CNN-Based framework can automatically segment 3D kidneys, renal tumors, arteries, and veins for kidney parsing techniques, benefiting surgery-based renal cancer treatment.

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MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation

arXiv

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arXiv 2023年

作者： Tang, Fenghe Nian, Bingkun Ding, Jianrui Quan, Quan Yang, Jie Liu, Wei Zhou, S. Kevin School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China School of Computer Science and Technology Harbin Institute of Technology China Institute of Computing Technology China

Due to the scarcity and specific imaging characteristics in medical images, light-weighting Vision Transformers (ViTs) for efficient medical image segmentation is a significant challenge, and current studies have not yet paid attention to this issue. This work revisits the relationship between CNNs and Transformers in lightweight universal networks for medical image segmentation, aiming to integrate the advantages of both worlds at the infrastructure design level. In order to leverage the inductive bias inherent in CNNs, we abstract a Transformer-like lightweight CNNs block (ConvUtr) as the patch embeddings of ViTs, feeding Transformer with denoised, non-redundant and highly condensed semantic information. Moreover, an adaptive Local-Global-Local (LGL) block is introduced to facilitate efficient local-to-global information flow exchange, maximizing Transformer's global context information extraction capabilities. Finally, we build an efficient medical image segmentation model (MobileUtr) based on CNN and Transformer. Extensive experiments on five public medical image datasets with three different modalities demonstrate the superiority of MobileUtr over the state-of-the-art methods, while boasting lighter weights and lower computational cost. Code is available at https://***/FengheTan9/MobileUtr. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Single landmark based self-localization of mobile robots

Single landmark based self-localization of mobile robots

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Canadian Conference on computer and Robot Vision, CRV

作者： A. Bais R. Sablatnig J. Gu Robotics Research Group Department of Electrical and Computer Engineering Dalhousie University Halifax Canada Institute of Computer Technology University of Technology Vienna Vienna Austria Pattern Recognition and Image Processing Group Institute of Computer Aided Automation University of Technology Vienna Vienna Austria

In this paper we discuss landmark based absolute localization of tiny autonomous mobile robots in a known environment. Landmark features are naturally occurring as it is not allowed to modify the environment with special navigational aids. These features are sparse in our application domain and are frequently occluded by other robots. This makes simultaneous acquisition of two or more landmarks difficult. Therefore, we propose a system that requires a single landmark feature. The algorithm is based on range measurement of a single landmark from two arbitrary points whose displacement can be measured using dead-reckoning sensors. Range estimation is done with a stereo vision system. Simulation results show that the robot can localize itself if it can estimates range of the same landmark from two different position and if the displacement between the two position is known.

关键词： Mobile robots Robot sensing systems Robot vision systems Cameras Navigation Position measurement Robotics and automation Displacement measurement Educational robots Goniometers

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An Optimizing Parameters and Feature Selection in SVM Based on Improved Cockroach Swarm Optimization 16th

An Optimizing Parameters and Feature Selection in SVM Based ...

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16th International Conference on Intelligent Information Hiding and Multimedia Signal processing, IIH-MSP 2020 in conjunction with the 13th International Conference on Frontiers of Information Technology, Applications and Tools, FITAT 2020

作者： Nguyen, Trong-The Yu, Jie Nguyen, Thi-Thanh-Tan Lai, Quoc-Anh Ngo, Truong-Giang Dao, Thi-Kien Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou China College of Mechanical and Automotive Engineering Fujian University of Technology Fuzhou China Information Technology Faculty Electric Power University Hanoi Viet Nam Department of Pattern Recognition & Image Processing Institute of Information Technology Vietnam Academy of Science and Technology Hanoi Viet Nam Faculty of Computer Science and Engineering Thuyloi University 175 Tay Son Dong Da Hanoi Viet Nam

ISBN: (纸本)9789813367562

This study improves a classifier of the support vector machine (SVM) by optimizing its parameters by adjusting cockroach swarm optimization (CSO). Classification system design includes data inputs, pre-process, and classification. The relief technique selects the feature subset, and the kernel parameters used as input for the system. The feature selection and classification of data sets were figured out by the combination of ICSO and SVM with its optimized parameters synchronous. Simulation results of the proposed scheme are compared with other systems, e.g., GA-SVM, PSO-SVM, and CSO-SVM, which show that the proposed project can achieve better classification accuracy than the different algorithms. It is promising that the proposed scheme can be applied to high-dimensional data classification, such as image classification, signal classification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Support vector machines

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Preliminary Design of a Terrain recognition System

Preliminary Design of a Terrain Recognition System

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Fan Zhang Zheng Fang Ming Liu He Huang department of Electrical Computer and Biomedical Engineering University of Rhode Island National Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

ISBN: (纸本)9781424441211

This paper aims to design a wearable terrain recognition system, which might assist the control of powered artificial prosthetic legs. A laser distance sensor and inertial measurement unit (IMU) sensors were mounted on human body. These sensors were used to identify the movement state of the user, reconstruct the geometry of the terrain in front of the user while walking, and recognize the type of terrain before the user stepped on it. Different sensor configurations were investigated and compared. The designed system was evaluated on one healthy human subject when walking on an obstacle course in the laboratory environment. The results showed that the reconstructed terrain height demonstrated clearer pattern difference among studied terrains when the laser was placed on the waist than that when the laser was mounted on the shank. The designed system with the laser on the waist accurately recognized 157 out of 160 tested terrain transitions, 300ms-2870ms before the user switched the negotiated terrains. These promising results demonstrated the potential application of the designed terrain recognition system to further improve the control of powered artificial legs.

关键词： terrains preliminary design landscapes recognition system Laser design system Consumers Waist range sensor laboratory environment human subject walking

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Optimal sub-shape models by minimum description length

Optimal sub-shape models by minimum description length

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Conference on computer Vision and pattern recognition (CVPR)

作者： G. Langs P. Peloschek H. Bischof Institute for Computer Graphics and Vision Graz University of Technology Graz Austria Pattern Recognition and Image Processing Group University of Technology Vienna Vienna Austria Department of Clinical Radiology Vienna Medical University Vienna Austria

Active shape models are powerful and widely used tool to interpret complex image data. By building models of shape variation they enable search algorithms to use a priori knowledge in an efficient and gainful way. However, due to the linearity of PCA, non-linearities like rotations or independently moving sub-parts in the data can deteriorate the resulting model considerably. Although non-linear extensions of active shape models have been proposed and application specific solutions have been used, they still need a certain amount of user interaction during model building. In this paper the task of building/choosing optimal models is tackled in a more generic information theoretic fashion. In particular, we propose an algorithm based on the minimum description length principle to find an optimal subdivision of the data into sub-parts, each adequate for linear modeling. This results in an overall more compact model configuration. Which in turn leads to a better model in terms of modes of variations. The proposed method is evaluated on synthetic data, medical images and hand contours.

关键词： Active shape model Biomedical imaging Principal component analysis computer vision Linearity Polynomials pattern recognition computer graphics Radiology image processing

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Speed estimation for scene objects using stereo visual odometry methods

Speed estimation for scene objects using stereo visual odome...

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IEEE International Conference on Intelligent computer Communication and processing

作者： Catalin Golban Sergiu Nedevschi Image Processing and Pattern Recognition Group Computer Science Department Technical University of Cluj-Napoca

ISBN: (纸本)9781479914920

This paper proposes a novel method to determine the speed of the surrounding vehicles in traffic scenarios. Relying on the video information obtained from a stereo camera mounted on a moving vehicle, we first determine the vehicle ego motion based on static scene features then we determine the relative motion between objects based on features situated on the moving objects. For robustness to false feature matches everything is plugged into a multi-RANSAC framework. The novelty of the method consist in the fact that the relative motion between the objects can be determined with the same algorithm that was previously used for ego motion estimation, the only difference consisting in the geometric constraints that are imposed to the subset of point features considered for inliers set detection and evaluation. Also, the proposed method does not rely on the fact that objects are detected previously and it does not detect the objects.

关键词： Visual odometry Obstacles speed Speed detection Ego motion Motion estimation Speed estimation

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Automatic needle segmentation using 3D Gray-level Hough Transform in 3D ultrasound images

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Journal of Computational Information Systems 2008年第5期4卷 2021-2026页

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Image Processing and Intelligence Control Key Lab. of Education Ministry of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in 3D space and Local Dimensionality Estimator (GST). Different from the traditional Hough transformation the process of binarization is not needed, because the proposed algorithm adopts the original gray level image directly. We tested it with the synthetic data and water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1° and position deviation less than 1 mm. © 2008 Binary Information Press.

关键词： Hough transforms

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Face recognition - A One-Shot Learning Perspective

Face Recognition - A One-Shot Learning Perspective

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International IEEE Conference on Signal-image Technologies and Internet-Based System

作者： Sukalpa Chanda Asish Chakrapani GV Anders Brun Anders Hast Umapada Pal David Doermann Department of Information Technology Østfold University College Norway Computer Vision and Pattern Recognition Unit Indian Statistical Institute India Centre for Image Analysis Uppsala University Sweden Computer Science and Engineering University at Buffalo USA

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Ability to learn from a single instance is something unique to the human species and One-shot learning algorithms try to mimic this special capability. On the other hand, despite the fantastic performance of Deep Learning-based methods on various image classification problems, performance often depends having on a huge number of annotated training samples per class. This fact is certainly a hindrance in deploying deep neural network-based systems in many real-life applications like face recognition. Furthermore, an addition of a new class to the system will require the need to re-train the whole system from scratch. Nevertheless, the prowess of deep learned features could also not be ignored. This research aims to combine the best of deep learned features with a traditional One-Shot learning framework. Results obtained on 2 publicly available datasets are very encouraging achieving over 90% accuracy on 5-way One-Shot tasks, and 84% on 50-way One-Shot problems.

关键词： Face Face recognition Feature extraction Task analysis Training Adaptation models computer architecture

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Game-theoretic route planning for team of UAVs

Game-theoretic route planning for team of UAVs

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Ping Yan Ming-Yue Ding Cheng-Ping Zhou Institute of Pattern Recognition and Artificial Intelligence. Education Ministry Key Laboratory for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Department of Weaponry Engineering Naval University of Engineering Wuhan China

ISBN: (纸本)0780384032

Unmanned air vehicles (UAVs) are identified as an integral part of future military forces. The coordinated route-planning problems of UAV team with various architectures are addressed in the framework of game theory. A two-stage route planner has been proposed, which combines various game models and the concept of evolutionary computation and is compatible with the cooperative/competitive nature envisioned for UAV team. Our route planner can handle different kinds of mission constraints in hierarchical style. Potential routes of each vehicle form their own sub-population, and evolve only in their own sub-population, while the cooperation and competition among UAVs are reflected by the definition of fitness function. Experimental results show the feasibility of generating the coordinated routes for UAV team using game theory methods.

关键词： Unmanned aerial vehicles Game theory Evolutionary computation Automotive engineering Time of arrival estimation pattern recognition Artificial intelligence Control engineering education Laboratories image processing

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