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arXiv 2022年

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

Siamese trackers have been among the state-of-the-art solutions in each Visual Object Tracking (VOT) challenge over the past few years. However, with great accuracy comes great computational complexity: to achieve real-time processing, these trackers have to be massively parallelised and are usually run on high-end GPUs. Easy to implement, this approach is energy consuming, and thus cannot be used in many low-power applications. To overcome this, one can use energy-efficient embedded devices, such as heterogeneous platforms joining the ARM processor system with programmable logic (FPGA). In this work, we propose a hardware-software implementation of the well-known fully connected Siamese tracker (SiamFC). We have developed a quantised Siamese network for the FINN accelerator, using algorithm-accelerator co-design, and performed design space exploration to achieve the best efficiency-to-energy ratio (determined by FPS and used resources). For our network, running in the programmable logic part of the Zynq UltraScale+ MPSoC ZCU104, we achieved the processing of almost 50 frames-per-second with tracker accuracy on par with its floating point counterpart, as well as the original SiamFC network. The complete tracking system, implemented in ARM with the network accelerated on FPGA, achieves up to 17 fps. These results bring us towards bridging the gap between the highly accurate but energy-demanding algorithms and energy-efficient solutions ready to be used in low-power, edge systems. © 2022, CC BY.

关键词： Energy efficiency

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APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semantic Segmentation

APSeg: Auto-Prompt Network for Cross-Domain Few-Shot Semanti...

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Conference on computer vision and Pattern Recognition (CVPR)

作者： Weizhao He Yang Zhang Wei Zhuo Linlin Shen Jiaqi Yang Songhe Deng Liang Sun Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen Institute of Artificial Intelligence and Robotics for Society School of Computer Science University of Nottingham China

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Few-shot semantic segmentation (FSS) endeavors to segment unseen classes with only a few labeled samples. Current FSS methods are commonly built on the assumption that their training and application scenarios share similar domains, and their performances degrade significantly while applied to a distinct domain. To this end, we propose to leverage the cutting-edge foundation model, the segment Anything Model (SAM), for generalization enhancement. The SAM however performs unsatisfactorily on domains that are distinct from its training data, which primarily comprise natural scene images, and it does not support automatic segmentation of specific semantics due to its interactive prompting mechanism. In our work, we introduce APSeg, a novel auto-prompt network for cross-domain few-shot semantic segmentation (CD-FSS), which is designed to be auto-prompted for guiding cross-domain segmentation. Specifically, we propose a Dual Prototype Anchor Transformation (DPAT) module that fuses pseudo query prototypes extracted based on cycle-consistency with support prototypes, allowing features to be transformed into a more stable domain-agnostic space. Additionally, a Meta Prompt (MPG) module is introduced to automatically generate prompt embeddings, eliminating the need for manual visual prompts. We build an efficient model which can be applied directly to target domains without fine-tuning. Extensive experiments on four cross-domain datasets show that our model outperforms the state-of-the-art CD-FSS method by 5.24% and 3.10% in average accuracy on 1-shot and 5-shot settings, respectively.

关键词： Training Metalearning Visualization Semantic segmentation Semantics Prototypes Training data

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Data-Driven Analysis of Skin Cancer Classification with Convolutional Neural Networks for E-Health Applications

Data-Driven Analysis of Skin Cancer Classification with Conv...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil Anglia Ruskin University School of Computing and Information Science Cambridge United Kingdom Hartpury University Animal and Agriculture Department Gloucester United Kingdom University of the West of England Centre for Machine Vision Bristol Robotics Laboratory Bristol United Kingdom Department of Mathematics and Computer Science Canada Incheon National University Department of Embedded Systems Engineering Incheon Korea Republic of

ISBN: (纸本)9798350351255

This study explores the effectiveness of Convolutional Neural Networks (CNNs) in automatically classifying skin cancer for e-health applications. The trained model showcases impressive performance by leveraging the HAM10000 dataset, which includes a wide range of skin lesion images from seven different classes. The parameters and architecture of the CNN model are presented in a systematic manner, providing valuable insights into the reasoning behind its design. The model is optimized using the Adam optimizer and annealing techniques to ensure efficient convergence. The model's performance is assessed on validation and test datasets, showcasing an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. This study highlights the significant potential of CNN as a powerful tool for automating the diagnosis of skin cancer, which is in line with the growing trend of using deep learning for medical image analysis. © 2024 IEEE.

关键词： Electronic health record

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Stiffness-based Offline Toolpath Error Compensation for Robotized Incremental Forming

Stiffness-based Offline Toolpath Error Compensation for Robo...

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Automation, Control and Robots (ICACR), International Conference on

作者： Walid K. Shaker Alexandr Klimchik Institute of Robotics and Computer Vision Innopolis University Innopolis Russia Lincoln Centre for Autonomous Systems Research (L-CAS) School of Computer Science University of Lincoln Lincoln United Kingdom

This paper addresses the challenge of the poor accuracy and the toolpath deviation in Incremental Sheet Forming (ISF) caused by the forming forces applied to the workpiece. The paper proposes an offline toolpath error compensation approach based on stiffness analysis. A truncated cone was used as a test case and the numerical simulation of the ISF process was conducted on Abaqus CAE software to extract forming forces corresponding to toolpath points. The compensation algorithm is based on the Virtual Joint Method (VJM) and is implemented on a FANUC R-2000iC/165F robot using RoboDK API for MATLAB. The results show that the deviation of the robot tool due to the forming forces may reach up to 20 mm which can be handled by the compensated toolpath. Finally, the study proposes an overall process for the stiffness-based correction algorithm.

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Comparative Analysis of Springback Compensation for Various Profiles in Incremental Forming

Comparative Analysis of Springback Compensation for Various ...

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International Russian Automation Conference (RusAutoCon)

Incremental forming encounters a common challenge known as the springback effect, wherein the workpiece undergoes elastic deformation and deviates slightly from the desired shape once the forming tool is released. This discrepancy between the intended and obtained shape results in reduced geometric accuracy, making incremental forming less precise compared to conventional methods. This research presents a novel springback effect compensation model for sheet forming processes. The main objective is to evaluate the model's performance across various profiles, with a focus on enhancing precision and accuracy in the formed shapes. The proposed model demonstrates an impressive ability to compensate for approximately 60% of the springback effect, offering a practical solution for offline springback compensation.

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Image saliency detection method based on multi-feature maps fusion 4

Image saliency detection method based on multi-feature maps ...

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4th International Conference on computer Graphics, Image, and Virtualization, ICCGIV 2024

作者： Li, Xiaoli Liu, Yunpeng Zhao, Huaici Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China Key Laboratory of Opto-Electronic Information Processing Shenyang China The Key Lab of Image Understanding and Computer Vision Shenyang China Shenyang Jianzhu University Shenyang China

ISBN: (纸本)9781510683242

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics." © 2024 SPIE.

关键词： Feature extraction

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Traffic Sign Classification Using Deep and Quantum Neural Networks

arXiv

引用

arXiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2022, CC BY.

关键词： Traffic signs

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Traffic Sign Classification Using Deep and Quantum Neural Networks

TechRxiv

引用

TechRxiv 2022年

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Kraków Poland

关键词： Traffic signs

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DualQuat-LOAM: LiDAR Odometry and Mapping parametrized on Dual Quaternions

arXiv

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arXiv 2024年

作者： Velasco-Sánchez, Edison P. Recalde, Luis F. Li, Guanrui Candelas-Herias, Francisco A. Puente-Mendez, Santiago T. Torres-Medina, Fernando AUROVA Group of Automation Robotics and Computer Vision University of Alicante San Vicente del Raspeig S/N Alicante03690 Spain Worcester Polytechnic Institute Worcester01609 United States

This paper reports on a novel method for LiDAR odometry estimation, which completely parameterizes the system with dual quaternions. To accomplish this, the features derived from the point cloud, including edges, surfaces, and Stable Triangle Descriptor (STD), along with the optimization problem, are expressed in the dual quaternion set. This approach enables the direct combination of translation and orientation errors via dual quaternion operations, greatly enhancing pose estimation, as demonstrated in comparative experiments against other state-of-the-art methods. Our approach reduced drift error compared to other LiDAR-only-odometry methods, especially in scenarios with sharp curves and aggressive movements with large angular displacement. DualQuat-LOAM is benchmarked against several public datasets. In the KITTI dataset it has a translation and rotation error of 0.79% and 0.0039°/m, with an average run time of 53 ms. © 2024, CC BY-NC-ND.

关键词： robotics

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Arbitrary-Scale Image Synthesis

arXiv

引用

arXiv 2022年

作者： Ntavelis, Evangelos Shahbazi, Mohamad Kastanis, Iason Timofte, Radu Danelljan, Martin Van Gool, Luc Computer Vision Lab ETH Zurich Switzerland Robotics & ML CSEM Switzerland KU Leuven Belgium

Positional encodings have enabled recent works to train a single adversarial network that can generate images of different scales. However, these approaches are either limited to a set of discrete scales or struggle to maintain good perceptual quality at the scales for which the model is not trained explicitly. We propose the design of scale-consistent positional encodings invariant to our generator’s layers transformations. This enables the generation of arbitrary-scale images even at scales unseen during training. Moreover, we incorporate novel inter-scale augmentations into our pipeline and partial generation training to facilitate the synthesis of consistent images at arbitrary scales. Lastly, we show competitive results for a continuum of scales on various commonly used datasets for image synthesis. Copyright © 2022, The Authors. All rights reserved.

关键词： Encoding (symbols)

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