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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Jonathan Ventura Zuzana Kukelova Torsten Sattler Dániel Baráth Department of Computer Science & Software Engineering Cal Poly San Luis Obispo Faculty of Electrical Engineering Visual Recognition Group Czech Technical University in Prague Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Department of Computer Science Computer Vision and Geometry Group ETH Zürich

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Keypoints used for image matching often include an estimate of the feature scale and orientation. While recent work has demonstrated the advantages of using feature scales and orientations for relative pose estimation, relatively little work has considered their use for absolute pose estimation. We introduce minimal solutions for absolute pose from two oriented feature correspondences in the general case, or one scaled and oriented correspondence given a known vertical direction. Nowadays, assuming a known direction is not particularly restrictive as modern consumer devices, such as smartphones or drones, are equipped with Inertial Measurement Units (IMU) that provide the gravity direction by default. Compared to traditional absolute pose methods requiring three point correspondences, our solvers need a smaller minimal sample, reducing the cost and complexity of robust estimation. Evaluations on large-scale and public real datasets demonstrate the advantage of our methods for fast and accurate localization in challenging conditions. Code is available at https://***/danini/absolute-pose-from-oriented-and-sealed-features.

关键词： Location awareness Accuracy Simultaneous localization and mapping Measurement units Pose estimation Noise Inertial navigation

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Early Detection of Diabetes Using ML Based Classification Algorithms 13th

Early Detection of Diabetes Using ML Based Classification Al...

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13th International Advanced Computing Conference, IACC 2023

作者： Ashisha, G.R. Mary, X. Anitha Chowdhury, Subrata Karthik, C. Choudhury, Tanupriya Kotecha, Ketan Electronics and Instrumentation Engineering Karunya Institute of Technology and Science Tamil Nadu Coimabtore India Robotics Engineering Karunya Institute of Technology and Sciences Tamil Nadu Coimbatore India Computer Science and Engineering Sreenivasa Institute of Technology and Management Studies Andhra Pradesh Chittoor India Robotics Engineering Jyothi Enngineering College Kerala Cheruthuruthy India Maharashtra Pune412115 India Maharashtra Pune412115 India

ISBN: (纸本)9783031567025

This article introduces a method for classifying diabetes based on machine learning (ML) methods. In recent years, significant focus have been put onto increasing disease classification performance through the use of ML approaches. This paper outlines the use of five interpretable ML algorithms: Bagging classifier, Random Forest, AdaBoost, Multilayer Perceptron, and Restricted Boltzmann Machine. All the ML classifiers were trained and tested in a benchmark Biostat Diabetes Dataset using Python programming. Each technique’s performance is evaluated to discover which has the finest accuracy, precision, recall, F1-score, specificity, and sensitivity. Experimental findings and assessment reveal that the Random Forest technique outperforms all other ML techniques by achieving 98% precision, 98% recall, 98% F1-score, 75% sensitivity, 96% specificity, and accuracy of 97.5%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Multilayer neural networks

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Domain-Invariant Similarity Activation Map Contrastive Learning for Retrieval-Based Long-Term Visual Localization

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IEEE/CAA Journal of Automatica Sinica 2022年第2期9卷 313-328页

作者： Hanjiang Hu Hesheng Wang Zhe Liu Weidong Chen Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China IEEE Department of Automation Institute of Medical RoboticsKey Laboratory of System Control and Information Processing of Ministry of EducationKey Laboratory of Marine Intelligent Equipment and System of Ministry of EducationShanghai Jiao Tong UniversityShanghai 200240China Department of Computer Science and Technology University of CambridgeCambridge CB30FDUnited Kingdom

Visual localization is a crucial component in the application of mobile robot and autonomous *** retrieval is an efficient and effective technique in image-based localization *** to the drastic variability of environmental conditions,e.g.,illumination changes,retrievalbased visual localization is severely affected and becomes a challenging *** this work,a general architecture is first formulated probabilistically to extract domain-invariant features through multi-domain image ***,a novel gradientweighted similarity activation mapping loss(Grad-SAM)is incorporated for finer localization with high *** also propose a new adaptive triplet loss to boost the contrastive learning of the embedding in a self-supervised *** final coarse-to-fine image retrieval pipeline is implemented as the sequential combination of models with and without Grad-SAM *** experiments have been conducted to validate the effectiveness of the proposed approach on the CMU-Seasons *** strong generalization ability of our approach is verified with the RobotCar dataset using models pre-trained on urban parts of the CMU-Seasons *** performance is on par with or even outperforms the state-of-the-art image-based localization baselines in medium or high precision,especially under challenging environments with illumination variance,vegetation,and night-time ***,real-site experiments have been conducted to validate the efficiency and effectiveness of the coarse-to-fine strategy for localization.

关键词： Deep representation learning place recognition visual localization

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Exploring Intermittent Dynamics of Neural Activity in a Single-Pair System of Excitatory and Inhibitory Neurons

Exploring Intermittent Dynamics of Neural Activity in a Sing...

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Emerging Techniques in Computational Intelligence (ICETCI), International Conference on

作者： Akio Sugawara Sou Nobukawa Nobuhiko Wagatsuma Keiichiro Inagaki Department of Computer Science Chiba Institute of Technology Narashino Japan Department of Information Science Toho University Funabashi Japan Department of Artificial Intelligence and Robotics Chubu University Kasugai Japan

A long-tailed property has been observed at various levels of the brain, ranging from neural population activity to the level of cognitive neurodynamics. Specifically, at the cognitive neurodynamics level, a phenomenon called perceptual alternation displays perceptual durations that follow a gamma or log-normal distribution. Moreover, this perceptual alternation becomes nondeterministic. The occurrence of alternation phenomena has also been noticed in neural populations at the cognitive neurodynamics level. Even in a system consisting of a single pair of excitatory and inhibitory neurons, referred to as chaos-chaos intermittency (CCI), a similar intermittent alternation of neural activity emerges, involving intermittent transitions between multiple isolated attractors. In this study, our hypothesis was that nondeterminism and long-tailed properties can emerge in this single-pair system. To test this hypothesis, we evaluated the determinism of two types of dynamics in the system that couples excitatory and inhibitory neurons: 1) transitions between attractors and 2) behavior within attractors. This evaluation was performed on multiple time scales using multi-scale entropy analysis (MSE) and an iterated amplitude-adjusted Fourier transform (IAAFT) surrogate. The results demonstrated that the transitions between attractors were nondeterministic, while the behavior within attractors was deterministic. Furthermore, a long-tailed property manifested in the duration of the transitions between attractors, possibly resulting from long-term evaluation in the chaotic unstable orbit. The emergence of this long-tailed property was attributed to the low frequency CCI around the attractor-merging bifurcation. This discovery contributes to the understanding of the long-tailed properties exhibited by neural activity across multiple levels.

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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International Conference on Microelectronics, MIEL

作者： P. Bogacki M. Długosz T. Talaśka R. Długosz Aptiv Services Poland Kraków Poland Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications AGH University of Science and Technology Kraków Poland Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan University of Technology Poznan Poland Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz University of Science and Technology Bydgoszcz Poland

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance Systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock.

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A study on the noise reduction method in turbid water environments using Spatial frequency characteristics

A study on the noise reduction method in turbid water enviro...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Ayumu Koura Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka city Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong-si Korea

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Scattering noise reduction is a challenging project to remove noise under scattering media conditions such as fog or turbid water. In previous study, blurring caused by scattering medium particles in fog or turbid water was removed by low-pass filtering. However, in this method, the accurate color information of the object may be lost due to rough low-frequency filtering. In addition, it is difficult to the obtain object shape information in heavy turbid environments. To solve this problem, we propose a new de-scattering algorithm that combines Fourier-domain filtering and integral imaging by creating a filter tailored to the frequency to be removed. To prove the proposed method, we perform a numerical analysis of experimental results using conventional and proposed methods.

关键词： Water Three-dimensional displays Image color analysis Shape Numerical analysis Noise reduction Scattering Reconstruction algorithms Colored noise Image reconstruction

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Lightweight 2D Human Pose Estimation for Fitness Coaching System 36

Lightweight 2D Human Pose Estimation for Fitness Coaching Sy...

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36th International Technical Conference on Circuits/Systems, computers and Communications, ITC-CSCC 2021

作者： Jeon, Hobeom Yoon, Youngwoo Kim, Dohyung University of Science and Technology Department of Computer Software Korea Republic of Electronics and Telecommunications Research Institute Intelligent Robotics Research Division Korea Republic of

ISBN: (纸本)9781665435536

In this paper, we describe lightweight 2D human pose estimation for a fitness coaching system. To achieve real-time inference speed on mobile devices, we propose the online pose distillation learning strategy that trains large teacher networks and small student networks simultaneously. The proposed lightweight model requires significantly lower computation as well as shows competitive pose estimation accuracy on the COCO keypoint detection dataset. In addition, the model was fine-tuned on a fitness dataset for the fitness coaching application where various fitness postures were accessed. © 2021 IEEE.

关键词： Distillation

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A study on Image Processing Method for Noise Reduction in Digital Holographic Microscopy

A study on Image Processing Method for Noise Reduction in Di...

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International Symposium on Environmentally Conscious Design and Inverse Manufacturing (EcoDesign)

作者： Masaharu Nagashima Hyun-Woo Kim Myungjin Cho Min-Chul Lee Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Iizuka city Japan School of ICT Robotics and Mechanical Engineering IITC Hankyong National University Anseong-si Korea

ISBN: (数字)9798350363043

ISBN: (纸本)9798350363050

Digital Holographic Microscopy (DHM) can obtain three-dimensional (3D) information about the fine structure of an object by utilizing the phase information of coherent light. In DHM, during the process of reconstructing the shape of an object, phase information is extracted by windowing the sideband containing the phase information. The 3D information of the object is then obtained from the acquired phase information. When windowing the sideband in a narrow range, it can remove noise caused by the DC spectrum, but detailed shape information of the object is lost. On the other hand, when windowing in a wide range, detailed shape information of the object can be obtained, but noise from the DC spectrum increases. To solve this trade-off relationship, we propose a method of determining the range for windowing the sideband using a function and then averaging the windowed sidebands. This proposed method can reduce the impact of noise from the DC spectrum and enables the acquisition of the detailed shape information. In this paper, we explain the reconstruction algorithm of this method and compare it with conventional methods and the previous study, phase differences averaging (PDA).

关键词： Three-dimensional displays Filtering Shape Microscopy Noise Noise reduction Reconstruction algorithms Amplitude modulation Data mining Informatics

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Learning Embeddings with Centroid Triplet Loss for Object Identification in Robotic Grasping

arXiv

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arXiv 2024年

作者： Gouda, Anas Schwarz, Max Reining, Christopher Behnke, Sven Kirchheim, Alice TU Dortmund Germany Autonomous Intelligent Systems - Computer Science VI Center for Robotics University of Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Germany Fraunhofer IML Germany

Foundation models are a strong trend in deep learning and computer vision. These models serve as a base for applications as they require minor or no further fine-tuning by developers to integrate into their applications. Foundation models for zero-shot object segmentation such as Segment Anything (SAM) output segmentation masks from images without any further object information. When they are followed in a pipeline by an object identification model, they can perform object detection without training. Here, we focus on training such an object identification model. A crucial practical aspect for an object identification model is to be flexible in input size (number of input images). As object identification is an image retrieval problem, a suitable method should handle multi-query multigallery situations without constraining the number of input images (e.g. by having fixed-size aggregation layers). The key solution to train such a model is the centroid triplet loss (CTL), which aggregates image features to their centroids. CTL yields high accuracy, avoids misleading training signals and keeps the model input size flexible. In our experiments, we establish a new state of the art on the ArmBench object identification task, which shows general applicability of our model. We furthermore demonstrate an integrated unseen object detection pipeline on the challenging HOPE dataset, which requires fine-grained detection. There, our pipeline matches and surpasses related methods which have been trained on dataset-specific data. Code and pretrained models are available. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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Modular Sensor Integration into Soft Robots using Stretchable Wires for Nuclear Infrastructure Inspection and Radiation Spectroscopy

Modular Sensor Integration into Soft Robots using Stretchabl...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Calder Wilson Joseph Karam Callen Votzke Farhan Rozaidi Camille J. Palmer Ross L. Hatton Matthew L. Johnston School of Electrical Engineering and Computer Science Corvallis OR USA Collaborative Robotics and Intelligent Systems Institute Corvallis OR USA School of Nuclear Science & Engineering Oregon State University Corvallis OR USA

Soft robots are uniquely suited for applications in inspection, search and rescue, and exploration in confined and unstructured environments. Leveraging the benefits of these soft robots will increasingly require the integration of equally compliant electronic components for sensing, actuation, control, and computation to maintain mechanical conformability at the system level. In this work, we demonstrate the integration of modular electronic sensors into a robotic snake platform using stretchable interconnects. Aimed at applications in nuclear infrastructure inspection, included sensors include visual perception and on-board radiation spectroscopy. In addition to high mechanical compliance, the stretchable interconnect enables physical separation of analog sensors and digital electronics for use in radiation environments. We present details of stretchable electronics fabrication and integration, standalone validation of integrated sensors, and field test results from a simulated nuclear infrastructure inspection task.

关键词： Mechanical sensors Fabrication Spectroscopy Visualization Wires Soft robotics Inspection

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