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Pose Refinement with Joint Optimization of Visual Points and Lines

Pose Refinement with Joint Optimization of Visual Points and...

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ieee/RSJ International conference on Intelligent Robots and Systems (IROS)

作者： Gao, Shuang Wan, Jixiang Ping, Yishan Zhang, Xudong Dong, Shuzhou Yang, Yuchen Ning, Haikuan Li, Jijunnan Guo, Yandong OPPO Res Inst Shanghai Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai Peoples R China

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

High-precision camera re-localization technology in a pre-established 3D environment map is the basis for many tasks, such as Augmented Reality, Robotics and Autonomous Driving. The point-based visual re-localization approaches are well-developed in recent decades, but are insufficient in some feature-less cases. In this paper, we design a complete pipeline for camera pose refinement with points and lines, which contains the innovatively designed line extracting CNN named VLSE, the line matching and the pose optimization approaches. We adopt a novel line representation and customize a hybrid convolution block based on the Stacked Hourglass network [1], to detect accurate and stable line features on images. Then we apply a geometric-based strategy to obtain precise 2D-3D line correspondences using epipolar constraint and reprojection filtering. A following point-line joint cost function is constructed to optimize the camera pose with the initial coarse pose from the pure point-based localization. Sufficient experiments are conducted on open datasets, i.e, line extractor on Wireframe and YorkUrban, localization performance on InLoc duc1 and duc2, to confirm the effectiveness of our point-line joint pose optimization method.

关键词： Location awareness Training visualization Three-dimensional displays Filtering Pipelines Robot vision systems

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Comprehending and Ordering Semantics for Image Captioning

Comprehending and Ordering Semantics for Image Captioning

引用

ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Yehao Pan, Yingwei Yao, Ting Mei, Tao JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Comprehending the rich semantics in an image and ordering them in linguistic order are essential to compose a visually-grounded and linguistically coherent description for image captioning. Modern techniques commonly capitalize on a pre-trained object detector/classifier to mine the semantics in an image, while leaving the inherent linguistic ordering of semantics under-exploited. In this paper, we propose a new recipe of Transformer-style structure, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture. Technically, we initially utilize a cross-modal retrieval model to search the relevant sentences of each image, and all words in the searched sentences are taken as primary semantic cues. Next, a novel semantic comprehender is devised to filter out the irrelevant semantic words in primary semantic cues, and mean-while infer the missing relevant semantic words visually grounded in the image. After that, we feed all the screened and enriched semantic words into a semantic ranker, which learns to allocate all semantic words in linguistic order as humans. Such sequence of ordered semantic words are further integrated with visual tokens of images to trigger sentence generation. Empirical evidences show that COS-Net clearly surpasses the state-of-the-art approaches on COCO and achieves to-date the best CIDEr score of 141.1% on Karpathy test split. Source code is available at https://***/YehLi/xmodaler/tree/master/configs/image_caption/cosnet.

关键词： visualization Computer vision Codes Semantics Computer architecture Linguistics Transformers

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Envisioning Individualized Fashion Remanufacturing: Empowering Non-Experts Through visualization-Enhanced Human-Multi-Robot Interaction 20

Envisioning Individualized Fashion Remanufacturing: Empoweri...

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20th Annual ACM/ieee International conference on Human-Robot Interaction, HRI 2025

作者： Gollob, Emanuel Bastan, Amir Braumann, Johannes Luible-Bär, Christiane University of Arts Linz Linz Austria University of Arts Linz Linz Austria

ISBN: (纸本)9798350378931

Automating apparel remanufacturing can improve textiles' ecological and societal impact, but integrating robotics into fashion is challenging due to fabric complexity. This demo presents a platform enabling non-experts to define multi-robot remanufacturing tasks through visualization-enhanced hand guiding. By leveraging collaborative automation and parametric systems, a dual-robot setup achieves precision and adaptability. Using Grasshopper for path planning and VVVV for real-time interaction, we created an intuitive, easy-to-use interface whereby real-time visualization makes advanced robotics accessible to non-experts, transforming remanufacturing processes, fostering innovation in human-robot collaboration, and ultimately advancing the fashion and textiles sector. This approach broadens participation and encourages sustainable solutions. © 2025 ieee.

关键词： Multipurpose robots

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Enhanced Framework for MRI Brain Tumor Recognition with Residual Learning and Intuitive Heatmap visualization

Enhanced Framework for MRI Brain Tumor Recognition with Resi...

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2024 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Velmurugan, L. Maheswari, R.K. Janani, K. Agalya, K. Poornima, M. Gayathri, K.C. Vit Bhopal University Bhopal-Indore Highway Kothrikalan Madhya Pradesh Sehore India M.A.M College of Enginnering and Technology Artificial Intelligence and Data Science Tamilnadu Trichy India Manipal institute of Technology Manipal Academy of Higher Education Department of Data science and computer applications Manipal India Sri Eshwar College of Engineering Department of Computer Science Engineering Coimbatore India Oasys Institute of Technology Department of Information Technology Tamil Nadu Trichy India Kelambakkam Chennai India

ISBN: (纸本)9798331543617

Early detection of brain tumors is crucial for effective treatment, yet it poses a significant challenge due to the rapid nature of the disease's progression. This study explores the application of ResNet, aimed at improving the accuracy of brain tumor diagnosis in magnetic resonance imaging (MRI) scans. Our model was trained on a dataset comprising 253 MRI images, which included 155 confirmed cases of tumors. The residual learning framework of ResNet effectively captures intricate features and mitigates issues such as vanishing gradients. The results indicate that the ResNet model achieved an impressive accuracy rate of 97%, surpassing traditional detection methods. We evaluated the model's performance using metrics like accuracy and F1-score, demonstrating its robustness. To improve the interpretability of our model's predictions, we utilized Grad-CAM (Gradient-weighted Class Activation Mapping), which highlighted critical areas in the MRI scans that influenced the model's decisions. Our findings confirm ResNet' s effectiveness in medical imaging and stress the importance of model interpretability in healthcare, allowing radiologists to make informed diagnostic choices. © 2024 ieee.

关键词： Brain mapping

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Semi-Supervised Semantic Segmentation Using Unreliable Pseudo-Labels

Semi-Supervised Semantic Segmentation Using Unreliable Pseud...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Wang, Yuchao Wang, Haochen Shen, Yujun Fei, Jingjing Li, Wei Jin, Guoqiang Wu, Liwei Zhao, Rui Le, Xinyi Shanghai Jiao Tong Univ Shanghai Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

The crux of semi-supervised semantic segmentation is to assign adequate pseudo-labels to the pixels of unlabeled images. A common practice is to select the highly confident predictions as the pseudo ground-truth, but it leads to a problem that most pixels may be left unused due to their unreliability. We argue that every pixel matters to the model training, even its prediction is ambiguous. Intuitively, an unreliable prediction may get confused among the top classes (i.e., those with the highest probabilities), however, it should be confident about the pixel not belonging to the remaining classes. Hence, such a pixel can be convincingly treated as a negative sample to those most unlikely categories. Based on this insight, we develop an effective pipeline to make sufficient use of unlabeled data. Concretely, we separate reliable and unreliable pixels via the entropy of predictions, push each unreliable pixel to a category-wise queue that consists of negative samples, and manage to train the model with all candidate pixels. Considering the training evolution, where the prediction becomes more and more accurate, we adaptively adjust the threshold for the reliable-unreliable partition. Experimental results on various benchmarks and training settings demonstrate the superiority of our approach over the state-of-the-art alternatives.(1)

关键词： Training visualization Shape Semantics Predictive models Semisupervised learning Solids

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Actor-Centric Tubelets for Real-Time Activity Detection in Extended Videos

Actor-Centric Tubelets for Real-Time Activity Detection in E...

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22nd ieee/CVF Winter conference on Applications of Computer Vision (WACV)

作者： Mavroudi, Effrosyni Bindal, Prashast Vidal, Rene Johns Hopkins Univ Math Inst Data Sci Baltimore MD 21218 USA

ISBN: (纸本)9781665458245

We address the problem of detecting human and vehicle activities in long, untrimmed surveillance videos that capture a large field of view. Most existing activity detection approaches are designed for recognizing atomic human actions performed in the foreground. Therefore, they are not suitable for detecting activities in extended videos, which contain multiple actors performing co-occurring, complex activities with extreme spatio-temporal scale variations. In this paper, we propose a modular, actor-centric framework for real-time activity detection in extended videos. In particular, we decompose an extended video into a collection of smaller actor-centric tubelets of interest. Each tubelet is a video sub-volume associated with an actor and includes adaptive visual context for recognizing the actor's activities. Once these tubelets are extracted via an object-detection-based approach, we are able to detect activities in each tubelet by focusing on the actor situated in its foreground. To accurately detect the activities of a tubelet's actor we take into account the interactions with other detected actors and objects within the tubelet. We encode such interactions with a dynamic visual spatio-temporal graph and process it with a Graph Neural Network that yields context-aware actor representations. We validate our activity detection framework on the MEVA (Multiview Extended Video with Activities) dataset and the ActEV 2021 Sequestered Data Leaderboard and demonstrate its effectiveness in terms of speed and performance.

关键词： visualization Tracking Surveillance Focusing Object detection Real-time systems Graph neural networks

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Gamma-enhanced Spatial Attention Network for Efficient High Dynamic Range Imaging

Gamma-enhanced Spatial Attention Network for Efficient High ...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Li, Fangya Gang, Ruipeng Li, Chenghua Li, Jinjing Ma, Sai Liu, Chenming Cao, Yizhen State Key Lab Media Convergence & Commun CUC Beijing 100024 Peoples R China NRTA Acad Broadcasting Sci Beijing 100866 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

High dynamic range(HDR) imaging is the task of recovering HDR image from one or multiple input Low Dynamic Range (LDR) images. In this paper, we present Gamma-enhanced Spatial Attention Network(GSANet), a novel framework for reconstructing HDR images. This problem comprises two intractable challenges of how to tackle overexposed and underexposed regions and how to overcome the paradox of performance and complexity trade-off. To address the former, after applying gamma correction on the LDR images, we adopt a spatial attention module to adaptively select the most appropriate regions of various exposure low dynamic range images for fusion. For the latter one, we propose an efficient channel attention module, which only involves a handful of parameters while bringing clear performance gain. Experimental results show that the proposed method achieves better visual quality on the HDR dataset. The code will be available at: https://***/fancyicookie/GSANet

关键词： visualization Codes conferences Pipelines Imaging Dynamic range Performance gain

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High Performance, Web-Based, Real-Time Telemetry visualization for Deep Space Mission Support and Operations 8

High Performance, Web-Based, Real-Time Telemetry Visualizati...

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8th ieee International conference on Space Mission Challenges for information Technology (SMC-IT)

作者： Pomerantz, Marc, I Hans, Michael A. Sandoval, Michael W. Newman, Emily P. Wenzel, Sean D. Huang, Calvin CALTECH Jet Prop Lab 4800 Oak Grove Dr Pasadena CA 91125 USA

ISBN: (纸本)9781728185606

The Jet Propulsion Laboratory's Mission Control Systems Section and Mobility and Robotic Systems Section have partnered to developed a high-performance telemetry visualization system, based on our mature, web-based, 3D visualization software technology, that provides mission trajectory designers, operations personnel and reconstruction analysts with a highly accurate, re-usable, multi-mission solution to interactively visualize results from Entry, Descent and Landing (EDL) simulations, real-time mission test beds, live telemetry streams during critical mission events, such as landing, as well as post-event reconstruction analysis. This work builds on previous mission visualization systems that we have deployed for a variety of flight missions and JPL organizations including NASA's Mars Science Laboratory (MSL), NASA'S Low Density Supersonic Decelerator (LDSD), NASA's Soil Moisture Active Passive (SMAP), JPL's MoonRise Lunar sample return proposal. JPL's mission design teams, JPL's Office of Naval Research tasks, and JPL's Education and Public Outreach Office.

关键词： Mars 2020 Entry Decent and Landing Telemetry Processing Client-Server Web-based 3D visualization UI/UX Flight Mission Testing and Operations Simulation

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Recurrent Glimpse-based Decoder for Detection with Transformer

Recurrent Glimpse-based Decoder for Detection with Transform...

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ieee/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Chen, Zhe Zhang, Jing Tao, Dacheng Univ Sydney Sydney NSW Australia JD Explore Acad Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Although detection with Transformer (DETR) is increasingly popular, its global attention modeling requires an extremely long training period to optimize and achieve promising detection performance. Alternative to existing studies that mainly develop advanced feature or embedding designs to tackle the training issue, we point out that the Region-of-Interest (RoI) based detection refinement can easily help mitigate the difficulty of training for DETR methods. Based on this, we introduce a novel REcurrent Glimpse-based decOder (REGO) in this paper. In particular, the REGO employs a multi-stage recurrent processing structure to help the attention of DETR gradually focus on foreground objects more accurately. In each processing stage, visual features are extracted as glimpse features from RoIs with enlarged bounding box areas of detection results from the previous stage. Then, a glimpse-based decoder is introduced to provide refined detection results based on both the glimpse features and the attention modeling outputs of the previous stage. In practice, REGO can be easily embedded in representative DETR variants while maintaining their fully end-to-end training and inference pipelines. In particular, REGO helps Deformable DETR achieve 44.8 AP on the MSCOCO dataset with only 36 training epochs, compared with the first DETR and the Deformable DETR that require 500 and 50 epochs to achieve comparable performance, respectively. Experiments also show that REGO consistently boosts the performance of different DETR detectors by up to 7% relative gain at the same setting of 50 training epochs. Code is available via https://***/zhechen/DeformableDETR-REGO.

关键词： Training visualization Computer vision Pipelines Detectors Feature extraction Transformers

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Augmented Visual Localization Using a Monocular Camera for Autonomous Mobile Robots 18

Augmented Visual Localization Using a Monocular Camera for A...

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ieee 18th International conference on Automation Science and Engineering (ieee CASE)

作者： Salimzadeh, Ali Bhatt, Neel P. Hashemi, Ehsan Univ Alberta Mech Engn Dept 9211-116 St NW Edmonton AB Canada Univ Waterloo Mech & Mechatron Engn Dept 200 Univ Ave W Waterloo ON Canada

ISBN: (数字)9781665490429

ISBN: (纸本)9781665490429

A visual localization method utilizing a fisheye monocular camera is proposed to enhance navigation accuracy of autonomous mobile robots in indoor environments for warehouse or service robotics applications. Existing visual infrastructure-aided localization algorithms take advantage of uniquely colored or lit robots that limit their application to ideal lighting conditions, occlusion-free scenarios or multi-modal fusion with stereo vision, LiDAR, and inertial sensors which inevitably increases their complexity. Using fisheye monocular vision imposes challenges such as depth estimation, frame warping, and low accuracy of the state estimation for far objects. The proposed augmented localization framework includes an uncertainty-aware state observer employing a motion model with a learning-based input estimator and point cloud clusters over a region of interest, to estimate the position of a robot while maintaining effective computational efficiency. Observability of the developed state estimator and asymptotic stability of the estimation error dynamics are also studied. Various tests including occlusion, low visibility for far objects, and noisy depth estimation (from the clustered region of interest), have been conducted in indoor settings to validate the method. The tests confirm robust performance of the augmented visual localization framework in presence of intermittent measurements due to environmental conditions or low reliability of vision-based depth estimation. Furthermore, a significant increase in accuracy and consistency of visual localization is shown without using additional stereo, inertial, or LiDAR measurements.

关键词： Location awareness visualization Estimation error Robot vision systems Dynamics Estimation Predictive models

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