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Camera Calibration with a Transparent Calibration Tool Using Color Filters: Application to Stereo Camera Calibration for a Distant Object

Camera Calibration with a Transparent Calibration Tool Using...

引用

International conference on pattern recognition

作者： Y. Katayama Institute of Aerospace Technology Japan Aerospace Exploration Agency Tokyo Japan

In this paper, we propose a transparent calibration tool that can calibrate in a straightforward manner without resetting or removing the tool, which consists of two parallel transparent boards with patterns of dots of a color filter material. The tool is installed in front of a color camera, which acquires images through it. Images for calibration and the scene image are acquired simultaneously in different color bands. The proposed method is applied to stereo camera calibration for a distant small object

关键词： Calibration Cameras Filters Stereo vision Layout Parameter estimation Robot vision systems Aerospace materials computer vision Application software

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Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic Segmentation of Urban Roadways

Toronto-3D: A Large-scale Mobile LiDAR Dataset for Semantic ...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Weikai Tan Nannan Qin Lingfei Ma Ying Li Jing Du Guorong Cai Ke Yang Jonathan Li Department of Geography and Environmental Management University of Waterloo Waterloo ON Canada College of Computer Engineering Jimei University Xiamen China Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Semantic segmentation of large-scale outdoor point clouds is essential for urban scene understanding in various applications, especially autonomous driving and urban high-definition (HD) mapping. With rapid developments of mobile laser scanning (MLS) systems, massive point clouds are available for scene understanding, but publicly accessible large-scale labeled datasets, which are essential for developing learning-based methods, are still limited. This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. This dataset covers approximately 1 km of point clouds and consists of about 78.3 million points with 8 labeled object classes. Baseline experiments for semantic segmentation were conducted and the results confirmed the capability of this dataset to train deep learning models effectively. Toronto-3D is released 1 to encourage new research, and the labels will be improved and updated with feedback from the research community.

关键词： Three-dimensional displays Semantics Roads Laser radar Sensors Machine learning Automobiles

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AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results

AIS 2024 Challenge on Video Quality Assessment of User-Gener...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Marcos V. Conde Saman Zadtootaghaj Nabajeet Barman Radu Timofte Chenlong He Qi Zheng Ruoxi Zhu Zhengzhong Tu Haiqiang Wang Xiangguang Chen Wenhui Meng Xiang Pan Huiying Shi Han Zhu Xiaozhong Xu Lei Sun Zhenzhong Chen Shan Liu Zicheng Zhang Haoning Wu Yingjie Zhou Chunyi Li Xiaohong Liu Weisi Lin Guangtao Zhai Wei Sun Yuqin Cao Yanwei Jiang Jun Jia Zhichao Zhang Zijian Chen Weixia Zhang Xiongkuo Min Steve Göring Zihao Qi Chen Feng CAIDAS & IFI Computer Vision Lab. University of Würzburg Sony Interactive Entertainment FTG

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based methods capable of estimating the perceptual quality of UGC videos. The user-generated videos from the YouTube UGC Dataset include diverse content (sports, games, lyrics, anime, etc.), quality and resolutions. The proposed methods must process 30 FHD frames under 1 second. In the challenge, a total of 102 participants registered, and 15 submitted results during the challenge period. The performance of the top-5 submissions is reviewed and provided here as a survey of diverse deep models for Video Quality Assessment of user-generated content.

关键词： Surveys Learning systems Video on demand Reviews User-generated content Quality assessment pattern recognition

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Blurry-Consistency Segmentation Framework with Selective Stacking on Differential Interference Contrast 3D Breast Cancer Spheroid

Blurry-Consistency Segmentation Framework with Selective Sta...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Thanh-Huy Nguyen Thi Kim Ngan Ngo Mai Anh Vu Ting-Yuan Tu Department of Biomedical Engineering IMBSL Lab National Cheng Kung University Tainan ROC

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The ability of three-dimensional (3D) spheroid modeling to study the invasive behavior of breast cancer cells has drawn increased attention. The deep learning-based image processing framework is very effective at speeding up the cell morphological analysis process. Out-of-focus photos taken while capturing 3D cells under several z-slices, however, could negatively impact the deep learning model. In this work, we created a new algorithm to handle blurry images while preserving the stacked image quality. Furthermore, we proposed a unique training architecture that leverages consistency training to help reduce the bias of the model when dense-slice stacking is applied. Additionally, the model’s stability is increased under the sparse-slice stacking effect by utilizing the self-training approach. The new blurring stacking technique and training flow are combined with the suggested architecture and self-training mechanism to provide an innovative yet easy-to-use framework. Our methods produced noteworthy experimental outcomes in terms of both quantitative and qualitative aspects.

关键词： Training Solid modeling Three-dimensional displays Microscopy Stacking computer architecture Breast cancer

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Multi-Stream CNN for Spatial Resource Allocation: a Crop Management Application

Multi-Stream CNN for Spatial Resource Allocation: a Crop Man...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Alexandre Barbosa Thiago Marinho Nicolas Martin Naira Hovakimyan University of Illinois at Urbana-Champaign Urbana IL USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Modeling the spatial structure of crop inputs is of great importance for accurate yield prediction. It is a fundamental step towards optimizing the spatial allocation of resources such as seed and fertilizer. We propose two distinct architectures of Multi-Stream Convolutional Neural Network (MSCNN) - Late Fusion (LF) and Early Fusion (EF) - to model yield response to seed and nutrient management. A study presents a comparison between proposed models with conventional 2D and 3D CNN architectures, and existing agronomy methods. The dataset used to train and test the models is constructed using on-farm experiment data from nine cornfields across the US together with multispectral satellite images. Results show that the MSCNN-LF achieved a 20% reduction of the prediction's mean squared error value when compared to a 3D CNN, and a 26% reduction when compared to a 2D CNN. An optimization algorithm uses the MSCNN-LF model's gradient to change the manageable inputs variables in a way the expected profit is maximized subject to resource constraints. It is shown that an increase of up to 5.2% on expected crop yield return is obtained when compared to usual management practices.

关键词： Agriculture Nitrogen computer architecture Data models Three-dimensional displays Soil Analytical models

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Efficient Search and Verification for Function Based Classification from Real Range Images

Efficient Search and Verification for Function Based Classif...

引用

International conference on pattern recognition

作者： I. Shimshoni E. Rivlin O. Soldea Department of Management Information Systems University of Haifa Haifa Israel Department of Computer Science Technion-Israel Institute of Technology Haifa Israel

In this work we propose a probabilistic model for generic object classification from raw range images. Our approach supports a validation process in which classes are verified using a functional class graph in which functional parts and their realization hypotheses are explored. The validation tree is efficiently searched. Some functional requirements are validated in a final procedure for more efficient separation of objects from non-objects. The search employs a knowledge repository mechanism that monotonically adds knowledge during the search and speeds up the classification process. Finally, we describe our implementation and present results of experiments on a database that comprises about one-hundred-and-fifty real raw range images of object instances from ten classes

关键词： Shape Image databases Object recognition pattern recognition Management information systems computer science Tree graphs computer vision System testing Stereo vision

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Multi-Head Distillation for Continual Unsupervised Domain Adaptation in Semantic Segmentation

Multi-Head Distillation for Continual Unsupervised Domain Ad...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Antoine Saporta Arthur Douillard Tuan-Hung Vu Patrick Pé rez Matthieu Cord Sorbonne Universit&#x00E9Valeo.ai Sorbonne Universit&#x00E9Heuritech Valeo.ai

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487405

Unsupervised Domain Adaptation (UDA) is a transfer learning task which aims at training on an unlabeled target domain by leveraging a labeled source domain. Beyond the traditional scope of UDA with a single source domain and a single target domain, real-world perception systems face a variety of scenarios to handle, from varying lighting conditions to many cities around the world. In this context, UDAs with several domains increase the challenges with the addition of distribution shifts within the different target domains. This work focuses on a novel framework for learning UDA, continuous UDA, in which models operate on multiple target domains discovered sequentially, without access to previous target domains. We propose MuHDi, for Multi-Head Distillation, a method that solves the catastrophic forgetting problem, inherent in continual learning tasks. MuHDi performs distillation at multiple levels from the previous model as well as an auxiliary target-specialist segmentation head. We report both extensive ablation and experiments on challenging multi-target UDA semantic segmentation benchmarks to validate the proposed learning scheme and architecture.

关键词： Training Adaptation models Urban areas Semantics Transfer learning computer architecture Benchmark testing

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Tri-VAE: Triplet Variational Autoencoder for Unsupervised Anomaly Detection in Brain Tumor MRI

Tri-VAE: Triplet Variational Autoencoder for Unsupervised An...

引用

IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Hansen Wijanarko Evelyne Calista Li-Fen Chen Yong-Sheng Chen National Yang Ming Chiao Tung University Taiwan

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

The intricate manifestations of pathological brain lesions in imaging data pose challenges for supervised detection methods due to the scarcity of annotated samples. To overcome this difficulty, our focus shifts to unsupervised anomaly detection. In this work, we exclusively train the proposed model using healthy data to identify unseen anomalies during testing. This study entails investigating the triplet-based variational autoencoder to simultaneously learn the distribution of healthy brain data and denoising capabilities. Importantly, we rectify a misconception inherent in prior projection-based approaches which relies on the presumption that healthy regions within images would persist unaltered in the reconstructed output. This inadvertently implied a substantial likeness in latent space representations between lesion and lesion-free images. However, this assumption might not hold true, particularly due to the potential significant impact of lesion area intensities on the projection process notably for autoencoders with single information bottleneck. To overcome this limitation, we disentangled metric learning from latent sampling. This approach ensures that both lesion and lesion-free input images are projected into the same distribution, specifically the lesion-free projection. Moreover, we introduce a semantic-guided gated cross skip module to enhance spatial detail retrieval while suppressing anomalies, leveraging robust healthy brain representation semantics exist in the deeper levels of the decoder. We also discovered that incorporating structure similarity index measure as an extra training objective bolsters the capability of anomaly detection for the proposed model.

关键词： Training Measurement Pathology Semantics Noise reduction Brain modeling pattern recognition

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Deep Lighting Environment Map Estimation from Spherical Panoramas

Deep Lighting Environment Map Estimation from Spherical Pano...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vasileios Gkitsas Nikolaos Zioulis Federico Alvarez Dimitrios Zarpalas Petros Daras Centre for Research and Technology Hellas (CERTH) Information Technologies Institute (ITI) Signals Systems and Radiocommunications Department (SSRD) Universidad Politecnica de Madrid (UPM)

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Estimating a scene's lighting is a very important task when compositing synthetic content within real environments, with applications in mixed reality and postproduction. In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama. In addition to being a challenging and ill-posed problem, the lighting estimation task also suffers from a lack of facile illumination ground truth data, a fact that hinders the applicability of data-driven methods. We approach this problem differently, exploiting the availability of surface geometry to employ image-based relighting as a data generator and supervision mechanism. This relies on a global Lambertian assumption that helps us overcome issues related to pre-baked lighting. We relight our training data and complement the model's supervision with a photometric loss, enabled by a differentiable image-based relighting technique. Finally, since we predict spherical spectral coefficients, we show that by imposing a distribution prior on the predicted coefficients, we can greatly boost performance. Code and models available at ***/DeepPanoramaLighting.

关键词： Lighting Estimation Task analysis Geometry Data models Image color analysis Rendering (computer graphics)

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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

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IEEE computer Society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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