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UDAC: Under-Display Array Cameras

UDAC: Under-Display Array Cameras

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Chengyu Wang Jing Li Pavan C. Madhusudanarao Jinhan Hu Jitesh K. Singh WooJhon Choi Seok-Jun Lee Hamid R. Sheikh Samsung Research America Samsung Research India-Bangalore Samsung Electronics

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Under-display cameras (UDC) provide uninterrupted display experience on smartphones, but the captured images suffer from quality degradations such as diffraction blur, and reduced signal-to-noise ratio due to the presence of the OLED panel which obstructs the camera. In this work, we propose to use under-display array cameras (UDAC) consisting of two cameras placed under the OLED display panel to further reduce image artifacts and enhance image quality. We rotate the OLED panel by 45 degrees for one of the cameras to utilize the complimentary nature of the point spread function. We also propose an algorithm to combine images from the two cameras, which includes occlusion and optical flow analysis, image warping, color match and image blending. Experiments on real UDC devices show that the proposed method is superior in reducing the artifacts and improving the signal-to-noise ratio. The proposed method is simple but effective, and it can be easily extended to various configurations and incorporated with various algorithms.

关键词： Image quality Image color analysis Pipelines Organic light emitting diodes Cameras Image restoration pattern recognition

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Dynamic Addition of Noise in a Diffusion Model for Anomaly Detection

Dynamic Addition of Noise in a Diffusion Model for Anomaly D...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Justin Tebbe Jawad Tayyub Otto von Guericke University Magdeburg Magdeburg Germany Endress + Hauser Maulburg Germany

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models have found valuable applications in anomaly detection by capturing the nominal data distribution and identifying anomalies via reconstruction. Despite their merits, they struggle to localize anomalies of varying scales, especially larger anomalies such as entire missing components. Addressing this, we present a novel framework that enhances the capability of diffusion models, by extending the previous introduced implicit conditioning approach [24] in three significant ways. First, we incorporate a dynamic step size computation that allows for variable noising steps in the forward process guided by an initial anomaly prediction. Second, we demonstrate that denoising an only scaled input, without any added noise, outperforms conventional denoising process. Third, we project images in a latent space to abstract away from fine details that interfere with reconstruction of large missing components. Additionally, we propose a fine-tuning mechanism that facilitates the model to effectively grasp the nuances of the target domain. Our method undergoes rigorous evaluation on prominent anomaly detection datasets VisA, BTAD and MVTec yielding strong performance. Importantly, our framework effectively localizes anomalies regardless of their scale, marking a pivotal advancement in diffusion-based anomaly detection.

关键词： Location awareness Technological innovation computer vision conferences Noise reduction Noise Diffusion models

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Neural Fields for Co-Reconstructing 3D Objects from Incidental 2D Data

Neural Fields for Co-Reconstructing 3D Objects from Incident...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Dylan Campbell Eldar Insafutdinov João F. Henriques Andrea Vedaldi Australian National University University of Oxford

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We ask whether 3D objects can be reconstructed from real world data collected for some other purpose, such as autonomous driving or augmented reality, thus inferring objects only incidentally. 3D reconstruction from incidental data is a major challenge because, in addition to significant noise, only a few views of each object are observed, which are insufficient for reconstruction. We approach this problem as a co-reconstruction task, where multiple objects are reconstructed together, learning shape and appearance priors for regularization. In order to do so, we introduce a neural radiance field that is conditioned via an attention mechanism on the identity of the individual objects. We further disentangle shape from appearance and diffuse color from specular color via an asymmetric two-stream network, which factors shared information from instance-specific details. We demonstrate the ability of this method to reconstruct full 3D objects from partial, incidental observations in autonomous driving and other datasets.

关键词： computer vision Three-dimensional displays Shape Image color analysis conferences Noise Neural radiance field

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NTIRE 2023 Challenge on Light Field Image Super-Resolution: Dataset, Methods and Results

NTIRE 2023 Challenge on Light Field Image Super-Resolution: ...

引用

2023 ieee/CVF conference on computer vision and pattern recognition workshops, CVPRW 2023

作者： Wang, Yingqian Wang, Longguang Liang, Zhengyu Yang, Jungang Timofte, Radu Guo, Yulan Jin, Kai Wei, Zeqiang Yang, Angulia Guo, Sha Gao, Mingzhi Zhou, Xiuzhuang Van Duong, Vinh Huu, Thuc Nguyen Yim, Jonghoon Jeon, Byeungwoo Liu, Yutong Cheng, Zhen Xiao, Zeyu Xu, Ruikang Xiong, Zhiwei Liu, Gaosheng Jin, Manchang Yue, Huanjing Yang, Jingyu Gao, Chen Zhang, Shuo Chang, Song Lin, Youfang Chao, Wentao Wang, Xuechun Wang, Guanghui Duan, Fuqing Xia, Wang Wang, Yan Xia, Peiqi Wang, Shunzhou Lu, Yao Cong, Ruixuan Sheng, Hao Yang, Da Chen, Rongshan Wang, Sizhe Cui, Zhenglong Chen, Yilei Lu, Yongjie Cai, Dongjun An, Ping Salem, Ahmed Ibrahem, Hatem Yagoub, Bilel Kang, Hyun-Soo Zeng, Zekai Wu, Heng National University of Defense Technology China Aviation University of Air Force China University of Würzburg Germany Eth Zürich Switzerland Sun Yat-sen University The Shenzhen Campus of Sun Yat-sen University China Bigo Technology Pte. Ltd. Singapore Smart Medical Innovation Lab Beijing University of Posts and Telecommunications China Global Explorer Ltd. Suzhou China National Engineering Research Center of Visual Technology School of Computer Science Peking University China School of Artificial Intelligence Beijing University of Posts and Telecommunications China Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of University of Science and Technology of China China School of Electrical and Information Engineering Tianjin University China Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University China Beijing Normal University China Toronto Metropolitan University Canada Beijing Institute of Technology China Shenzhen MSU-BIT University China State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beihang Hangzhou Innovation Institute Yuhang China Faculty of Applied Sciences Macao Polytechnic University China School of Communication and Information Engineering Shanghai University China School of Information and Communication Engineering Chungbuk National University Korea Republic of Guangdong University of Technology China

ISBN: (纸本)9798350302493

In this report, we summarize the first NTIRE challenge on light field (LF) image super-resolution (SR), which aims at super-resolving LF images under the standard bicubic degradation with a magnification factor of 4. This challenge develops a new LF dataset called NTIRE-2023 for validation and test, and provides a toolbox called BasicLFSR to facilitate model development. Compared with single image SR, the major challenge of LF image SR lies in how to exploit complementary angular information from plenty of views with varying disparities. In total, 148 participants have registered the challenge, and 11 teams have successfully submitted results with PSNR scores higher than the baseline method LF-InterNet [1]. These newly developed methods have set new state-of-the-art in LF image SR, e.g., the winning method achieves around 1 dB PSNR improvement over the existing state-of-the-art method DistgSSR [2]. We report the solutions proposed by the participants, and summarize their common trends and useful tricks. We hope this challenge can stimulate future research and inspire new ideas in LF image SR. © 2023 ieee.

关键词： Image enhancement

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Mushroom Segmentation and 3D Pose Estimation from Point Clouds using Fully Convolutional Geometric Features and Implicit Pose Encoding

Mushroom Segmentation and 3D Pose Estimation from Point Clou...

引用

ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： George Retsinas Niki Efthymiou Petros Maragos School of Electrical and Computer Engineering National Technical University of Athens Greece

Modern agricultural applications rely more and more on deep learning solutions. However, training well-performing deep networks requires a large amount of annotated data that may not be available and in the case of 3D annotation may not even be feasible for human annotators. In this work, we develop a deep learning approach to segment mushrooms and estimate their pose on 3D data, in the form of point clouds acquired by depth sensors. To circumvent the annotation problem, we create a synthetic dataset of mushroom scenes, where we are fully aware of 3D information, such as the pose of each mushroom. The proposed network has a fully convolutional backbone, that parses sparse 3D data, and predicts pose information that implicitly defines both instance segmentation and pose estimation task. We have validated the effectiveness of the proposed implicit-based approach for a synthetic test set, as well as provided qualitative results for a small set of real acquired point clouds with depth sensors.

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Event-driven Re-Id: A New Benchmark and Method Towards Privacy-Preserving Person Re-Identification

Event-driven Re-Id: A New Benchmark and Method Towards Priva...

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22nd ieee/CVF Winter conference on Applications of computer vision (WACV)

作者： Ahmad, Shafiq Scarpellini, Gianluca Morerio, Pietro Del Bue, Alessio Univ Genoa Genoa Italy Ist Italino Tecnol Pattern Anal & Comp Vis PAVIS Genoa Italy Ist Italiano Tecnol Visual Geometry & Modelling VGM Genoa Italy

ISBN: (纸本)9781665458245

The large-scale use of surveillance cameras in public spaces raised severe concerns about an individual privacy breach. Introducing privacy and security in video surveillance systems, primarily in person re-identification (re-id), is quite challenging. Event cameras are novel sensors, which only respond to brightness changes in the scene. This characteristic makes event-based vision sensors viable for privacy-preserving in video surveillance. Integrating privacy into the person re-id;this work investigates the possibility of performing person re-id with the event-camera network for the first time. We transform the asynchronous events stream generated by an event camera into synchronous image-like representations to leverage deep learning models and then evaluate how complex the re-id problem is with this new sensor modality. Interestingly, such event-based representations contain meaningful spatial details which are very similar to standard edges and contours. We use two different representations, image-like representation and their transformation to polar coordinates (which carry more distinct edge patterns). Finally, we train a person re-id model on such images to demonstrate the feasibility of performing event-driven re-id. We evaluate the performance of our approach and produce baseline results on two synthetic datasets (generated from publicly available datasets, SAIVT and DukeMTMC-reid).

关键词： Image edge detection Transforms vision sensors Streaming media Sensor phenomena and characterization Cameras Video surveillance

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Multispectral Contrastive Learning with Viewmaker Networks

Multispectral Contrastive Learning with Viewmaker Networks

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Jasmine Bayrooti Noah Goodman Alex Tamkin Department of Computer Science Stanford University Stanford CA USA

Contrastive learning methods have been applied to a range of domains and modalities by training models to identify similar "views" of data points. However, specialized scientific modalities pose a challenge for this paradigm, as identifying good views for each scientific instrument is complex and time-intensive. In this paper, we focus on applying contrastive learning approaches to a variety of remote sensing datasets. We show that Viewmaker networks, a recently proposed method for generating views without extensive domain knowledge, can produce useful views in this setting. We also present a Viewmaker variant called Divmaker, which achieves similar performance and does not require adversarial optimization. Applying both methods to four multispectral imaging problems, each with a different format, we find that Viewmaker and Divmaker can outperform cropping- and reflection-based methods for contrastive learning in every case when evaluated on downstream classification tasks. This provides additional evidence that domain-agnostic methods can empower contrastive learning to scale to real-world scientific domains. Open source code can be found at https://***/jbayrooti/divmaker.

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DSTCFuse: A Method based on Dual-cycled Cross-awareness of Structure Tensor for Semantic Segmentation via Infrared and Visible Image Fusion

DSTCFuse: A Method based on Dual-cycled Cross-awareness of S...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Xuan Li Rongfu Chen Jie Wang Lei Ma Li Cheng Haiwen Yuan School of Electrical and Information Engineering Wuhan Institute of Technology Hubei Key Laboratory of Optical Information and Pattern Recognition

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-modality information fusion can compensate deficiencies of single modality and provide rich scene information for 2D semantic segmentation. However, the inconsistency in the feature space between different modalities may lead to poor presentation of objects and that would affect subsequent segmented effectiveness. The idea of modal transition can reduce the modal differences and avoid biased processing during the fusion process, but it is hard to perfectly retain the contents of the source images. To address these challenges, a fusion method based on dual-cycled cross-awareness of structure tensor is proposed. Firstly, we propose a dual-cycle modality transition network based on cross-awareness consistency to learn the differences in feature space from different modalities. Secondly, a set of globally structure-tensor preserving modules are designed to enhance the capabilities of the network in capturing complementary features and perceiving global modal consistency. Under the joint constraint of globally structure-tensor awareness loss and cross-awareness loss, our network achieves a robust mapping of feature space from visible to pseudo-infrared images without relying on Ground-Truth. Finally, the pseudo-infrared images that inherit the superior qualities of two modalities are fused with the original infrared images directly, which effectively reduces the complexity of fusion. Extensive comparative experiments show that our method outperforms state-of-the-art methods in qualitative and quantitative evaluation.

关键词： Degradation computer vision Tensors Semantic segmentation conferences Interference pattern recognition

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Online Multi-camera People Tracking with Spatial-temporal Mechanism and Anchor-feature Hierarchical Clustering

Online Multi-camera People Tracking with Spatial-temporal Me...

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ieee computer society conference on computer vision and pattern recognition workshops (CVPRW)

作者： Riu Cherdchusakulchai Sasin Phimsiri Visarut Trairattanapa Suchat Tungjitnob Wasu Kudisthalert Pornprom Kiawjak Ek Thamwiwatthana Phawat Borisuitsawat Teepakorn Tosawadi Pakcheera Choppradit Kasisdis Mahakijdechachai Supawit Vatathanavaro Worawit Saetan Vasin Suttichaya AI and Robotics Ventures (ARV) Thailand

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multi-camera Multi-object tracking (MTMC) surpasses conventional single-camera tracking by enabling seamless object tracking across multiple camera views. This capability is critical for security systems and improving situational awareness in various environments. This paper proposes a novel MTMC framework designed for online operation. The framework employs a three-stage pipeline: Multiobject Tracking (MOT), Multi-target Multi-camera Tracking (MTMC), and Cross Interval Synchronization (CIS). In the MOT stage, ReID features are extracted and localized tracklets are created. MTMC links these tracklets across cameras using spatial-temporal constraints and constraint hierarchical clustering with anchor features for improved inter-camera association. Finally, CIS ensures the temporal coherence of tracklets across time intervals. The proposed framework achieves robust tracking performance, validated on the challenging 2024 AI City Challenge with a HOTA score of 51.0556%, ranking sixth. The code is available at: https://***/ARV-MLCORE/AIC2024Track1ARV

关键词： Urban areas Pipelines Cameras Feature extraction Real-time systems pattern recognition Synchronization

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MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning

MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incre...

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2022 ieee/CVF conference on computer vision and pattern recognition, CVPR 2022

作者： Chi, Zhixiang Gu, Li Liu, Huan Wang, Yang Yu, Yuanhao Tang, Jin Noah's Ark Lab Huawei Technologies McMaster University Canada University of Manitoba Canada

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

In this paper, we tackle the problem of few-shot class incremental learning (FSCIL). FSCIL aims to incrementally learn new classes with only a few samples in each class. Most existing methods only consider the incremental steps at test time. The learning objective of these methods is often hand-engineered and is not directly tied to the objective (i.e. incrementally learning new classes) during testing. Those methods are sub-optimal due to the misalignment between the training objectives and what the methods are expected to do during evaluation. In this work, we proposed a bi-level optimization based on meta-learning to directly optimize the network to learn how to incrementally learn in the setting of FSCIL. Concretely, we propose to sample sequences of incremental tasks from base classes for training to simulate the evaluation protocol. For each task, the model is learned using a meta-objective such that it is capable to perform fast adaptation without forgetting. Furthermore, we propose a bi-directional guided modulation, which is learned to automatically modulate the activations to reduce catastrophic forgetting. Extensive experimental results demonstrate that the proposed method outperforms the baseline and achieves the state-of-the-art results on CIFARIOO, MiniImageNet, and CUB200 datasets. © 2022 ieee.

关键词： Training Adaptation models Protocols Modulation Bidirectional control Power capacitors pattern recognition

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