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Three-Dimensional Volume Reconstruction Based on Trajectory Fusion from Confocal Laser Scanning Microscope Images

Three-Dimensional Volume Reconstruction Based on Trajectory ...

引用

conference on computer vision and pattern recognition (CVPR)

作者： Sang-Chul Lee P. Bajcsy National Center for Supercomputing Applications University of Illinois Urbana-Champaign Urbana-Champaign IL USA

In this paper, we address the problem of 3D volume reconstruction from depth adjacent subvolumes (i.e., sets of image frames) acquired using a confocal laser scanning microscope (CLSM). Our goal is to align sub-volumes by estimating an optimal global image transformation which preserves morphological smoothness of medical structures (called features, e.g., blood vessels) inside of a reconstructed 3D volume. We approached the problem by learning morphological characteristics of structures inside of each sub-volume, i.e. centroid trajectories of features. Next, adjacent sub-volumes are aligned by fusing the morphological characteristics of structures using extrapolation or model fitting. Finally, a global sub-volume to subvolume transformation is computed based on the entire set of fused structures. The trajectory-based 3D volume reconstruction method described here is evaluated with a pair of consecutive physical sections using two evaluation metrics for morphological continu

关键词： Image reconstruction Laser fusion Microscopy Biomedical imaging computer vision Optical distortion Optical imaging Biomedical optical imaging Geometrical optics Blood vessels

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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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Multi-Resolution Spin-Images

Multi-Resolution Spin-Images

引用

conference on computer vision and pattern recognition (CVPR)

作者： H.Q. Dinh S. Kropac Department of Computer Science Stevens Institute of Technology USA

Johnson and Hebert’s spin-images have been applied to the registration of range images and object recognition with much success because they are rotation, scale, and pose invariant. In this paper we address two issues concerning spin-images, namely: (1) comparing uncompressed spinimages across large datasets is costly, and (2) a method to select the appropriate bin size and image width for spinimages is not clearly defined. Our solution to these issues is a multi-resolution method that generates a pyramid of spin-images by successively decreasing the spin-image size by powers of two. To efficiently correlate surface points, we compare spin-images in a low-to-high resolution manner. Once multi-resolution spin-images are generated for a given object, we have found that the different resolutions can also be used to compare objects that have differing or non-uniform point densities. To select the appropriate bin sizes for comparing such objects, we use the ratio of the average edge lengths of the objects. We also show preliminary results of using the pyramid to converge on the appropriate image width by traversing the pyramid in a low-to-high resolution manner looking for the highest resolution at which the fewest number of highly correlated points are found to match a given feature point.

关键词： Image resolution Multiresolution analysis computer science Image converters Shape measurement Object recognition Power generation Spline Image sampling Timing

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Contrastive Clothing and Pose Generation for Cloth-Changing Person Re-Identification

Contrastive Clothing and Pose Generation for Cloth-Changing ...

引用

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

作者： Vuong D. Nguyen Pranav Mantini Shishir K. Shah Dept. of Computer Science Quantitative Imaging Lab University of Houston

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Cloth-Changing Person Re-Identification (CCRe-ID) aims at matching an individual across cameras after a long period of time, presenting variations in clothing compounded with changes in pose, viewpoint, etc. In this work, we propose CCPG: Contrastive Clothing and Pose Generation framework for CCRe-ID. Beyond appearance, CCPG captures cloth-invariant body shape information using a Relational Graph Attention Network. Training a robust CCRe-ID model requires a wide range of clothing variations and expensive cloth labeling, which is lacked in current CCRe-ID datasets. To address this, we propose a GAN-based model for clothing and pose transfer across identities to augment images of more clothing variations and of different persons wearing similar clothing. The augmented batch of images serve as inputs to our proposed Fine-grained Contrastive Losses, which not only supervise the Re-ID model to learn discriminative person embeddings under long-term scenarios but also ensure in-distribution data generation. Results on CCRe-ID datasets demonstrate the effectiveness of our CCPG framework. Code will be available here.

关键词： Training computer vision Shape conferences Computational modeling Clothing Data models

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Challenges in Energy-Efficient Deep Neural Network Training with FPGA

Challenges in Energy-Efficient Deep Neural Network Training ...

引用

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

作者： Yudong Tao Rui Ma Mei-Ling Shyu Shu-Ching Chen Department of Electrical and Computer Engineering University of Miami Coral Gables FL USA School of Computing and Information Sciences Florida International University Miami FL USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

In recent years, it is highly demanding to deploy Deep Neural Networks (DNNs) on edge devices, such as mobile phones, drones, robotics, and wearable devices, to process visual data collected by the cameras embedded in these systems. In addition to the model inference, training DNNs locally can benefit model customization and data privacy protection. Since many edge systems are powered by batteries or have limited energy budgets, Field-Programmable Gate Array (FPGA) is commonly used as the primary processing engine to satisfy both demands in performance and energy-efficiency. Although many recent research papers have been published on the topic of DNN inference with FPGAs, training a DNN with FPGAs has not been well exploited by the community. This paper summarizes the current status of adopting FPGA for DNN computation and identifies the main challenges in deploying DNN training on FPGAs. Moreover, a performance metric and evaluation workflow are proposed to compare the FPGA-based systems for DNN training in terms of (1) usage of on-chip resources, (2) training efficiency, (3) energy efficiency, and (4) model performance for specific computer vision tasks.

关键词： Field programmable gate arrays Computational modeling Training Hardware Neural networks Machine learning Graphics processing units

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Leveraging Large Language Models for Multimodal Search

Leveraging Large Language Models for Multimodal Search

引用

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

作者： Oriol Barbany Michael Huang Xinliang Zhu Arnab Dhua CSIC-UPC Institut de Robòtica i Informàtica Industrial Visual Search & AR Amazon

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Multimodal search has become increasingly important in providing users with a natural and effective way to express their search intentions. Images offer fine-grained details of the desired products, while text allows for easily incorporating search modifications. However, some existing multimodal search systems are unreliable and fail to address simple queries. The problem becomes harder with the large variability of natural language text queries, which may contain ambiguous, implicit, and irrelevant information. Addressing these issues may require systems with enhanced matching capabilities, reasoning abilities, and context-aware query parsing and rewriting. This paper introduces a novel multimodal search model that achieves a new performance milestone on the Fashion200K dataset [19]. Additionally, we propose a novel search interface integrating Large Language Models (LLMs) to facilitate natural language interaction. This interface routes queries to search systems while conversationally engaging with users and considering previous searches. When coupled with our multimodal search model, it heralds a new era of shopping assistants capable of offering human-like interaction and enhancing the overall search experience.

关键词： computer vision Large language models conferences Natural languages Pipelines Image retrieval computer architecture

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Sports Camera Calibration via Synthetic Data

Sports Camera Calibration via Synthetic Data

引用

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

作者： Jianhui Chen James J. Little University of British Columbia

ISBN: (数字)9781728125060

ISBN: (纸本)9781728125077

Calibrating sports cameras is important for autonomous broadcasting and sports analysis. Here we propose a highly automatic method for calibrating sports cameras from a single image using synthetic data. First, we develop a novel camera pose engine that generates camera poses by randomly sampling camera parameters. The camera pose engine has only three significant free parameters so that it can effectively generate diverse camera poses and corresponding edge (i.e. field marking) images. Then, we learn compact feature descriptors via a siamese network from paired edge images and build a feature-pose database. After that, we use a novel GAN (generative adversarial network) model to detect field markings in real images. Finally, we query an initial camera pose from the feature-pose database and refine camera poses using truncated distance images. We evaluate our method on both synthetic and real data. Our method not only demonstrates the robustness on the synthetic data but also achieves state-of-the-art accuracy on a standard soccer dataset and very high performance on a volleyball dataset.

关键词： Cameras Image edge detection Gallium nitride Calibration Robot vision systems Feature extraction

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Dr-SAM: An End-to-End Framework for Vascular Segmentation, Diameter Estimation, and Anomaly Detection on Angiography Images.

Dr-SAM: An End-to-End Framework for Vascular Segmentation, D...

引用

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

作者： Vazgen Zohranyan Vagner Navasardyan Hayk Navasardyan Jan Borggrefe Shant Navasardyan Yerevan State University (YSU) ServiceTitan Inc. Ruhr-Universität Bochum (RUB) Johannes Wesling University Hospital Department of Radiology Neuroradiology and Nuclear Medicine Synopsys Armenia CJSC Picsart AI Research (PAIR)

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Recent advancements in AI have significantly transformed medical imaging, particularly in angiography, by enhancing diagnostic precision and patient care. However existing works are limited in analyzing the aorta and iliac arteries, above all for vascular anomaly detection and characterization. To close this gap, we propose Dr-SAM, a comprehensive multi-stage framework for vessel segmentation, diameter estimation, and anomaly analysis aiming to examine the peripheral vessels through angiography images. For segmentation we introduce a customized positive/negative point selection mechanism applied on top of the Segment Anything Model (SAM), specifically for medical (Angiography) images. Then we propose a morphological approach to determine the vessel diameters followed by our histogram-driven anomaly detection approach. Moreover, we introduce a new benchmark dataset for the comprehensive analysis of peripheral vessel angiography images which we hope can boost the upcoming research in this direction leading to enhanced diagnostic precision and ultimately better health outcomes for individuals facing vascular issues.

关键词： Image segmentation Angiography conferences Estimation Benchmark testing pattern recognition Anomaly detection

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VT-Former: An Exploratory Study on Vehicle Trajectory Prediction for Highway Surveillance through Graph Isomorphism and Transformer

VT-Former: An Exploratory Study on Vehicle Trajectory Predic...

引用

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

作者： Armin Danesh Pazho Ghazal Alinezhad Noghre Vinit Katariya Hamed Tabkhi University of North Carolina Charlotte

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Enhancing roadway safety has become an essential computer vision focus area for Intelligent Transportation Systems (ITS). As a part of ITS, Vehicle Trajectory Prediction (VTP) aims to forecast a vehicle’s future positions based on its past and current movements. VTP is a pivotal element for road safety, aiding in applications such as traffic management, accident prevention, work-zone safety, and energy optimization. While most works in this field focus on autonomous driving, with the growing number of surveillance cameras, another sub-field emerges for surveillance VTP with its own set of challenges. In this paper, we introduce VT-Former, a novel transformer-based VTP approach for highway safety and surveillance. In addition to utilizing transformers to capture long-range temporal patterns, a new Graph Attentive Tokenization (GAT) module has been proposed to capture intricate social interactions among vehicles. This study seeks to explore both the advantages and the limitations inherent in combining transformer architecture with graphs for VTP. Our investigation, conducted across three benchmark datasets from diverse surveillance viewpoints, showcases the State-of-the-Art (SotA) or comparable performance of VT-Former in predicting vehicle trajectories. This study underscores the potential of VT-Former and its architecture, opening new avenues for future research and exploration.

关键词： Road transportation computer vision Surveillance Computational modeling computer architecture Benchmark testing Transformers

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TattTRN: Template Reconstruction Network for Tattoo Retrieval

TattTRN: Template Reconstruction Network for Tattoo Retrieva...

引用

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

作者： Lazaro Janier Gonzalez-Soler Maciej Salwowski Christian Rathgeb Daniel Fischer da/sec - Biometrics and Security Research Group Darmstadt Germany Faculty of Computer Science Technical University of Denmark Denmark

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Tattoos have been used effectively as soft biometrics to assist law enforcement in the identification of offenders and victims, as they contain discriminative information, and are a useful indicator to locate members of a criminal gang or organisation. Due to various privacy issues in the acquisition of images containing tattoos, only a limited number of databases exists. This lack of databases has delayed the development of new methods to effectively retrieve a potential suspect’s tattoo images from a candidate gallery. To mitigate this issue, in our work, we use an unsupervised generative approach to create a balanced database consisting of 28,550 semi-synthetic images with tattooed subjects from 571 tattoo categories. Further, we introduce a novel Tattoo Template Reconstruction Network (TattTRN), which learns to map the input tattoo sample to its respective tattoo template to enhance the distinguishing attributes of the final feature embedding. Experimental results with real data, i.e. WebTattoo and BIVTatt databases, demonstrate the soundness of the presented approach: an accuracy of up to 99% is achieved for checking at most the first 20 entries of the candidate list. 1

关键词： Image segmentation Data privacy computer vision Databases Law enforcement conferences Semantics

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