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arXiv 2023年

作者： Temtam, A. Pei, L. Iftekharuddin, K. The Vision Lab Electrical and Computer Engineering Old Dominion University NorfolkVA23529 United States

Computational modeling of Multiresolution-Fractional Brownian motion (fBm) has been effective in stochastic multiscale fractal texture feature extraction and machine learning of abnormal brain tissue segmentation. Further, deep multiresolution methods have been used for pixel-wise brain tissue segmentation. Robust tissue segmentation and volumetric measurement may provide more objective quantification of disease burden and offer improved tracking of treatment response for the disease. However, we posit that computational modeling of deep multiresolution fractal texture features may offer elegant feature learning. Consequently, this work proposes novel modeling of Multiresolution Fractal Deep Neural Network (MFDNN) and its computational implementation that mathematically combines a multiresolution fBm model and deep multiresolution analysis. The proposed full 3D MFDNN model offers the desirable properties of estimating multiresolution stochastic texture features by analyzing large amount of raw MRI image data for brain tumor segmentation. We apply the proposed MFDNN to estimate stochastic deep multiresolution fractal texture features for tumor tissues in brain MRI images. The MFDNN model is evaluated using 1251 patient cases for brain tumor segmentation using the most recent BRATS 2021 Challenges dataset. The evaluation of the proposed model using Dice overlap score, Husdorff distance and associated uncertainty estimation offers either better or comparable performances in abnormal brain tissue segmentation when compared to the state-of-the-art methods in the literature. Copyright © 2023, The Authors. All rights reserved.

关键词： Image segmentation

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Hierarchical Attention Approach in Multimodal Emotion Recognition for Human Robot Interaction 36

Hierarchical Attention Approach in Multimodal Emotion Recogn...

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

作者： Abdullah, Muhammad Ahmad, Mobeen Han, Dongil Vision and Image Processing Lab Sejong University Department of Computer Engineering Seoul Korea Republic of

ISBN: (纸本)9781665435536

The ability to perceive human emotions is one of the key elements that may promise a natural, genuine and more reliable human robot interaction. Though emotional perception in human robot interaction has been challenged by many difficulties, the lack of contextual understanding is can be attributed as the biggest hurdle in this regard. Most of the literature refers to the datasets developed in controlled environment to validate the performance of their systems which happens to be really good. Still those systems are very far from achieving that kind of performance in real-life scenarios. In this paper a multimodal emotion recognition strategy is presented, that uses voice features in addition to the facial expressions to determine the emotional state of the user. A hierarchical attention layer is used to for feature fusion purpose. The final system is end-to-end trainable, multimodal approach makes it more resilient to the changes in environment. The achieved 76.3% accuracy for eNterface05 video dataset, which is higher than the any single modality approach in the comparison. © 2021 IEEE.

关键词： Emotion Recognition

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Long-Term Invariant Local Features via Implicit Cross-Domain Correspondences

arXiv

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arXiv 2023年

作者： Pataki, Zador Altillawi, Mohammad Kanakis, Menelaos Pautrat, Rémi Shen, Fengyi Liu, Ziyuan Van Gool, Luc Pollefeys, Marc The Computer Vision and Geometry Lab Department of Computer Science ETH Zurich Switzerland The Computer Vision Center CVC-Barcelona The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Computer Vision Lab Department Electrical Engineering ETH Zurich Switzerland The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Intelligent Robotics Cloud Technology lab of Huawei-Munich Germany The Center for Processing Speech and Images KU Leuven The Computer Vision Lab ETH Zurich Switzerland

Modern learning-based visual feature extraction networks perform well in intra-domain localization, however, their performance significantly declines when image pairs are captured across long-term visual domain variations, such as different seasonal and daytime variations. In this paper, our first contribution is a benchmark to investigate the performance impact of long-term variations on visual localization. We conduct a thorough analysis of the performance of current state-of-the-art feature extraction networks under various domain changes and find a significant performance gap between intra- and cross-domain localization. We investigate different methods to close this gap by improving the supervision of modern feature extractor networks. We propose a novel data-centric method, Implicit Cross-Domain Correspondences (iCDC). iCDC represents the same environment with multiple Neural Radiance Fields, each fitting the scene under individual visual domains. It utilizes the underlying 3D representations to generate accurate correspondences across different long-term visual conditions. Our proposed method enhances cross-domain localization performance, significantly reducing the performance gap. When evaluated on popular long-term localization benchmarks, our trained networks consistently outperform existing methods. This work serves as a substantial stride toward more robust visual localization pipelines for long-term deployments, and opens up research avenues in the development of long-term invariant descriptors. Copyright © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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MSDNet: A novel multi-stage progressive image dehazing network 21

MSDNet: A novel multi-stage progressive image dehazing netwo...

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12th Indian Conference on computer vision, Graphics and Image Processing, ICVGIP 2021

作者： Manu, Chippy M. Sreeni, K.G. Computer Vision Lab Dept. Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

This paper presents a novel algorithm to dehaze a given hazy input image using a Multi-Stage progressive image Dehazing Network (MSDNet) architecture. The proposed multi-stage strategy framework splits the challenge of image recovery into sub-tasks for a degraded image to be gradually restored. The network is trained with images from two benchmark datasets - RESIDE and NTIRE 2021. Experimental evaluations have been conducted with several hazy images from different datasets. The performance evaluation of MSDNet is done using various metrics such as Peak-Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measurement (SSIM), Feature Similarity Metric (FSIM), Visibility Index (VI), Realness Index (RI), Fog Aware Density Evaluator (FADE), Naturalness Image Quality Evaluator (NIQE) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE). An average PSNR of 27.687, 36.25, 34.98, 20.52, and 20.97 have been obtained with images from ITS, SOTS indoor, SOTS outdoor, Dense-Haze, and NH-Haze datasets, respectively. The experimental results reveals the enhanced performance of MSDNet compared to other state-of-the-art techniques. © 2021 ACM.

关键词： Image reconstruction

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Abnormality detection and classification of macular diseases from optical coherence tomography images: Using feature space comparison 21

Abnormality detection and classification of macular diseases...

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12th Indian Conference on computer vision, Graphics and Image Processing, ICVGIP 2021

作者： Ashok, L.R. Sreeni, K.G. Computer Vision Lab Dept.Electronics and Communication College of Engineering Kerala Trivandrum India

ISBN: (纸本)9781450391276

Optical Coherence Tomography (OCT) is a non-invasive imaging technology for diagnosing various macular pathologies. It assists ophthalmologists to detect abnormalities in the retina and thereby avoid many sight-threatening conditions. However, manual detection process depends on the experience of the clinical practitioner and it is a time-consuming process. Automatic detection of abnormalities using computer algorithms allows the clinical practitioner to detect the abnormalities in a faster and accurate manner. Here, a Deep learning-based approach is proposed which can detect abnormal retinal images in various disease classes which are not limited to classes present in the training data-set. The performance of the Abnormality detector is tested on UCSD, Kaggle & OCTID data-sets and obtained an accuracy of 99.80%, 99.90% & 94.59%, respectively. The performance of Classifier is also tested on UCSD & Kaggle data-sets and obtained an accuracy of 97.7%, 99.79%, respectively. © 2021 ACM.

关键词： Optical tomography

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An Automated Performance Evaluation of the Newborn Life Support Procedure

An Automated Performance Evaluation of the Newborn Life Supp...

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Annual Symposium on Reliability and Maintainability (RAMS)

作者： Alfian Tan Joy Egede Rasa Remenyte-Prescott Michel Valstar Don Sharkey Resilience Engineering Research Group University of Nottingham UK Computer Vision Lab University of Nottingham UK Computer Vision Lab & BlueSkeyeTM AI University of Nottingham Innovation Park UK BlueSkeye AI Centre for Perinatal Research Queen's Medical Centre University of Nottingham UK

This research is conducted to develop an automated action recognition method to evaluate the performance of the Newborn Life Support (NLS) procedure. It will be useful to find deviations in the procedure, such as missing steps and incorrect actions, which will reflect the reliability of the performing protocol. This method is also part of the work towards its integration with the NLS reliability model. A combination of image segmentation and action classification methods is used. The U-net Deep Learning model is trained to do segmentation on 18 objects. Every 150 consecutive segmented video frames are then grouped for action analysis. Four types of handcrafted features are extracted from every grouped image. A training strategy using traditional Machine Learning models is developed to deal with an imbalanced dataset, as well as to reduce the complexity of the system. The predicted action segment is visually examined to make sure of its practicality. Results show that the NLS first step of wet towel removal was correctly recognized in 23 of 23 videos (52.2%), indicating the potential usefulness of the model in determining if this critical action is performed correctly and at the right time.

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L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lidars and Event Cameras

L2E: Lasers to Events for 6-DoF Extrinsic Calibration of Lid...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Kevin Ta David Bruggemann Tim Brödermann Christos Sakaridis Luc Van Gool Computer Vision Lab ETH Zurich Zurich Switzerland Department of Electrical Engineering KU Leuven Leuven Belgium

As neuromorphic technology is maturing, its application to robotics and autonomous vehicle systems has become an area of active research. In particular, event cameras have emerged as a compelling alternative to frame-based cameras in low-power and latency-demanding applications. To enable event cameras to operate alongside staple sensors like lidar in perception tasks, we propose a direct, temporally-decoupled extrinsic calibration method between event cameras and lidars. The high dynamic range, high temporal resolution, and low-latency operation of event cameras are exploited to directly register lidar laser returns, allowing information-based correlation methods to optimize for the 6- DoF extrinsic calibration between the two sensors. This paper presents the first direct calibration method between event cameras and lidars, removing dependencies on frame-based camera intermediaries and/or highly-accurate hand measurements. Code: https://***/kev-in-ta/12e

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Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus

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Biomedical engineering Frontiers 2022年第1期3卷 1-13页

作者： Angela Zhang Amil Khan Saisidharth Majeti Judy Pham Christopher Nguyen Peter Tran Vikram Iyer Ashutosh Shelat Jefferson Chen B.S.Manjunath Vision Research Laboratory Electrical and Computer EngineeringUniversity of CaliforniaSanta BarbaraSanta BarbaraCAUSA Chen Lab Department of NeurosurgeryUniversity of CaliforniaIrvine Medical CenterOrangeCAUSA Santa Barbara Cottage Hospital Santa BarbaraCAUSA

Objective and Impact *** propose an automated method of predicting Normal Pressure Hydrocephalus(NPH)from CT scans.A deep convolutional network segments regions of interest from the *** regions are then combined with MRI information to predict *** our knowledge,this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for *** to their low cost and high versatility,CT scans are often used in NPH *** well-defined and effective protocol currently exists for analysis of CT scans for ***’index,an approximation of the ventricle to brain volume using one 2D image slice,has been proposed but is not *** proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting *** propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these *** segmentation and network features are used to train a model for NPH *** method outperforms the current state-of-the-art by 9 precision points and 29 recall *** segmentation model outperforms the current state-of-the-art in segmenting the ventricle,gray-white matter,and subarachnoid space in CT *** experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process,and network properties can increase NPH prediction accuracy.

关键词： diagnosis network Connectivity

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CodePhys: Robust Video-Based Remote Physiological Measurement Through Latent Codebook Querying

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Chu, Shuyang Xia, Menghan Yuan, Mengyao Liu, Xin Seppanen, Tapio Zhao, Guoying Shi, Jingang Xi'an Jiaotong University School of Software Engineering Xi'an China Tencent Ai Lab Shenzhen China Lappeenranta-Lahti University of Technology Lut Computer Vision and Pattern Recognition Laboratory Lappeenranta53850 Finland University of Oulu Center for Machine Vision and Signal Analysis Finland

Remote photoplethysmography (rPPG) aims to measure non-contact physiological signals from facial videos, which has shown great potential in many applications. Most existing methods directly extract video-based rPPG features by designing neural networks for heart rate estimation. Although they can achieve acceptable results, the recovery of rPPG signal faces intractable challenges when interference from real-world scenarios takes place on facial video. Specifically, facial videos are inevitably affected by non-physiological factors (e.g., camera device noise, defocus, and motion blur), leading to the distortion of extracted rPPG signals. Recent rPPG extraction methods are easily affected by interference and degradation, resulting in noisy rPPG signals. In this paper, we propose a novel method named CodePhys, which innovatively treats rPPG measurement as a code query task in a noise-free proxy space (i.e., codebook) constructed by ground-truth PPG signals. We consider noisy rPPG features as queries and generate high-fidelity rPPG features by matching them with noise-free PPG features from the codebook. Our approach also incorporates a spatial-aware encoder network with a spatial attention mechanism to highlight physiologically active areas and uses a distillation loss to reduce the influence of non-periodic visual interference. Experimental results on four benchmark datasets demonstrate that CodePhys outperforms state-of-the-art methods in both intra-dataset and cross-dataset settings. © 2025 IEEE.

关键词： Heart

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A Deep Learning-Based Approach to Detect Correct Suryanamaskara Pose

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SN computer Science 2022年第5期3卷 337页

作者： Bhaumik, Ujjayanta Singh, Koushlendra Kumar Akbari, Akbar Sheikh Bajpai, Manish Kumar University College London United Kingdom Machine Vision and Intelligence Lab Department of Computer Science and Engineering National Institute of Technology Jamshedpur India School of Computing Creative Technologies and Engineering at Leeds Beckett University Leeds United Kingdom IIITDM Jabalpur India

We present a technique to analyse Suryanamaskar poses using keypoint estimation and statistical analysis. The proposed approach uses a trained model based on COCO keypoint detection dataset and uses it to determine keypoints in yoga poses. Our work uses the keypoint detection to suggest a self yoga correction system. A novel dataset, Surya-yoga, containing 10000 Suryanamaskara poses has been generated and made publicly available. The model presented in this paper performed better on the COCO dataset and combined COCO and Surya-yoga dataset when tested using part affinity fields. The work also presents an analytical method of distinguishing different Suryanamaskar poses alongside deep learning methods. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Keypoint detection Pose detection Suryanamaskar Yoga

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