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Correction of Two Human Key-Points Estimations for Medical Applications

Correction of Two Human Key-Points Estimations for Medical A...

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Pattern Recognition and Artificial Intelligence (PRAI), International conference on

作者： Hao Luo Said Ramdhan Rashid Yuru Nie Ancheng Chen Chuan Li Sophiann Wu Zhiwen Zheng Zhuzhong Cheng Benjamin Castaneda Bo Peng Chao Tian Zhe Wu School of Computer Science Southwest Petroleum University Chengdu China School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China Department of Women’s Health Sichuan Cancer Hospital Chengdu China Canyon Crest Academy Sandiego USA Institute of Omics and Applied Biotechnology Pontificia Universidad Catolica del Peru Lima Peru

Anatomical key-points recognition is essential in many medical image analyses and clinical healthcare applications. Successfully identifying these anatomical key points provides multiple advantages, such as assisting medical experts in making treatment adjustments and offering information that helps to position surgical instruments at the appropriate locations. However, manual anatomical key-point recognition is subjective, slow, and time-consuming, especially when processing many medical images in clinical institutions. To overcome these limitations, this study aims to establish the correlation between human anatomical key points based on OpenPose and Baidu AI key-point detection techniques and the truth ground anatomical key-points marked by therapists in human medical images. this relationship will help to optimize the detection performance, reduce cost, decrease human error, and accelerate the process. the Sichuan Cancer Hospital provided five whole-body scan images obtained from a clinical CT scanner. A medical expert subsequently identified 14 anatomical key points from each scan. Finally, the datasets were reconstructed into 3- dimensional volume models to visualize whole-body skin models and the skeletons. the human-annotated 14 key points were then used as ground truth compared to the computer vision techniques: OpenPose and Baidu AI. Both OpenPose and Baidu AI were found to have systematic offsets from the ground true reference points. these findings are reported in this work and can be used as a correction method.

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One Draw Attack: Fool Deep Learning Models with Modifying Fewer Pixels 6

One Draw Attack: Fool Deep Learning Models with Modifying Fe...

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2021 6th International conference on Electronic Technology and Information Science, ICETIS 2021

作者： Gu, Shuangjia Liu, Wanping Li, Zhi College of Computer Science and Engineering Chongqing University of Technology Chongqing400000 China College of Computer Science Chongqing University Chongqing400000 China

Deep learning plays the leading role in the field of image recognition, natural language processing, and speech processing. But many researches reveal that the existence of adversarial examples would make models vulnerable. Most of the researches focus on restricting norm distance which result in perturbing images too much. With examples given in this paper, we demonstrate that restricting the number of modified pixels is easier to classify in human vision instead of norm distance, meanwhile, it can get a high accuracy of non-targeted attack on VGG16, ResNet50 and DenseNet201. Our method is adding Bézier curves on original image using differential evolution algorithm (DE). through continuous evolution, kept the good individuals and eliminated the bad ones, DE can guide the search to the best solution. With DE, we do not need to know the gradient information about models, therefore, our method is robust on different models. When we add more Bézier curves on original image, we can get a higher accuracy which also shows that our method is effective. We find that the distribution of prediction labels focuses on a few categories, which means some labels are easier to attack than the most others. We also think the encode order about labels has an influence on the distribution of misclassified labels. © Published under licence by IOP Publishing Ltd.

关键词： Pixels

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Object 6DoF Pose Estimation for Power Grid Manipulating Robots 11th

Object 6DoF Pose Estimation for Power Grid Manipulating Robo...

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11th International conference on image and graphics, ICIG 2021

作者： Du, Shan Zhang, Xiaoye Li, Zhongliang Yue, Jingpeng Zou, Qin School of Computer Science Wuhan University Wuhan China Electric Power Research Institute of Guangdong Power Grid Co. Ltd. Guangzhou China China Southern Power Grid Technology Co. Ltd. GuangZhou China

ISBN: (纸本)9783030873578

this paper introduces a six degree-of-freedom (6DoF) pose estimation method for manipulating robots to construct a robust machine-vision system. Generally, 2DoF results obtained by traditional object detectors cannot meet the requirements of manipulating operations, where the posture of targets are additionally needed. Meanwhile, due to the sensitivity to light and the limitation to distance, the depth sensor of RGB-D cameras could not always be reliable. To overcome these problems, we study 6DoF pose estimation from a single RGB image. To reduce the complexity and computation, we divide the task into four stages, i.e., data collection and pre-processing, instance segmentation, keypoints prediction, and 2D-to-3D projection. We build the model with deep neural networks, and test it in practical manipulating tasks. the experimental results demonstrate the high accuracy and practicality of our method. © 2021, Springer Nature Switzerland AG.

关键词： Robots

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Video Analysis and Deep Learning for Contact Rate Estimation in Public Places and Mass Gatherings in Saudi Arabia

Video Analysis and Deep Learning for Contact Rate Estimation...

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International conference on Information Technology (INCIT)

作者： Asma Alghamdi Suha Bako Zahrah Al-Safwan Sultanah M. Alshammari Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Smart Environment Research King Abdulaziz University Jeddah Saudi Arabia

Contact patterns and social mixing are crucial parameters in studying disease epidemics. Various approaches, such as diaries, surveys, and interviews, were used to collect data about contact rates and interactions between individuals in different settings. Advances in technology and artificial intelligence approaches have provided new methods to determine contact rates, including wearable sensors and contact-tracing smartphone apps. this study describes an automated approach to estimating contact rates in public places and mass gatherings in Saudi Arabia. the proposed model integrates computer vision, image processing, and deep learning algorithms to compute the average contact rate from video recordings. Our results showed that the proposed model estimated the number and duration of contacts per individual from video recordings.

关键词： Deep learning Computational modeling Urban areas Estimation Detectors Data models Planning

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Deep Learning Envisioned Accident Detection System 6th

Deep Learning Envisioned Accident Detection System

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6th International conference on Recent Trends in image processing and Pattern Recognition, RTIP2R 2023

作者： Alam, Intekhab Verma, Ayush Khari, Manju Jawaharlal Nehru University New Delhi110067 India

ISBN: (纸本)9783031530814

the conflict between computational overhead and detection accuracy affects nearly every Automated Accident Detection (AAD) system. Although the accuracy of detection and classification approaches has recently improved dramatically, the systems’ high computational resource requirements make them unsuitable for deployment in situations when immediate feedback is required. this paper suggests a strategy for developing an accurate, cost-efficient automatic collision identification system that can be deployed with a minimal amount of hardware. the AAD system is divided into three key phases—Detection, Tracking, and Classification and offers computationally reduced intensive algorithms for each of these stages. For the detection phase, YOLOv3, it uses a deep learning model that has been trained through knowledge distillation. Its accuracy is on par with that of YOLO (You-Only-Look-Once). On the basis of the information inference and data mining techniques COCO (Common Objects in Context) dataset, YOLOv3 surpasses all other identification techniques considering runtime complexity, averaging an astounding 30 frames/second on a low-end system. It attains an Average Precision (AP) mark of 44.8. For the tracking phase, the system makes use of SORT (Simple Online Real-time Tracking). Since a radial basis kernel with a support vector machine works efficiently, an area under the curve (or AUC) score of 0.92 is achieved. Alongside this, this paper examines a number of machine-learning techniques for the classification stage. © 2024, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： computer vision

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HDRVideo-GAN: Deep generative HDR video reconstruction 21

HDRVideo-GAN: Deep generative HDR video reconstruction

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Anand, Mrinal Harilal, Nidhin Kumar, Chandan Raman, Shanmuganathan Indian Institute of Technology Gujarat Gandhinagar India University of Colorado BoulderCO United States

ISBN: (纸本)9781450391276

High dynamic range (HDR) videos provide a more visually realistic experience than the standard low dynamic range (LDR) videos. Despite having significant progress in HDR imaging, it is still a challenging task to capture high-quality HDR video with a conventional off-the-shelf camera. Existing approaches rely entirely on using dense optical flow between the neighboring LDR sequences to reconstruct an HDR frame. However, they lead to inconsistencies in color and exposure over time when applied to alternating exposures with noisy frames. In this paper, we propose an end-to-end GAN-based framework for HDR video reconstruction from LDR sequences with alternating exposures. We first extract clean LDR frames from noisy LDR video with alternating exposures with a denoising network trained in a self-supervised setting. Using optical flow, we then align the neighboring alternating-exposure frames to a reference frame and then reconstruct high-quality HDR frames in a complete adversarial setting. To further improve the robustness and quality of generated frames, we incorporate temporal stability-based regularization term along with content and style-based losses in the cost function during the training procedure. Experimental results demonstrate that our framework achieves state-of-the-art performance and generates superior quality HDR frames of a video over the existing methods. © 2021 ACM.

关键词： Optical flows

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Few-shot classification without forgetting of event-camera data 21

Few-shot classification without forgetting of event-camera d...

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12th indian conference on computer vision, graphics and image processing, ICVGIP 2021

作者： Goyal, Anik Biswas, Soma Indian Institute of Science Karnataka Bangalore India

ISBN: (纸本)9781450391276

Event-based cameras can capture changes in brightness in the form of asynchronous events, unlike traditional cameras, which has sparked tremendous interest due to their wide range of applications. In this work, we address for the first time in literature, the task of few-shot classification of event data without forgetting the base classes on which it has been initially trained. this not only relaxes the constraint of data availability from all possible classes before the initial model is trained, but also the constraint of capturing large amounts of training data for each of the classes we want to classify. the proposed framework has three main stages: First, we train the base classifier by augmenting the original event data using a data mixing technique, so that the feature extractor can better generalize to unseen classes. We also utilize an adaptive semantic similarity between the classifier weights. this guarantees that the margin between similar classes is greater than that between dissimilar classes which in turn reduces confusion between similar classes. Second, weight imprinting is employed to learn the initial classifier weights for the new classes with few examples. Finally, we finetune the entire framework using a class-imbalance aware loss in an end-to-end manner. this is accomplished by converting the event data via a series of differentiable operations, which are then fed into our network. Extensive experiments on few-shot versions of two standard event-camera datasets justify the effectiveness of the proposed framework. We believe that this study will serve as a solid foundation for future work in this critical field. © 2021 ACM.

关键词： Cameras

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An improved SSD for small target detection 2021

An improved SSD for small target detection

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6th International conference on Multimedia and image processing, ICMIP 2021

作者： Li, Xiang Luo, Haibo Key Laboratory of Opt-Electronic Information Processing Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China The Key Laboratory of Image Understanding and Computer Vision Shenyang Liaoning110016 China

ISBN: (纸本)9781450389167

SSD is one of heuristic one-stage target detection approaches. Although it has got impressive results in general target detection, it still struggles in small-size object detection and precise location. In this paper, we proposed an improved SSD which forces on the small-size target detection. We include a shallow and high resolution feature into the hierarchical detection feature which are used for prediction. then, we fuse the detection features (including the shallow and high resolution one) as a feature pyramid through some convolution layers and unsample operations to pass information from deep features to the shallow ones, aiming to enrich the semantic information of the shallow features. To make the network easier to converge, we add a L2 normalization to the bottom detection feature of the feature pyramid to make a norm balance between each pyramid feature. the experimental results on the VEDAI dataset show that the proposed method has obtained impressive progress than the original SSD for the small targets detection. © 2021 ACM.

关键词： Object detection

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Optimization of the Process of Parameter Adjustment: image processing as a Case Study 6

Optimization of the Process of Parameter Adjustment: Image P...

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6th International conference on Optimization and Applications, ICOA 2020

作者： Qaffou, Issam LISI Laboratory Faculty of Science Semlalia Cadi Ayyad University Computer Science Department Marrakech Morocco

ISBN: (纸本)9781728166544

Selecting optimal parameters' values in a computer vision task is a challenge for users because of the multitude of possible cases. this paper discusses the use of artificial intelligence methods and techniques to optimize the process of finding the best values for parameters in image processing applications. We propose an automatic method based on multi-agent system and reinforcement learning. the proposed solution is proved by an experimentation of object extraction from an image. © 2020 IEEE.

关键词： Multi agent systems

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Six Convolutional Layered Deep Convolutional Neural Network Based Real Time Single image Super Resolution

Six Convolutional Layered Deep Convolutional Neural Network ...

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Smart Electronics and Communication (ICOSEC), International conference on

作者： M. Shyamala Devi J. Arun Pandian Rahul Kumar thakur Vinod Babu Vemuri Javvaji Naga Bharath Computer Science & Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India School of Information Technology and Engineering Vellore Institute of Technology Vellore India

the super-resolving images investigation has advanced in recent years through the use of cutting-edge Deep Learning-based architectures. Numerous previously documented super-resolution-based solutions need the most advanced and top-tier graphics processing Units (GPUs) to execute picture super-resolution. With the growing advancement of technology, this research focuses on suggesting the needed quantity of convolutional layers for creating the real time super resolution of single image using Convolutional Neural Network. the proposed Six Convolutional Layered Deep Convolutional Neural Network (6CL-DCNN) to predict the super resolution of images with high accuracy and ideal Peak Signal-to-Noise Ratio. the dataset extracted from ***(http://***/Research/Projects/CS/vision/grouping/BSR/BSR_***). the dataset contains 800 images with the combination of low resolution and high-resolution images having the image resolution of 300 * 300 pixels. the proposed 6CL-DCNN built with single input layer followed by six convolutional layers followed by single lambda optimized output layer that predicts the super resolution of both high resolution images and low resolution images. Python was used through 500 training iterations and a 64-bit block size on a NVidia Geforce Tesla V100 GPU workstation. the processed low resolution images and high-resolution images are applied with proposed 6CL-DCNN model and also the performance is compared using Peak Signal to Noise Ratio with other optimized output layers. Experimental results shows that the proposed model 6CL-DCNN shows the maximum Peak Signal to Noise Ratio of 48 dB when compared to other optimized output layers.

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