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Object detection in dental X-ray image using 5-axis coordinate system

Object detection in dental X-ray image using 5-axis coordina...

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2021 ieee International conference on Electrical, Computer, communications and Mechatronics Engineering, ICECCME 2021

作者： Park, Jonghwan Lee, Younghoon Seoul National University of Science and Technology Department of Data Science Gongneung-ro Nowon-gu Seoul01811 Korea Republic of Seoul National University of Science and Technology Department of Industrial Engineering Gongneung-ro Nowon-gu Seoul01811 Korea Republic of

ISBN: (纸本)9781665412629

X-rayimages are critical data sources analyzing the conditions of the teeth, gums, jaws, and bone structure of the mouth. Teeth recognition is one of the most fundamental studies in image processing-based diagnosis. Most existing recognition studies only consider the 4-axis of an object detection model because they address normal object detection while standing on the ground. However, considering the shape of teeth with various orientations, the existing 4-axis based model leads to inaccurate and inefficient recognition results. Thus, in this study, we propose a 5-axis based object detection model that considers the orientation of the teeth. Based on the teeth image dataset labeled with 5-axis ground truth, our proposed method processed 5-axis annotated data with a variant of the region-based convolutional neural network (R-CNN architecture). In the experiment, our proposed method outperformed the existing 4-axis approach in qualitative and quantitative results. Based on the experimental results, we conclude that the 5-axis based recognition model proposed in this study will be an important basis for dental image-based diagnosis. © 2021 ieee.

关键词： Object detection

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SCENE-UNIFIED image TRANSLATION FOR visual LOCALIZATION

SCENE-UNIFIED IMAGE TRANSLATION FOR VISUAL LOCALIZATION

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ieee International conference on image processing (ICIP)

作者： Han, Sheng Gao, Wei Wan, Yiming Wu, Yihong Chinese Acad Sci Inst Automat NLPR Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781728163956

visual localization is a key technology in the field of 3D robot vision. One of its major difficulties lies in how to deal with the challenges brought by the appearance changes of query images and database images caused by large time spans. Many methods focus on extracting more robust features from images to deal with the impact of complex scenes. In this paper, we explore the impact of image translation on visual localization tasks in complex scenes. We propose UniGAN - a modified image translation model, fusing semantic label constraints and finer reconstruction losses, to unify images captured under different environmental conditions to a standard scene more suitable for localization tasks. To estimate the 6-DOF camera pose, a two-stage localization framework composed of image retrieval and local matching is utilized. Experiments show that our method outperforms the state-of-the-art in terms of both accuracy and robustness to environmentally sensitive scenes.

关键词： visual Localization image Translation Semantic Segmentation GANs

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Development of Context-Sensitive Formulas to Obtain Constant Luminance Perception

Development of Context-Sensitive Formulas to Obtain Constant...

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ieee Signal processing and communications Applications (SIU)

作者： Ergun Akleman Bekir Tevfik Akgün Adil Alpkocak Visual Computing and Computational Media SVPA Texas A & M University Computer Science and Engineering College of Engineering Texas A & M University College Station ABD Bilgisayar ve Bilişim Bilimleri Fakültesi Yeditepe Üniversitesi İstanbul Türkiye Bilgisayar Mühendisliği Fakültesi İzmir Bakırçay Üniversitesi Menemen Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

In this article, we present a method to develop context-sensitive luminance correction formulas to produce constant luminance perception for a foreground object. Our formulas make the foreground object slightly transparent to mix with the blurred version of the background. This mix can be used to quickly produce any desired illusion of luminance in foreground objects based on the luminance of the background. The transparency formula has only one variable; the relative size of the foreground object. We identified the general structure of the transparency formula as a function of the relative size of the foreground object. We develop a method to intuitively control the coefficients of the transparency formula. We have implemented an interactive Web-based program in Shadertoy to identify coefficients. Using this program, we determined the coefficients of the transparency formula. We also identified a simpler affine formula, which requires only two coefficients. We made our program publicly available so that anyone can access and improve it. In this article, we also explain how to intuitively change the polynomial part of the formula. Using our explanation, users change the polynomial part of the formula to obtain their own perceptively constant luminance. This can be used as a crowd-sourcing experiment to further improve the formula.

关键词： visualization Signal processing Polynomials Object recognition

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Point Cloud Completion of Space Targets Based on Generative Adversarial Mapping

Point Cloud Completion of Space Targets Based on Generative ...

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International conference on Vision, image and Signal processing (ICVISP)

作者： Yujia Wu Tongyou Peng Yejian Zhou Jia Duan College of Information Engineering Zhejiang University of Technology Hangzhou P.R.China School of Electronics and Communications Engineering Sun Yat-sen University Shenzhen P.R.China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Generating 3D point clouds from inverse synthetic aperture radar (ISAR) image sequences is crucial for assessing the operational condition of space targets. However, the imaging characteristics of ISAR often produce sparse or incomplete point clouds. In order to surmount this constraint, we propose an innovative approach for point cloud completion leveraging the inverse transformation abilities of Generative Adversarial Networks (GANs). Our approach involves pre-training the GAN on a diverse dataset of 3D point cloud models, allowing it to learn rich geometric and semantic distributions. The proposed PointGAN module enhances feature representation by leveraging ancestral node information, thereby improving the model's adaptability and completion accuracy. Furthermore, the method operates in an unsupervised manner, generating 3D point clouds through random latent encoding and eliminating dependence on manually labeled data. Experimental evaluations indicate that the proposed method outstrips current networks with respect to chamfer distance and intersection over union (IoU) metrics. Producing 3D point clouds from sequences of inverse synthetic aperture radar (ISAR) images plays a vital role in evaluating the operational conditions of space targets. Nevertheless, the nature of ISAR imaging frequently results in point clouds that are either sparse or fragmented. To overcome this limitation, we introduce an innovative approach for completing point clouds, leveraging the inverse mapping potential inherent in Generative Adversarial Networks (GANs).

关键词： Point cloud compression Solid modeling Adaptation models Three-dimensional displays Semantics Imaging Radar imaging Signal processing Generative adversarial networks Inverse synthetic aperture radar

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A Comparative Analysis of image Dehazing using image processing and Deep Learning Techniques 6

A Comparative Analysis of Image Dehazing using Image Process...

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6th ieee International conference on Communication and Electronics Systems, ICCES 2021

作者： Likhitaa, Palagiri Sai Anand, R. Amrita School of Engineering Bengaluru Department of Electrical and Electronics Engineering Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665435871

The haziness presence in atmosphere now poses to be a critical issue, especially in terms of visibility and visual quality. The major challenge faced in the present day scenario is to stitch a technique that detects the presence of haziness and to provide an efficient method to remove it as well. This process is known as image dehazing. Although different dehazing algorithms has been introduced, still better performances can be achieved which is addressed in this paper. This challenge is overcome by using a suitable classification algorithm to detect hazy images and then cleaning it to get corresponding clear image. In this method, two goals are presented and executed, Initial one be a comparative analysis between the machine learning and deep learning models is presented for the classification of hazy and clear images and then the hazy images are processed by a robust deep neural network for the removal process. In order to classify, two ML algorithms, namely, SVM and Logistic Regression are used and two CNN standard models Resnet, Vgg are also used and their performances are compared to obtain best classification algorithm for this problem. In classification model, standard CNN models outperforms Machine Learning models by a fair margin by proving to be more accurate. Similarly for dehazing also DCP image processing algorithm is implemented with a proposed CNN de-hazing network and their performances are also compared to prove the efficiency of proposed CNN model. ITS, HSTS, Indoor SOTS Dataset are used as inputs. The performance of CNN network is compared with an image processing technique called Dark Channel Prior (DCP) algorithm. Comparatively the CNN model outperforms the DCP algorithm by attaining higher image quality metrics. To evaluate the image quality, two metrics are compared, they are Structural-similarity-index (SSIM) and peak-signal-to-noise-ratio (PSNR). © 2021 ieee.

关键词： image quality

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Identification of Lung Lesions in Computed Tomography Scan images using Machine Learning Techniques

Identification of Lung Lesions in Computed Tomography Scan I...

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ieee International conference on Control System, Computing and Engineering (ICCSCE)

作者： Mohd Firdaus Abdullah Siti Noraini Binti Sulaiman Muhammad Khusairi Osman Noor Khairiah A. Karim Adi Izhar Che Ani Nurfadzilah Ahmad Centre for Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Permatang Pauh Pulau Pinang Malaysia Regenerative Medicine Cluster Advanced Medical & Dental Institute Universiti Sains Malaysia Bertam Kepala Batas Pulau Pinang Malaysia Centre for Electrical Engineering Studies Universiti Teknologi MARA Shah Alam Selangor Malaysia

ISBN: (数字)9798350364507

ISBN: (纸本)9798350364514

Lung cancer is a common cause of death among people throughout the world. Other methods for detecting lung cancer include computed tomography (CT), magnetic resonance imaging (MRI), and radiography. This method suggests CT is useful for detecting lung cancer since it is widely available, has a shorter imaging time, and is less expensive. For clinical analysis and effective preventive planning by medical authorities to lower the number of fatalities, early diagnosis of lung lesions is crucial. visual score is a typical method used by skilled radiologists to manually identify lesions on CT images. Nevertheless, the manual approach is labor-intensive, time-consuming, tedious, and visible. This research proposes a method for automatically detecting soft lung tissue lesions for CT images. To detect lung lesions from CT scans, the system employs image processing and machine learning techniques. The proposed automatic identification system divided into three stages. The first stage is image acquisition and data collection. The second stage proposed method involved designing a procedure for lung region segmentation using an image processing technique specifically for lung region for detection of lesions. The final stage explains an automated lesion identification for further classifying the lesion and non-lesion from CT scan lung images based on geometrical properties that have been calculated. Finally, a system that utilized the best of above-mentioned methods is proposed to perform the automated lesion detection. The method achieved high capability for automatically identifying lung lesions near the manually delineated lesion by radiologists with ${9 8 \%}$ accuracy. These findings show the possible used of this approach as an assisted tool for the radiologist in detecting lung lesions.

关键词： image segmentation visualization Computed tomography Magnetic resonance imaging Lung Lung cancer Manuals Machine learning Lesions Medical diagnostic imaging

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image Quality Assessment for Computer Vision Based Perception Algorithms Using Edge and Structural Features

Image Quality Assessment for Computer Vision Based Perceptio...

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ieee Region 10 International conference TENCON

作者： V G Akshaya Rithika S Lanciya Angel A K S M Amirtha Pravin Karthi Balasubramanian Suganthi Srinivasan Rakesh Rajegowda Payal Bhattacherjee Department of Electronics and Communication Engineering Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Comfort Driving Assistance Group R & D-Computer Vision Systems Valeo India Pvt.ltd Chennai

image Quality Assessment (IQA) is a critical task in image processing, computer vision, and other related fields, as it helps in evaluating the effects of various impairments on the Quality of Experience (QoE) of consumers. However, current IQA metrics may have limitations in evaluating image quality accurately for different types of images or under different conditions. To address this, a proposed approach involves calculating an overall image quality score based on linear combination of individual image quality metrics. This method considers multiple image features such as sharpness, contrast, color, and noise, and assigns weights to each feature based on its relative importance in determining overall image quality. By combining and weighting multiple image features, the proposed approach aims to provide a more comprehensive and accurate evaluation of image quality compared to using individual metrics alone. In this study, we focus on the importance of edge features and structure-based metrics in object detection and analyze existing No-Reference (NR) IQA metrics, including Just Noticeable Blur (JNB), Cumulative Probability Blur Detection (CPBD), visual Quality Assessment (VQA), Blind/Referenceless image Spatial Quality Evaluator (BRISQUE) and No-Reference Low- Light image Enhancement Evaluation (NLIEE), and propose a linear combination formula that combines these metrics to evaluate image. We evaluate the performance of the proposed metric on several datasets by calculating the linear combination of two, three, and all the five metrics. The weights assigned to each metric in the linear combination formula are determined through experimental analysis. The combined metric scores and MOS scores are then used to compute the Spearman's rank order correlation coefficient, which measures the monotonic relationship between two variables. A higher SROCC value indicates a stronger correlation between the combined metric and MOS scores and is used to evaluate the performanc

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An ISAC-ISAR Imaging and Tracking Method for Moving Target Sensing

An ISAC-ISAR Imaging and Tracking Method for Moving Target S...

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Signal, Information and Data processing (ICSIDP), ieee International conference on

作者： Shuliang Gui Haibo Peng Yuan Xu Zhiqin Zhao Luhong Fan Zengshan Tian Guangdong Institute of Electronic Information Engineering University of Electronic Science and Technology of China Dongguan China School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The integrated sensing and communication (ISAC) is a significant feature in the 6G communication network owing to its monitoring capability around the base station. Meanwhile, inverse synthetic aperture radar (ISAR) imaging technology can obtain two-dimensional image of moving targets with fixed antenna. Inspired by this, an ISAC-ISAR imaging and tracking method is proposed for blind moving target sensing in this paper. First, the ISAC-ISAR echo signal model and imaging mechanism of blind moving target are established with orthogonal frequency division multiplexing (OFDM) communication signal. Then, the fast imaging algorithm with wavenumber domain processed and tracking based on Kalman filter and Hungarian method for ISAC-ISAR blind moving target sensing are proposed, respective. Lastly, the ISAC real experiments are designed and processed based on universal software radio peripheral to demonstrate the performance and feasibility of the proposed method.

关键词： 6G mobile communication Target tracking OFDM Surveillance Imaging Transportation Radar imaging Radar tracking Integrated sensing and communication Software radio

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PIB: Prioritized Information Bottleneck Framework for Collaborative Edge Video Analytics

PIB: Prioritized Information Bottleneck Framework for Collab...

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ieee conference on Global communications (GLOBECOM)

作者： Zhengru Fang Senkang Hu Liyan Yang Yiqin Deng Xianhao Chen Yuguang Fang City University of Hong Kong Hong Kong China Shandong University Jinan China The University of Hong Kong Hong Kong China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Collaborative edge sensing systems, particularly in collaborative perception systems in autonomous driving, can significantly enhance tracking accuracy and reduce blind spots with multi-view sensing capabilities. However, their limited channel capacity and the redundancy in sensory data pose significant challenges, affecting the performance of collaborative inference tasks. To tackle these issues, we introduce a Prioritized Information Bottleneck (PIB) framework for collaborative edge video analytics. We first propose a priority-based inference mechanism that jointly considers the signal-to-noise ratio (SNR) and the camera’s coverage area of the region of interest (RoI). To enable efficient inference, PIB reduces video redundancy in both spatial and temporal domains and transmits only the essential information for the downstream inference tasks. This eliminates the need to reconstruct videos on the edge server while maintaining low latency. Specifically, it derives compact, task-relevant features by employing the deterministic information bottleneck (IB) method, which strikes a balance between feature informativeness and communication costs. Given the computational challenges caused by IB-based objectives with high-dimensional data, we resort to variational approximations for feasible optimization. Compared to TOCOM-TEM, JPEG, and HEVC, PIB achieves an improvement of up to 15.1% in mean object detection accuracy (MODA) and reduces communication costs by 66.7% when edge cameras experience poor channel conditions.

关键词： Costs Accuracy visual analytics image edge detection Collaboration Termination of employment Transform coding Sensors Servers Signal to noise ratio

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Pearl: A Fast Deep Learning Driven Compression Framework for UHD Video Delivery

Pearl: A Fast Deep Learning Driven Compression Framework for...

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ieee International conference on communications (ICC)

作者： Huang, Siqi Xie, Jiang Univ North Carolina Charlotte Dept Elect & Comp Engn Charlotte NC 28223 USA

ISBN: (纸本)9781728171227

Ultra-high-definition (UHD) videos are enjoying increased popularity in people's daily usage because of the good visual experience. However, the data size of UHD videos is 4-16 times larger of HD videos. This will bring many challenges to existing video delivery systems, such as the shortage of network bandwidth resources and longer network transmission latency. Super resolution (SR) algorithms are widely used in video delivery applications to tackle these challenges. However, applying the super resolution model on UHD videos requires much more GPU memory, as compared with HD videos, which brings a significant challenge to existing systems. In this paper, we propose a deep compression framework named Pearl, which utilizes the power of deep learning to compress UHD videos. New channel-based super resolution models are developed to overcome the GPU memory shortage problem. In pearl, instead of applying the traditional RGB-based super resolution model, three separate super resolution models are trained based on the Y, U, and V channels of UHD videos. These super resolution models are used to reconstruct a UHD video from a low-resolution video. With Pearl, super resolution algorithms can be successfully applied to UHD videos. As a result, the data size of UHD videos can be significantly reduced during network transmission. At the same time, the efficiency of video encoding and decoding can also be improved with Pearl. To the best of our knowledge, Pearl is the first deep learning driven compression framework on UHD videos. We evaluate the performance of Pearl with extensive experiments. In all considered scenarios, Pearl can compress up to 95% of video data size during the video transmission and achieve 2.4 times faster, as compared with existing systems.(1)

关键词： Deep learning visualization conferences Memory management Graphics processing units Bandwidth Encoding

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