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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

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Multi-Frame Super-Resolution Algorithm Based on Attention Mechanism

Multi-Frame Super-Resolution Algorithm Based on Attention Me...

引用

IEEE International conference on Signal and image processing (ICSIP)

作者： Cheng Guo Juhao Jiang Qing Wang Guannan Chen College of Photonic and Electronic Engineering Fujian Normal University Fuzhou China

ISBN: (纸本)9781665446006

In the field of computer vision, image super-resolution is a difficult task, which have many applications in remote sensing, military and so on. In this paper, we introduce the convolutional block attention module (CBAM) into super-resolution problem, proposing a novel method called multi-frame super-resolution (MFSR) algorithm based on attention mechanism. Our proposed MFSR algorithm uses a three-layer CNN as benchmark and cascades CBAM at the end of each CNN block. the proposed algorithm can deliver a high-resolution output corresponding to the center (3rd) input frame. the average PSNR and SSIM of our algorithm are 33.318dB and 0.906 respectively, which outperform other MFSR algorithm.

关键词： computer vision Military computing Convolution conferences Superresolution Convolutional neural networks Task analysis

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Restoration of Digital Design Using Row and Column Major Parsing a Technique 6

Restoration of Digital Design Using Row and Column Major Par...

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6th IEEE International conference on Electronics, Computing and Communication Technologies, CONECCT 2020

作者： Rajabather, Kumaravelu Abimannan, Satheesh Kumar, thirunavukkarasu Kumari, Bhawna Kp Technologies Kanchipuram Tamil Nadu India Galgotias University School of Computer Science and Engineering Greater Noida India

ISBN: (纸本)9781728168289

We present a novel design and development of a photo electronics-based system for reading old and used punched cards and storing its binary information for transforming them into an effective image file format. the punched card's holes diameters having the sizes of 3mm, 5mm, and 5.5mm pitch. Before the adaptation of computing systems in the field of many industries, those punched cards were prepared manually without digital design sources, but those punched cards are having rich woven designs. Now, the idea is to retrieve binary information from the punched cards and store them in digital (Non-graphics) format before processing it. After processing the digital format (Non-graphics) it is converted into an effective image file format through either by Row major or Column major parsing technique. To accomplish these activities, an embedded system based device and software integration are developed. © 2020 IEEE.

关键词： ARM processors

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Local Regression Based Real-Time Traffic Sign Detection using YOLOv6

Local Regression Based Real-Time Traffic Sign Detection usin...

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Advances in Computing, Communication Control and Networking (ICAC3N), International conference on

作者： Ravinder Kaur Jitendra Singh Department of Computer Science and Engineering SRM Institute of Science & Technology Ghaziabad INDIA

Since it involves both classifying and Logisticising objects in a scene, object detection is considered one of the most challenging topics in computer vision. While YOLOv6 is an improved version of the original You Only Look Once model, it is also among the most cutting-edge deep neural network (DNN)-based object identification methods available today. DNNs have recently been proven to attain more extraordinary object identification performance than previous approaches. However, even without a strong GPU, YOLOv6 is still exceedingly fast as compared to its previously released versions and can decide the driving course approximately in real-time. Hence, YOLOv6 is suited in applications to certain embedded systems and smartphones as well which have a major underlying constaints of power usage. To improve traffic sign identification in real time, we are using YOLOv6 and an improved Logistic Regresssion (LR) based classifier in this research. the LR classifier here is used to identify traffic sign classes. We have used the indian Traffic Sign Detection Benchmark Dataset (ITSDBD) is used to train and evaluate the network whose results are then compared with similar systems proposed in the past.

关键词： Quantization (signal) Network topology Neural networks graphics processing units Object detection Real-time systems Trajectory

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A Shape Retentive Filtering Algorithm for Post-processing of Instance Contour of Cervical Cell Based on Level Set Method 6

A Shape Retentive Filtering Algorithm for Post-processing of...

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6th IEEE International conference on computer and Communications, ICCC 2020

作者： Liu, Guangqi Ding, Qinghai Ju, Moran Luo, Haibo Jin, Tianming He, Miao The Key Laboratory of Image Understanding and Computer Vision Shenyang110016 China Space Star Technology Company Ltd. Beijing100086 China

ISBN: (纸本)9781728186351

A novel filtering algorithm is proposed based on level set method (LSM) and linear time Euclidean distance transform (LET) algorithm in this paper, which has the property of shape retention and thus is suitable for post-processing of the initial contours for contacting instances in digital Pap image. As one of our contributions, we propose two new metrics based on the pixel-level average false positive rate and false negative rate that used by baseline method. A significant decrease in pixel-level average false positive rate (FP) by 62% can obtain by our proposed method. the result of quantitative and qualitative evaluation shows that our proposed shape retentive filtering algorithm (SRFA) can effectively filter out the false positive fragments of the initial instance contour of cervical cells from the ISBI-2014 dataset. © 2020 IEEE.

关键词： Numerical methods

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Object Detection and Narrator for Visually Impaired People 6

Object Detection and Narrator for Visually Impaired People

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6th IEEE International conference on Engineering, Technologies and Applied Sciences, ICETAS 2019

作者： Nasreen, Jawaid Arif, Warsi Shaikh, Asad Ali Muhammad, Yahya Abdullah, Monaisha Ilma University Karachi Pakistan Univerisity Kuala Lumpur Malaysia

ISBN: (数字)9781728140827

ISBN: (纸本)9781728140827

Machine Learning has gained attention since the introduction of high computing machines and the availability of huge amount of data also known as big data. Today, machine learning is used in many types of industries from medical image processing to autonomous car. Detecting objects in images has also become one of the important research areas and now computers are able to not only detect objects but also able to draw bounding boxes on it. this is also known as computer vision. In this paper, we proposed the implementation of computer vision machine learning algorithms to detect object and use it to aid visually impaired and blind persons. this paper explain how convolution neural network are trained on imageNet dataset that can detect objects and narrate detected objects information to the visually impairs person. this implementation can be used with any device using a camera that includes computers, tablets and mobile phones. © 2019 IEEE.

关键词： computer vision

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A Proposal of FPGA-Based Low Cost and Power Efficient Autonomous Fruit Harvester 6

A Proposal of FPGA-Based Low Cost and Power Efficient Autono...

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6th International conference on Control, Automation and Robotics (ICCAR)

作者： Nilay, Kumar Mandal, Swarnabha Agarwal, Yash Gupta, Rishabh Patel, Manthan Kumar, Sumeet Shah, Poojan Dey, Sombit Annanya Indian Inst Technol Dept Chem Kharagpur W Bengal India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur W Bengal India Indian Inst Technol Dept Elect Engn Kharagpur W Bengal India Indian Inst Technol Dept Geol & Geophys Kharagpur W Bengal India Indian Inst Technol Dept Mech Engn Kharagpur W Bengal India Indian Inst Technol Dept Aerosp Engn Kharagpur W Bengal India

ISBN: (纸本)9781728161396

In this paper, we present a power-efficient and low- cost prototype of a robotic harvester which employs multiple subsystems such as fruit detection, odometry, localization, profi- cient manipulation through computer vision, deep learning and a novel end-effector design. Fruit Plucking is performed using an end effector, and 3-degree of freedom (DOF) arm (made out of the integration of two linear actuators and a rotating platform) consolidated with a 4-wheeled differential drive mobile platform. Effective implementation of the visual processing is executed on the FPGA Fabric of the Xilinx PYNQ-Z2 Board, which accelerates Deep Neural Networks (DNNs) with improved Latency and Energy Efficiency as compared to a CPU or GPU based implementation.

关键词： autonomous vehicle autonomous fruit plucking control system object detection

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Iterative Outlier Removal Using Close-and-Compatible Neighbors for Robust Feature Correspondence

Iterative Outlier Removal Using Close-and-Compatible Neighbo...

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IEEE International conference on Signal and image processing (ICSIP)

作者： Liang Shen Pengyu Zhang Qin Xin Tian Jin Xiaotao Huang College of Electronic Science National University of Defense Technology Changsha China

ISBN: (纸本)9781665446006

Establishing reliable feature correspondence for the image matching is a fundamental problem in computer vision and remote sensing. Currently, many state-of-the-art correspondence selection methods are based on the neighborhood consensus, while the following two problems limit their performance: 1. It is difficult to select the consensus neighbors while preserving spatial smoothness. 2. the location-based consensus constraint is loose and may lead to low precision. In this paper, we propose a simple iterative outlier removal to address these two issues. It searches close-and-compatible neighbors to measure the neighborhood consensus based on the local affine frame, and works by rejecting outliers sequentially and iteratively. the proposed approach is much more robust than state-of-the-art methods when dealing with outliers and large-scale geometric transformation. Numerous experimental results demonstrate the superiority of our method.

关键词： computer vision image matching Heuristic algorithms conferences Measurement uncertainty Feature extraction Iterative algorithms

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Autonomous Face Detection System from Real-time Video Streaming for Ensuring the Intelligence Security System 6

Autonomous Face Detection System from Real-time Video Stream...

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6th International conference on Advanced Computing and Communication Systems (ICACCS)

作者： Hoque, Mohammad Ashraful Islam, thouhidul Ahmed, Tanvir Al Amin Southeast Univ Dept Comp Sci & Engn Dhaka Bangladesh Ahsanullah Univ Sci & Technol AUST Dept Comp Sci & Engn Dhaka Bangladesh

ISBN: (数字)9781728151977

ISBN: (纸本)9781728151977;9781728151960

Face detection is a popular research topic to the researchers at present in the Biometric system. the aim of this research is to develop a system which can detect human face or faces from a live video streaming and warn by giving the siren for ensuring the significant role in the surveillance security aspects. the core of this control system includes ATMega328p Microcontroller based Arduino Uno with Pan-Tilt mechanism and Open Source computer vision System (OpenCY) is the platform for the image processing system. For detecting human face or faces existing methods are Haar-Cascade, Camshift, Hausdorff distance, AdaBoost, viola jones, etc. Amongst all of this we used Haar classifier Cascade algorithm for face detection in this research.

关键词： Face detection Open CV processing Arduino ATmega328P Pan and Tilt mechanism Servo motor Haar-Cascade Face detection algorithm

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A Novel Deep Learning Framework for Diabetic Retinopathy Detection

A Novel Deep Learning Framework for Diabetic Retinopathy Det...

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conference on Information and Communication Technology (CICT)

作者： L V S K B Kasyap Varanasi Chandra Mohan Dasari Computer Science and Engineering () Indian Institute of Information Technology Sri City Chittoor Andhra Pradesh India

ISBN: (数字)9781665473125

ISBN: (纸本)9781665473132

Diabetes is one of the most dangerous diseases that is facing by many people in the world. Diabetic Retinopathy (DR) is a retinal disease that is caused by the Diabetes Mellitus (DM). DM causes the lesions on the retinal layer thereby affecting the vision. If it is not detected at the initial stages, it might lead to complete blindness and also DR is an irreversible disease. Hence, early detection of DR is inevitable to avoid the vision loss of the patients. Expeditious detection can decrease the complications of the DR thereby vision of the patient is preserved. It is a laborious task for the ophthalmologists to diagnosis DR manually, since it takes a lot more time and cost-effective. Mis-diagnosis might happen if the ophthalmologists are not skillful and experienced in detecting DR from the fundus images. Over the past two decades, deep learning has shown a significant raise in the bio-medical image processing and its niche areas obtaining the best performance. the state-of-the-art Convolutional Neural Networks (CNN) models achieved notable performance in classifying DR, but the severity levels of DR are not analyzed. To address these challenges, we propose a CNN based model that is used to analyze the fundus oculi retinal images to locate the eyeball structure and observe the presence of DR. the proposed model's hyperparameters are regulated by the transfer learning techniques for mapping the label of the images. the dataset used for training and testing the model is taken from the Kaggle that contains the retinal images and its corresponding severity in a scale. Severity level of the images is classified into five different categories from a normal to DR presented eyeball. the accuracy of the proposed model is 94.92%, proving the confident detection and prediction of the DR from the retinal images.

关键词： Deep learning Training Analytical models Retinopathy Biological system modeling Transfer learning Retina

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A Robust Pose Transformational GAN for Pose Guided Person image Synthesis 7th

A Robust Pose Transformational GAN for Pose Guided Person Im...

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7th National conference on computer vision, Pattern Recognition, image processing, and graphics, NCVPRIPG 2019

作者： Karmakar, Arnab Mishra, Deepak Indian Institute of Space Science and Technology ThiruvananthapuramKerala695547 India

ISBN: (纸本)9789811586965

Generating photorealistic images of human subjects in any unseen pose have crucial applications in generating a complete appearance model of the subject. However, from a computer vision perspective, this task becomes significantly challenging due to the inability of modelling the data distribution conditioned on pose. Existing works use a complicated pose transformation model with various additional features such as foreground segmentation, human body parsing etc. to achieve robustness that leads to computational overhead. In this work, we propose a simple yet effective pose transformation GAN by utilizing the Residual Learning method without any additional feature learning to generate a given human image in any arbitrary pose. Using effective data augmentation techniques and cleverly tuning the model, we achieve robustness in terms of illumination, occlusion, distortion and scale. We present a detailed study, both qualitative and quantitative, to demonstrate the superiority of our model over the existing methods on two large datasets. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Large dataset

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