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Compressive Hyperspectral Imaging and Super-resolution

Compressive Hyperspectral Imaging and Super-resolution

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international conference on image, vision and Computing (ICIVC)

作者： Han Yuan Fengxia Yan Xinmeng Chen Jubo Zhu College of Liberal Arts and Sciences National University of Defense Technology Changsha Hunan China PLA 91604 91550 Dalian Liaoning China

ISBN: (纸本)9781538649923

Coded aperture snapshot spectral imager (CASSI) has been a popular spectral imaging architecture for its ability of capturing hyperspectral images with high temporal resolution. However, such snapshot imaging system entails a large sacrifice in the spatial resolution of the data cube, since only a small amount of light gets into the imager during one snapshot. Also, the spatial resolution of the CASSI system is limited by the pixel size (and amount) of the detector, while it is difficult to fabricate a dense detector with small pixel size, especially for infrared spectral bands. Super-resolution is an advanced post-processing technique to alleviate such problem by exploiting the prior information of the image. In this letter, we try to realize image super-resolution from the perspective of developing new form of measurements by taking advantage of a modified CASSI system equipped with a coded aperture with higher spatial resolution than the detector, merging the SR model into the hardware configuration. Then the original data cube can be reconstructed from lower resolution measurements, thus the super-resolution is realized during the compressive sensing reconstruction process. The new system can be achieved based on the classical CASSI architecture in two dual ways, one by replacing the coded aperture with a higher resolution one and the other by substituting the focal plane array (FPA) detector with a lower resolution one. The experiments show that, we can recover images of higher quality with the first modification of CASSI system above, simply using a higher resolution coded aperture.

关键词： Spatial resolution Detectors Apertures Hyperspectral imaging image reconstruction Imaging

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Edge Detection Technique by Histogram processing with Canny Edge Detector

Edge Detection Technique by Histogram Processing with Canny ...

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IEEE international conference on Knowledge Innovation and Invention (ICKII)

作者： Md. Atiqur Rahman Md Faizul Ibne Amin Mohamed Hamada School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu City Fukushima Japan

ISBN: (数字)9781728193335

ISBN: (纸本)9781728193342

Edge detection is one of the significant properties of many computer vision systems in image recognition, enhancement, compression, restoration, and so on. Though many research results were published, researchers still try to develop a more efficient edge detection algorithm that meets the current needs. We propose an image pre-processing based edge detection technique using canny edge detector. The main problem of canny edge detector is that it cannot identify the edges which are slightly vague due to the Gaussian smoothing. In this study, we solve the problem using the histogram processing of an image before using the canny edge detector. The result demonstrates that the proposed approach gives better outcomes compared to the state-of-the-art edge detectors.

关键词： image edge detection Detectors Laplace equations Smoothing methods Filtering algorithms Histograms Kernel

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Enhancing Human-computer Interaction: Hand Detection for Air Writing Utilizing NumPy and OpenCV

Enhancing Human-Computer Interaction: Hand Detection for Air...

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Technological Advancements in Computational Sciences (ICTACS), international conference on

作者： Pranjal Shukla Prasenjit Das Department of CSE Chandigarh University Mohali Punjab India

In recent years, there has been a remarkable increase in interest and challenges in image processing and pattern recognition, specifically in the context of air writing. This exciting research area has significant potential to advance automation processes and improve human-machine interfaces in various applications. The emergence of faster computers, affordable high-performance video cameras, and the need for automated analysis of videos has led to an increase in the popularity of object tracking, a critical task in computer vision. The process of video analysis typically encompasses object detection, tracking, and behavior analysis. Object tracking involves four main aspects choosing the suitable object representation, selecting features for tracking, detecting the object, and tracking the object. Object tracking algorithms find applications in different domains, including vehicle navigation, video indexing, and surveillance that are automated. The objective of the paper is to create a software application for smart wearable devices that utilizes computer vision to track finger gestures in the air, functioning as a motion-to-text converter for air-writing. The technology will facilitate communication for people by enabling them to generate text for multiple purposes, like sending emails and messages, through intermittent gestures. This is a productive means of communication that curbs the usage of laptops and mobiles, making it particularly beneficial for individuals who are deaf.

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Diabetic Retinopathy Identification using Deep Believe Network 3

Diabetic Retinopathy Identification using Deep Believe Netwo...

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3rd international conference on Computing and Applied Informatics 2018, ICCAI 2018

作者： Syahputra, M.F. Rahmah, M. Jaya, I. Andayani, U. Abdullah, D. Sriadhi, S. Lalusu, E.Y. Tongko, M. Cahya, B.D. Fardian, N. Department of Information Technology Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Informatics Universitas Malikussaleh Aceh Utara Indonesia Department of Electrical Engineering Universitas Negeri Medan Indonesia Faculty of Public Health Universitas Tompotika Luwuk Banggai Indonesia Department of Nutrition Universitas Malikussaleh Aceh Utara Indonesia

Diabetic retinopathy is an eye disease caused by swelling in blood vessels. If it gets worse the blood vessels will rupture and this is the main factor of blindness. This disease is rarely found in children and symptoms can be seen during puberty. One way to find out this disease is a wide and comprehensive pupil jerky eye examination performed by an ophthalmologist manually. Manual examination takes a long time and can cause misidentification because the similarity of characteristics of diabetic retinopathy is difficult to see directly. Because of this problem a method is needed that can facilitate the eye doctor in identifying diabetic retinopathy by simply inserting a retinal image into the system. The method proposed in this study is Deep Belief Network (DBN). The steps taken before identification are the image of the retina undergoing pre-processing in the form of grayscale, median filter, contrast stretching, morphological close operation and feature extraction using the Grey level Counselling Matrix (GLCM). In this study it was shown that the proposed method was able to identify diabetic retinopathy with 84% accuracy, sensitivity 93%, and specificity 70%. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Blood vessels

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computer vision assisted Deep Learning based Sitting Posture Suggestion System

Computer Vision assisted Deep Learning based Sitting Posture...

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Innovation in Technology (INOCON), IEEE international conference for

作者： Ghanashyam Vagale GK Prajwal Rohit Kumar P. Padma Priya Dharishini Department of Computer Science and Engineering Ramaiah University of Applied Sciences Bangalore India

This paper presents an innovative framework that employs camera-captured visual data to detect and suggest optimal sitting postures. The framework consists of two crucial components: a video capture object and an object detection system that incorporates Deep Learning to enhance efficiency and reliability. The camera initially captures the user’s posture image, which is then subjected to video processing to extract video metadata. Subsequently, the object is created by extracting the image from the video, and the object detection algorithm is applied to provide posture recommendations. The algorithm continually monitors posture correctness and provides suggestions to improve it as necessary, ultimately benefiting the user’s daily life and mitigating potential long-term problems. Additionally, the algorithm can be further developed to recognize posture patterns and suggest corrective exercises or techniques. Furthermore, the algorithm’s efficiency can be enhanced by optimizing landmark detection for more effective outcomes. This cutting-edge framework offers immense potential for improving posture and overall health, and its development can significantly enhance the quality of life for individuals.

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Research on Intelligent Recognition of Train image Based on Machine vision

Research on Intelligent Recognition of Train Image Based on ...

引用

international conference on Cloud Computing and Internet of Things (CCIOT)

作者： Yu Zeng Jianhai Yue School of Mechanical Electronic and Control Engineering Beijing Jiaotong University Beijing China

ISBN: (数字)9781538671412

ISBN: (纸本)9781538671429

Machine vision has become a new technique in the AI field of image processing as an important and useful technique. Thus, it is significant to study image fusion on train fault. image fusion mainly studies the image data fusion technology to obtain a synthetic image including the multiple images of the same scene from many different patterns by the image sensors or the same sensor at the different time. In this paper, the images are collected form the bogie and coupler draft devices in the operation of train, and these images are merged based on wavelet transform. Compared with the weighted average fusion method about the fusion results of subjective and objective evaluation the superiority of wavelet transform is fully proved.

关键词： image fusion image edge detection Time-frequency analysis Discrete wavelet transforms Standards

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Edge Grouping by Using Orientation Information

Edge Grouping by Using Orientation Information

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1996 3rd international conference on Signal processing(ICSP’96)

作者： L.Zhang J.D'Haeyer H.Bruyland Dept.of Communication and Information Processing University of Ghent Belgium St.Pietersnieuwstraat 41B-9000 GentBelgium

Edge is an important features required by many computer vision *** crucial step of edge or line detection is *** orientation in a gray scale image provides an important clue for *** this paper,an algorithm,used for edge or grouping based on the local orientation information,is proposed. The results are shown,and compared with wellknown transform.

关键词： origin image Edge Grouping by Using Orientation Information

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image segmentation based on bat algorithm and pulse coupled neural network

Image segmentation based on bat algorithm and pulse coupled ...

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Electronic Information Technology and computer Engineering (EITCE), international conference on

作者： Qiang Lu Zhaochen Zhang Chao Yue College of Medical Information Engineering Shandong First Medical University & Shandong Academy of Medical Sciences Taian China

ISBN: (数字)9781728135847

ISBN: (纸本)9781728135854

image segmentation plays an important role in image processing and computer vision. The pulse coupled neural network has been applied in image segmentation. The pulse coupled neural network model has many parameters which have the influence on the performance. And the parameters optimization becomes a problem. Bat algorithm is inspired by the echolocation behavior of natural bats in determining their foods and also applied in many optimization problem. In this paper, the bat algorithm is used to optimize the parameters of pulse coupled neural network and the pulse coupled neural network is applied in image segmentation. The results show that the new algorithm has better performance than the threshold algorithm and otsu image segmentation algorithm.

关键词： Neural networks

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An image processing Method Reducing Road Marking Line Edge Features for Patrol Robots Identifying Road Boundaries 19

An Image Processing Method Reducing Road Marking Line Edge F...

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Proceedings of the 2019 3rd international conference on Advances in image processing

作者： Shuangye Chen Zhi Liu Beijing University of Technology Beijing China

ISBN: (纸本)9781450376754

When automatic patrol robots trying to identify road boundaries through edge detection, road marking lines on the road may cause errors. In this regard, this paper proposes a method to process images base on edge detection results, and builds a system based on the method for experiments. The experimental results show that the proposed method can effectively reduce the edge features of road markings lines while preserving the edge features of real boundaries, so that robots can identify real road boundaries.

关键词： image processing computer vision Hough transform Robotics Road recognition Edge detection

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Button Cell characterization testing technology research based on computer vision

Button Cell characterization testing technology research bas...

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3rd international conference on Mechatronics and Intelligent Materials (MIM 2013)

作者： Sun LingYu Sun LiXin Peng Ping LI Hui Zhang XiaoJun Hebei Univ Technol Sch Mech Engn Tianjin 300130 Peoples R China

ISBN: (纸本)9783037857106

The use of computer vision to detect and recognize target objects in the industrial field has high academic value. In this paper, computer vision is used to identify cell's label and detect the surface scratch. Using inter variance threshold method of image acquisition threshold processing, to obtain a better image binarization, subsequent using characterization information to establish a template, and the template using mathematical morphology operations. Object matching is achieved by matrix change of the template, in order to achieve the detection. The experiments show that this method has higher accuracy and better real-time, so online detection can be used for battery characterization.

关键词： computer vision morphology operations template match button cell

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