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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Transform-Based Quality Assessment for Enhanced image 44

Transform-Based Quality Assessment for Enhanced Image

引用

conference applications of Digital image processing XLIV

作者： Voronin, V. Semenishchev, E. Zelensky, A. Tokareva, O. Cenc, Y. Agaian, S. Moscow State Univ Technol STANKIN Ctr Cognit Technol & Machine Vis Moscow Russia Don State Tech Univ Lab Math Methods Image Proc & Intelligent Comp Vi Rostov Na Donu Russia Beijing Jiaotong Univ Inst Informat Sci Beijing Peoples R China CUNY Dept Comp Sci New York NY 10021 USA

ISBN: (数字)9781510645233

ISBN: (纸本)9781510645233;9781510645226

images captured in bad weather suffer from low contrast and faint color. Recently, plenty of enhanced algorithms have been proposed to improve visibility and restore color. The goal of image quality assessment is to predict the perceptual quality for improving imaging systems' performance. We proposed a no-reference image quality enhancement measure using hypercomplex Fourier transform for color images. The main idea is that enhancing the contrast of an image would create more high-frequency content in the enhanced image than the original image. An increase in the magnitude of higher frequency coefficients indicates an enhancement in contrast to the image's luminance content. To test the performance of the proposed algorithm, the public database TID2013 is used. The Pearson rank-ordered correlation coefficient is utilized to measure and compare the proposed quality measure's performance with state-of-the-art approaches.

关键词： quality measure frequency-domain transform no-reference (NR) image quality enhanced image

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Evaluation of roundness error for cylindrical surfaces using vision system

Evaluation of roundness error for cylindrical surfaces using...

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4th International conference on Advances in Materials and Manufacturing applications (IConAMMA)

作者： Vysakh, S. Surendran, K. N. Udupa, Ganesha Amrita Vishwa Vidyapeetham Dept Mech Engn Amrita Sch Engn Kollam 690525 Kerala India

The roundness of cylindrical surfaces is mainly measured using two methods;contact and non-contact. Contact method of evaluation is widely used in industries. But nowadays the demand for the non-contact method is increasing. The roundness measuring instruments are mainly used by the automobile and manufacturing industries. This paper deals with the development of non-contact measuring instrument using machine vision to measure dimension and form error which are affordable to Indian industries and educational institutions. (c) 2020 Elsevier Ltd. Selection and peer-review under responsibility of the scientific committee of the International conference on Advances in Materials and Manufacturing applications.

关键词： image processing Roundness Minimum Zone Circle

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Ant Colony Optimization based Medical image Preservation and Segmentation 2

Ant Colony Optimization based Medical Image Preservation and...

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2nd IEEE International conference on Advanced Technologies in Intelligent Control, Environment, Computing and Communication Engineering, ICATIECE 2022

作者： Nandihal, Priya Pareek, Piyush Kumar De Albuquerque, victor Hugo Costa Madhumala, R.B. Khanna, Ashish Sunil Kumar, V. Dayananda Sagar Academy of Technology and Management Dept of Computer Science and Design Bangalore India Graduate Program in Telecommunication Engineering Federal Institute of Education Science and Technology of Ceara Fortaleza Brazil Federal University of Ceara Department of Teleinformatics Engineering Brazil Maharaja Agrasen Institute of Technology GGSIPU CSE Dept India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence & Machine Learning Bengaluru India

ISBN: (纸本)9781665493963

image segmentation is an essential component in many different types of computer vision systems. image segmentation is used in order to identify objects and boundaries within pictures. It is important to note that the success of recognition algorithms is heavily dependent on the quality of the picture segmentation, and there is yet no technique that can universally segment images. Boundary detection has a long history of being recognised as one of the most difficult challenges in the fields of image processing and pattern analysis, in particular, when it comes to applications in medical imaging. The clarity of the picture is being muddled by more information, which is the source of the problem's complexity. The creation of segmentation algorithms for medical pictures is an active field of study, and segmentation algorithms are meant to account for a variety of factors, including noise, intensity inhomogeneity, poor contrast between the lesion and the surrounding skin, and so on. There is currently no active contour model that combines the locally statistical property of being more noise-resistant, in which the intensities in the transformed domain have less overlapping in the statistics, and the locally image fitting property of extracting the local image information in order to be able to segment images with intensity inhomogeneity. This is because there is no such model. So the proposed work includes Ant colony optimization based double threshold segmentation in medical images. Performance of the proposed algorithm is more efficient, reliable and accurate than the existing algorithm. Performance parameters such as segmentation accuracy, error rate, and Dice co-efficient are used in order to conduct the evaluation of the proposed work. © 2022 IEEE.

关键词： Impulse noise

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Design and VLSI Implementation of Digital image processing applications

Design and VLSI Implementation of Digital Image Processing A...

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Science, Technology, Engineering and Management (ICSTEM), International conference on

作者： S Nikhilesh S Navin Khumar N Muthukumaran. H Midun Kumar G S Rishikesh Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore Tamil Nadu

ISBN: (数字)9798350376913

ISBN: (纸本)9798350388473

The primary factor is the food that everyone consumes on a daily basis. To check the fuel contaminated successfully, it is important to screen the fuel quality at the dissemination point itself. In water shortage regions dribble water system pours water to the foundation of the plant through tubes in drops. To keep up with the dampness level of the dirt, the valve must be opened or shut likewise. Grains in the stockpiles are ruined because of the dampness in the room. Dampness level ought to be controlled in the food stockpiles, so the food can be utilized during starvation. Artistic tiles and marbles with imperfection can't be found out physically. Customers acquire these tiles without their knowledge. They get demolished however ahead of schedule as they may be stunned. These issues have been identified through the use of digital image processing. In any case, this is a tedious cycle. In this research, a system with high accuracy and speed is proposed to solve all issues. This will assist the clients with keeping themselves out of the malicious impacts of debasement. The target of this exploration is to distinguish the corruption in food, oil and so forth. Using Very Large Scale Integration (VLSI) to alert customers to the adulteration and take every possible precaution against it.

关键词： Schedules Accuracy Digital images Oils Very large scale integration Valves Water pollution

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Comparative Study of Plant Disease Detection Techniques: A Performance-Based Review

Comparative Study of Plant Disease Detection Techniques: A P...

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Advancement in Electronics & Communication Engineering (AECE), International conference on

作者： Sarita Singh Noopur Goel Veer Bahadur Singh Purvanchal University Department of Computer Applications Veer Bahadur Singh Purvanchal University

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Agriculture is essential for human civilization, contributing to the economy and ensuring food supply. However, plant diseases can hinder growth and reduce crop yield. It is crucial to identify and categorize these diseases as soon as possible and accurately. Manual methods for disease prediction and classification may lead to inaccuracies and tedium. Therefore, implementing computerized image processing methods in agriculture can help to minimize losses and increase productivity. To detect and categorize plant illnesses on the basis of photos of sick leaves or crops, a variety of approaches, including DL and ML techniques like k-means clustering, NB and convolutional neural networks have been investigated. Even though there has been a lot of development in this area, talks and changing technologies call for ongoing improvements. The particular problem, data accessibility, and processing capacity all influence which of the conventional machine learning and deep learning approaches are the best. When a large amount of data and computational resources are available, deep learning, especially with CNNs, is generally the method of choice for picture identification and classification. This paper introduced a brief overview of the latest research on identifying and categorizes crop diseases and illness using image processing in the field of artificial intelligence. It covers the performance, assessment criteria, and findings of different approaches, aiming to keep future researchers informed about the progress in this area.

关键词： Deep learning Plant diseases Adaptation models Computer vision Reviews Computational modeling Neural networks Feature extraction Agriculture Convolutional neural networks

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Improving Myanmar image Caption Generation Using NASNetLarge and Bi-directional LSTM

Improving Myanmar Image Caption Generation Using NASNetLarge...

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Computer applications (ICCA), IEEE conference on

作者： San Pa Pa Aung Win Pa Pa Tin Lay Nwe Natural Language Processing Lab University of Computer Studies Yangon Yangon Myanmar Visual Intelligence Department Institute for Infocomm Research Singapore

The main objective of this paper is to improve the automatic Myanmar captions by learning the contents of images using NASNetLarge and Bi-LSTM model. Describing the contents of an image is a complex task for machine without human intervention. Computer vision and Natural Language processing are widely used to tackle this problem. This paper proposed a deep learning-based Myanmar image captioning system which used a NASNetLarge feature extraction model of CNN as an encoder and a deep Recurrent Neural Network (RNN) with Bi-directional Long Short-Term Memory (LSTM) as a decoder. For corpus construction, we created and annotated the Myanmar image captions corpus (consists of over 40k Myanmar sentences), which is based on Flickr8k dataset. Furthermore, two different types of segmentations such as word segmentation level and syllable segmentation level are studied in text preprocessing step. In this work, the proposed Bi-directional LSTM model is compared with LSTM, GRU as well as the baseline model. Experiments on the updated dataset is presented that all of our models using syllable segmentation give higher and comparable BLEU scores than word segmentation for Myanmar image captioning system. NASNetLarge with Bi-directional LSTM model using syllable segmentation approach achieved the highest BLEU-4 score 40.05% which is 12.5% better than word segmentation in this work and 15.67% BLEU-4 score better than our previous work.

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AI based Plant Growth Monitoring System using Computer vision

AI based Plant Growth Monitoring System using Computer Visio...

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Technology & Engineering Management conference - Asia Pacific (TEMSCON-ASPAC), IEEE

作者： Archana Bhamare vivek Upadhyay Payal Bansal Dept of Electronics & Communication Engineering Poornima University Jaipur India

ISBN: (数字)9798350384659

ISBN: (纸本)9798350384666

This research work aims to develop an AI-based plant growth monitoring system using computer vision. By leveraging computer vision algorithms and artificial intelligence techniques, the system will enable real-time and automated assessment of plant growth and health. Traditional methods for plant growth monitoring are time-consuming and labor-intensive. The proposed system will address these limitations by analyzing images of plants using image processing algorithms and extracting relevant features such as leaf area, plant height and width. machine learning algorithms will be employed to train models capable of recognizing patterns and predicting plant growth based on the extracted features. The system will be designed to accommodate different plant types and environmental conditions. By providing accurate and timely insights into plant health and growth, this AI-based plant growth monitoring system will benefit farmers, researchers, and plant enthusiasts, enabling informed decision-making for plant care and optimizing growth conditions. This research work aims to revolutionize plant growth monitoring by offering a cost-effective, scalable, and efficient solution with wide applications in agriculture, horticulture and environmental research.

关键词： Computer vision machine learning algorithms Plants (biology) Learning (artificial intelligence) Predictive models Feature extraction Prediction algorithms

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Deep Ensemble Learning with Frame Skipping for Face Anti-Spoofing

Deep Ensemble Learning with Frame Skipping for Face Anti-Spo...

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Workshops on image processing Theory, Tools and applications, IPTA

作者： Usman Muhammad Md Ziaul Hoque Mourad Oussalah Jorma Laaksonen Center for Machine Vision and Signal Analysis University of Oulu Finland Department of Computer Science Aalto University Finland

Face presentation attacks, also known as spoofing attacks, pose a substantial threat to biometric systems that rely on facial recognition systems, such as access control systems, mobile payments, and identity verification systems. To mitigate the spoofing risk, several video-based methods have been presented in the literature that analyze facial motion in successive video frames. However, estimating the motion between adjacent frames is a challenging task and requires high computational cost. In this paper, we rephrase the face anti-spoofing task as a motion prediction problem and introduce a deep ensemble learning model with a frame skipping mechanism. In particular, the proposed frame skipping adopts a uniform sampling approach by dividing the original video into video clips of fixed size. By doing so, every nth frame of the clip is selected to ensure that the temporal patterns can easily be perceived during the training of three different recurrent neural networks (RNNs). Motivated by the performance of individual RNNs, a meta-model is developed to improve the overall detection performance by combining the prediction of individual RNNs. Extensive experiments were performed on four datasets, and state-of-the-art performance is reported on MSU-MFSD (3.12%), Replay-Attack (11.19%), and OULU-NPU (12.23%) databases by using half total error rates (HTERs) in the most challenging cross-dataset testing scenario.

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Implicit Channel Learning For machine Learning applications In 6G Wireless Networks

Implicit Channel Learning For Machine Learning Applications ...

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Acoustics, Speech, and Signal processing Workshops (ICASSPW), IEEE International conference on

作者： Ahmet M. Elbir Wei Shi Kumar vijay Mishra Anastasios K. Papazafeiropoulos Symeon Chatzinotas Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg School of Information Technology Carleton University Ottawa Canada United States DEVCOM Army Research Laboratory Adelphi USA University of Hertfordshire Hatfield U. K

With the deployment of the fifth generation (5G) wireless systems gathering momentum across the world, possible technologies for 6G are under active research discussions. In particular, the role of machine learning (ML) in 6G is expected to enhance and aid emerging applications such as virtual and augmented reality, vehicular autonomy computer vision and internet of everything. This will result in large segments of wireless data traffic comprising image, video and speech. The ML algorithms process these for classification/recognition/estimation through the learning models located on cloud servers. This requires wireless transmission of data from edge devices to the cloud server. Channel estimation, handled separately from recognition step, is critical for accurate learning performance. Toward combining the learning for both channel and the ML data, we introduce implicit channel learning to perform the ML tasks without estimating the wireless channel. Here, the ML models are trained with channel-corrupted datasets in place of nominal data. Without channel estimation, the proposed approach exhibits approximately 60% improvement in image and speech classification tasks for diverse scenarios such as millimeter wave and IEEE 802.11p vehicular channels.

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Research on the Application of Generative Adversarial Networks in Aerial image Generation

Research on the Application of Generative Adversarial Networ...

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image processing, Computer vision and machine Learning (ICICML), International conference on

作者： H. X. Cai X. Y. Zhu P. C. Wen L. T. Bai R. Q. Li W. Han AVIC Xi’an Aeronautics Computing Technique Research Institute Xi’an China

ISBN: (纸本)9781665464697

Computer vision is one of the important areas and directions of deep learning research, which requires different approaches to be chosen for different fields due to the complexity and diversity of vision tasks. In the field of aviation, the existing image resources are still far from the real needs due to the influence and constraints of realistic scenes and difficulties of image acquisition. More detailed and comprehensive images can better provide reliable technical support and basis for applications, and then make more accurate decisions on problems, which requires generating more effective images to expand the data. Generative Adversarial Networks (GAN) are the fastest growing and most effective generation method in recent years, so this experiment investigates the application of GAN on aviation data, taking images of airplanes, cars and ships as examples to conduct quantitative research. The effect on the effect of GAN is studied from the perspective of image size, number of images, number of iterations, and different categories of images, in order to obtain better parameter settings for generating effective images, which provides a theoretical and experimental basis for the subsequent application of GAN in the aviation field to generate more images with similar characteristics and solve the problem of insufficient data.

关键词： Deep learning Computer vision image synthesis Computer network reliability Generative adversarial networks Complexity theory Reliability

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