2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)最新文献_第5页

Estimation of Leaf Area in Bell Pepper Plant using Image Processing techniques and Artificial Neural Networks 利用图像处理技术和人工神经网络估算甜椒叶面积

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576778

Vahid Mohammadi, S. Minaei, A. Mahdavian, M. Khoshtaghaza, P. Gouton

{"title":"Estimation of Leaf Area in Bell Pepper Plant using Image Processing techniques and Artificial Neural Networks","authors":"Vahid Mohammadi, S. Minaei, A. Mahdavian, M. Khoshtaghaza, P. Gouton","doi":"10.1109/ICSIPA52582.2021.9576778","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576778","url":null,"abstract":"Measurement and estimation of physical properties of plant leaves have always been considered as important requirements for monitoring and optimizing of plant growth. This study aimed at utilization of image processing and artificial intelligence techniques for non-invasive and non-destructive estimation of bell pepper leaves properties in the first month of growth. Physical properties of bell pepper plant leaves were extracted from RGB images. The algorithm makes use of gradient magnitude and watershed image. Leaf area as the most important index of growth was estimated as a function of other physical parameters including leaf length, width, perimeter etc. Using stereo imaging, the leaf distance from the camera was measured and applied in pixel-wise calculations. Artificial neural networks (ANN) were trained based on a database of actual values of leaf properties (i.e. 311 bell-pepper plant leaves). The success rate of the developed algorithm for detection and separation of leaves was 84.32%. The Multilayer Perceptron (MLP) network could successfully estimate the leaf area values with a validation performance of 0.912.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133472460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification of the Writer of Historical Documents via Geometric Modeling of the Handwriting 用笔迹几何造型鉴定历史文献作者

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576800

Dimitris Arabadjis, C. Papaodysseus, A. R. Mamatsis

{"title":"Identification of the Writer of Historical Documents via Geometric Modeling of the Handwriting","authors":"Dimitris Arabadjis, C. Papaodysseus, A. R. Mamatsis","doi":"10.1109/ICSIPA52582.2021.9576800","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576800","url":null,"abstract":"In this work, a generic framework is developed that simultaneously embeds 2D shapes’ registration, comparison and grouping, via the assumption that shapes of the same class are distorted level-sets of the same implicit function. The corresponding system is developed so as to deal with the automatic classification of documents according to their writers. The data that the system processes are the realizations of the individual characters, which are mutually aligned per document and per character, modulo affine transformations, and then reduced to a single representative shape. Stationarity conditions of these representatives are then used to statistically test the hypothesis that different documents bear a single representative. The considered documents are grouped according to their writer, by determining the maximal groups that also maximize the joint probability of the classification, computed over the characters, which occur in all documents. We have tested the system on 26 pages of Byzantine manuscripts that preserve Iliad. The computed classification of these pages in 4 writers has been independently verified by expert scientists.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122184483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Smart Flight Controller based on Reinforcement Learning for Unmanned Aerial Vehicle (UAV) 基于强化学习的无人机智能飞行控制器

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576806

F. Khan, M. N. Mohd, R. M. Larik, Muhammad Danial Khan, Muhammad Inam Abbasi, Susama Bagchi

{"title":"A Smart Flight Controller based on Reinforcement Learning for Unmanned Aerial Vehicle (UAV)","authors":"F. Khan, M. N. Mohd, R. M. Larik, Muhammad Danial Khan, Muhammad Inam Abbasi, Susama Bagchi","doi":"10.1109/ICSIPA52582.2021.9576806","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576806","url":null,"abstract":"Traditional flight controllers consist of Proportional Integral Derivates (PID), that although have dominant stability control but required high human interventions. In this study, a smart flight controller is developed for controlling UAVs which produces operator less mechanisms for flight controllers. It uses a neural network that has been trained using reinforcement learning techniques. Engineered with a variety of actuators (pitch, yaw, roll, and speed), the next-generation flight controller is directly trained to control its own decisions in flight. It also optimizes learning algorithms different from the traditional Actor and Critic networks. The agent gets state information from the environment and calculates the reward function depending on the sensors data from the environment. The agent then receives the observations to identify the state and reward functions and the agent activates the algorithm to perform actions. It shows the performance of a trained neural network consisting of a reward function in both simulation and real-time UAV control. Experimental results show that it can respond with relative precision. Using the same framework shows that UAVs can reliably hover in the air, even under adverse initialization conditions with obstacles. Reward functions computed during the flight for 2500, 5000, 7500 and 10000 episodes between the normalized values 0 and −4000. The computation time observed during each episode is 15 micro sec.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128918875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Nom Document Background Removal Using Generative Adversarial Network 使用生成对抗网络的Nom文档背景去除

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576764

Loc Ho, S. Tran, Dinh Dien

引用次数: 0

A Study on Staircase Artifacts in Total Variation Image Restoration 全变分图像复原中阶梯伪影的研究

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-09-13 DOI: 10.1109/ICSIPA52582.2021.9576763

T. Adam, M. Hassan, R. Paramesran

{"title":"A Study on Staircase Artifacts in Total Variation Image Restoration","authors":"T. Adam, M. Hassan, R. Paramesran","doi":"10.1109/ICSIPA52582.2021.9576763","DOIUrl":"https://doi.org/10.1109/ICSIPA52582.2021.9576763","url":null,"abstract":"The total variation (TV) regularization is used in various image processing domains such as image super-resolution, reconstruction, compressed sensing, and restoration mainly due to its edge-preserving capabilities. However, the main problem when using the TV regularization is the staircase artifacts. For image restoration, the staircase artifacts manifest themselves by producing a smeared and blocky restored image, especially when the noise level is high. This problem has been a long-standing problem, and various improvements to TV regularization have been proposed. This paper studies the effects of the staircase artifacts produced by two different noises; Gaussian noise and salt-and-pepper noise. For this purpose, we compare three well-known algorithms, the alternating direction method of multipliers (ADMM), alternating minimization (AM), and accelerated AM, and observe the effects of staircase artifacts produced between the three algorithms. As a by-product, the accelerated AM tested for the salt-and-pepper noise can be seen as a new extension of the existing accelerated AM method. Results show that it is interesting to study further the effects of different types of noise and the algorithms to mitigate the staircase artifacts produced.","PeriodicalId":326688,"journal":{"name":"2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121104895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Deep Learning Radio Frequency Signal Classification with Hybrid Images 基于混合图像的深度学习射频信号分类

2021 IEEE International Conference on Signal and Image Processing Applications (ICSIPA) Pub Date : 2021-05-19 DOI: 10.1109/ICSIPA52582.2021.9576786

Hilal Elyousseph, M. Altamimi

引用次数: 2