Relevant bibliographies by topics / Visual coverage estimation

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Visual coverage estimation'

Author: Grafiati
Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Visual coverage estimation"

1

Karakaya, Mahmut, and Hairong Qi. "Coverage estimation for crowded targets in visual sensor networks." ACM Transactions on Sensor Networks 8, no. 3 (July 2012): 1–22. http://dx.doi.org/10.1145/2240092.2240100.

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Osorio, Kavir, Andrés Puerto, Cesar Pedraza, David Jamaica, and Leonardo Rodríguez. "A Deep Learning Approach for Weed Detection in Lettuce Crops Using Multispectral Images." AgriEngineering 2, no. 3 (August 28, 2020): 471–88. http://dx.doi.org/10.3390/agriengineering2030032.

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Weed management is one of the most important aspects of crop productivity; knowing the amount and the locations of weeds has been a problem that experts have faced for several decades. This paper presents three methods for weed estimation based on deep learning image processing in lettuce crops, and we compared them to visual estimations by experts. One method is based on support vector machines (SVM) using histograms of oriented gradients (HOG) as feature descriptor. The second method was based in YOLOV3 (you only look once V3), taking advantage of its robust architecture for object detection, and the third one was based on Mask R-CNN (region based convolutional neural network) in order to get an instance segmentation for each individual. These methods were complemented with a NDVI index (normalized difference vegetation index) as a background subtractor for removing non photosynthetic objects. According to chosen metrics, the machine and deep learning methods had F1-scores of 88%, 94%, and 94% respectively, regarding to crop detection. Subsequently, detected crops were turned into a binary mask and mixed with the NDVI background subtractor in order to detect weed in an indirect way. Once the weed image was obtained, the coverage percentage of weed was calculated by classical image processing methods. Finally, these performances were compared with the estimations of a set from weed experts through a Bland–Altman plot, intraclass correlation coefficients (ICCs) and Dunn’s test to obtain statistical measurements between every estimation (machine-human); we found that these methods improve accuracy on weed coverage estimation and minimize subjectivity in human-estimated data.
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Xu, Mingliang, Qingfeng Li, Jianwei Niu, Hao Su, Xiting Liu, Weiwei Xu, Pei Lv, Bing Zhou, and Yi Yang. "ART-UP: A Novel Method for Generating Scanning-Robust Aesthetic QR Codes." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 1 (April 16, 2021): 1–23. http://dx.doi.org/10.1145/3418214.

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Quick response (QR) codes are usually scanned in different environments, so they must be robust to variations in illumination, scale, coverage, and camera angles. Aesthetic QR codes improve the visual quality, but subtle changes in their appearance may cause scanning failure. In this article, a new method to generate scanning-robust aesthetic QR codes is proposed, which is based on a module-based scanning probability estimation model that can effectively balance the tradeoff between visual quality and scanning robustness. Our method locally adjusts the luminance of each module by estimating the probability of successful sampling. The approach adopts the hierarchical, coarse-to-fine strategy to enhance the visual quality of aesthetic QR codes, which sequentially generate the following three codes: a binary aesthetic QR code, a grayscale aesthetic QR code, and the final color aesthetic QR code. Our approach also can be used to create QR codes with different visual styles by adjusting some initialization parameters. User surveys and decoding experiments were adopted for evaluating our method compared with state-of-the-art algorithms, which indicates that the proposed approach has excellent performance in terms of both visual quality and scanning robustness.
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Zhou, Xiaolong, Y. F. Li, and Bingwei He. "Entropy distribution and coverage rate-based birth intensity estimation in GM-PHD filter for multi-target visual tracking." Signal Processing 94 (January 2014): 650–60. http://dx.doi.org/10.1016/j.sigpro.2013.08.002.

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Delamater, Paul L., Joseph P. Messina, Jiaguo Qi, and Mark A. Cochrane. "A hybrid visual estimation method for the collection of ground truth fractional coverage data in a humid tropical environment." International Journal of Applied Earth Observation and Geoinformation 18 (August 2012): 504–14. http://dx.doi.org/10.1016/j.jag.2011.10.005.

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Lissandrini, Nicola, Giulia Michieletto, Riccardo Antonello, Marta Galvan, Alberto Franco, and Angelo Cenedese. "Cooperative Optimization of UAVs Formation Visual Tracking." Robotics 8, no. 3 (July 7, 2019): 52. http://dx.doi.org/10.3390/robotics8030052.

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The use of unmanned vehicles to perform tiring, hazardous, repetitive tasks, is becoming a reality out of the academy laboratories, getting more and more interest for several application fields from the industrial, to the civil, to the military contexts. In particular, these technologies appear quite promising when they employ several low-cost resource-constrained vehicles leveraging their coordination to perform complex tasks with efficiency, flexibility, and adaptation that are superior to those of a single agent (even if more instrumented). In this work, we study one of said applications, namely the visual tracking of an evader (target) by means of a fleet of autonomous aerial vehicles, with the specific aim of focusing on the target so as to perform an accurate position estimation while concurrently allowing a wide coverage over the monitored area so as to limit the probability of losing the target itself. These clearly conflicting objectives call for an optimization approach that is here developed: by considering both aforementioned aspects and the cooperative capabilities of the fleet, the designed algorithm allows controling in real time the single fields of view so as to counteract evasion maneuvers and maximize an overall performance index. The proposed strategy is discussed and finally assessed through the realistic Gazebo-ROS simulation framework.
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Ichikawa, M., N. Nonaka, H. Amano, I. Takada, S. Ishimori, H. Andoh, and K. Kumamoto. "Proton NMR Analysis of Octane Number for Motor Gasoline: Part III." Applied Spectroscopy 46, no. 3 (March 1992): 498–503. http://dx.doi.org/10.1366/0003702924125294.

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A method to evaluate the octane number of automobile gasoline by proton magnetic resonance (PMR) spectrometry has been studied. Twelve samples of marketed winter gasoline, whose octane numbers and compositions were identified according to the ASTM standards, and high-olefin gasoline were used to supplement the insufficient coverage of a previous report with additional data. Then, a linear regression equation regarding the relationship between the octane number and PMR data was prepared from the PMR spectra of the 21 samples used for the previous report, whose octane numbers were known, and the 12 samples used this time. Further, the appropriateness of the regression equation was assessed. This report concerns the results of a study in which the scope of the previous study, lacking sufficient data, has been supplemented with additional data to improve the accuracy of the visual estimation of the octane number using the pattern recognition method. Also, a linear regression equation was obtained and found useful for octane number estimation.
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Никитина, Элина, and Elina Nikitina. "Statistical Method of Processing the Results of Experimental Research of Polycode-Polymodal Text Influence on Audience (Using the Example of Regional Televigion Sports Coverage)." Scientific Research and Development. Modern Communication Studies 8, no. 4 (August 30, 2019): 27–31. http://dx.doi.org/10.12737/article_5d4d6c94de5105.63925948.

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This article analyzes the description of the procedure of statistical processing of the obtained experimental data of polycode-polymodal texts influence on audience. As examples of such complicated texts were taken sports coverages aired on television covering the Republic of Bashkortostan. Sports coverages are polycode-polymodal texts as they combine components belonging to different sign systems in one text and this information is percepted by audience with the help of two channels: visual and audial. In our research the audience was represented by the first-year and secondyear students of Ufa State Aviation Technical University. Their task was to estimate 100 television sport coverages using the scale offered. Because of technical reasons 10 sports coverages were analyzed with the usage of STATISTICA Version 6, Statsoft program. We received the following results. The audial perceptual channel is suggested to be leading as audience’s total perceptual estimation was influenced by reporter’s speech quality (presence or absence speech defects) the most.
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Rantini, Dwi, Nur Iriawan, and Irhamah Irhamah. "On the Reversible Jump Markov Chain Monte Carlo (RJMCMC) Algorithm for Extreme Value Mixture Distribution as a Location-Scale Transformation of the Weibull Distribution." Applied Sciences 11, no. 16 (August 10, 2021): 7343. http://dx.doi.org/10.3390/app11167343.

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Data with a multimodal pattern can be analyzed using a mixture model. In a mixture model, the most important step is the determination of the number of mixture components, because finding the correct number of mixture components will reduce the error of the resulting model. In a Bayesian analysis, one method that can be used to determine the number of mixture components is the reversible jump Markov chain Monte Carlo (RJMCMC). The RJMCMC is used for distributions that have location and scale parameters or location-scale distribution, such as the Gaussian distribution family. In this research, we added an important step before beginning to use the RJMCMC method, namely the modification of the analyzed distribution into location-scale distribution. We called this the non-Gaussian RJMCMC (NG-RJMCMC) algorithm. The following steps are the same as for the RJMCMC. In this study, we applied it to the Weibull distribution. This will help many researchers in the field of survival analysis since most of the survival time distribution is Weibull. We transformed the Weibull distribution into a location-scale distribution, which is the extreme value (EV) type 1 (Gumbel-type for minima) distribution. Thus, for the mixture analysis, we call this EV-I mixture distribution. Based on the simulation results, we can conclude that the accuracy level is at minimum 95%. We also applied the EV-I mixture distribution and compared it with the Gaussian mixture distribution for enzyme, acidity, and galaxy datasets. Based on the Kullback–Leibler divergence (KLD) and visual observation, the EV-I mixture distribution has higher coverage than the Gaussian mixture distribution. We also applied it to our dengue hemorrhagic fever (DHF) data from eastern Surabaya, East Java, Indonesia. The estimation results show that the number of mixture components in the data is four; we also obtained the estimation results of the other parameters and labels for each observation. Based on the Kullback–Leibler divergence (KLD) and visual observation, for our data, the EV-I mixture distribution offers better coverage than the Gaussian mixture distribution.
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Weissling, B. P., and S. F. Ackley. "Antarctic sea-ice altimetry: scale and resolution effects on derived ice thickness distribution." Annals of Glaciology 52, no. 57 (2011): 225–32. http://dx.doi.org/10.3189/172756411795931679.

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AbstractThree ice type regimes at Ice Station Belgica (ISB), during the 2007 International Polar Year SIMBA (Sea Ice Mass Balance in Antarctica) expedition, were characterized and assessed for elevation, snow depth, ice freeboard and thickness. Analyses of the probability distribution functions showed great potential for satellite-based altimetry for estimating ice thickness. In question is the required altimeter sampling density for reasonably accurate estimation of snow surface elevation given inherent spatial averaging. This study assesses an effort to determine the number of laser altimeter ‘hits’ of the ISB floe, as a representative Antarctic floe of mixed first- and multi-year ice types, for the purpose of statistically recreating the in situ-determined ice-thickness and snow depth distribution based on the fractional coverage of each ice type. Estimates of the fractional coverage and spatial distribution of the ice types, referred to as ice ‘towns’, for the 5 km2 floe were assessed by in situ mapping and photo-visual documentation. Simulated ICESat altimeter tracks, with spot size ~70m and spacing ~170 m, sampled the floe’s towns, generating a buoyancy-derived ice thickness distribution. 115 altimeter hits were required to statistically recreate the regional thickness mean and distribution for a three-town assemblage of mixed first- and multi-year ice, and 85 hits for a two-town assemblage of first-year ice only: equivalent to 19.5 and 14.5 km respectively of continuous altimeter track over a floe region of similar structure. Results have significant implications toward model development of sea-ice sampling performance of the ICESat laser altimeter record as well as maximizing sampling characteristics of satellite/airborne laser and radar altimetry missions for sea-ice thickness.
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Dissertations / Theses on the topic "Visual coverage estimation"

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Karakaya, Mahmut. "Collaborative Solutions to Visual Sensor Networks." 2011. http://trace.tennessee.edu/utk_graddiss/1087.

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Visual sensor networks (VSNs) merge computer vision, image processing and wireless sensor network disciplines to solve problems in multi-camera applications in large surveillance areas. Although potentially powerful, VSNs also present unique challenges that could hinder their practical deployment because of the unique camera features including the extremely higher data rate, the directional sensing characteristics, and the existence of visual occlusions. In this dissertation, we first present a collaborative approach for target localization in VSNs. Traditionally; the problem is solved by localizing targets at the intersections of the back-projected 2D cones of each target. However, the existence of visual occlusions among targets would generate many false alarms. Instead of resolving the uncertainty about target existence at the intersections, we identify and study the non-occupied areas in 2D cones and generate the so-called certainty map of targets non-existence. We also propose distributed integration of local certainty maps by following a dynamic itinerary where the entire map is progressively clarified. The accuracy of target localization is affected by the existence of faulty nodes in VSNs. Therefore, we present the design of a fault-tolerant localization algorithm that would not only accurately localize targets but also detect the faults in camera orientations, tolerate these errors and further correct them before they cascade. Based on the locations of detected targets in the fault-tolerated final certainty map, we construct a generative image model that estimates the camera orientations, detect inaccuracies and correct them. In order to ensure the required visual coverage to accurately localize targets or tolerate the faulty nodes, we need to calculate the coverage before deploying sensors. Therefore, we derive the closed-form solution for the coverage estimation based on the "certainty-based detection" model that takes directional sensing of cameras and existence of visual occlusions into account. The effectiveness of the proposed collaborative and fault-tolerant target localization algorithms in localization accuracy as well as fault detection and correction performance has been validated through the results obtained from both simulation and real experiments. In addition, conducted simulation shows extreme consistency with results from theoretical closed-form solution for visual coverage estimation, especially when considering the boundary effect.
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Book chapters on the topic "Visual coverage estimation"

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Puerto Lara, Andres Esteban, Cesar Pedraza, and David A. Jamaica-Tenjo. "Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images." In Pattern Recognition Applications in Engineering, 204–28. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-1839-7.ch009.

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Each crop has their own weed problems. Therefore, to understand each problem, agronomists and weed scientists must be able to determine the weed abundance with the most precise method. There are several techniques to scouting, including visual counting for density or estimations for coverage of weeds. However, this technique depends by the evaluator subjectivity, performance, and training, causing errors and bias when estimating weeds abundance. This chapter introduces a methodology to process multispectral images, based on histograms of oriented gradients and support vector machines to detect weeds in lettuce crops. The method was validated by experts on weed science, and the statistical differences were calculated. There were no significant differences between expert analysis and the proposed method. Therefore, this method offers a way to analyze large areas of crops in less time and with greater precision.
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Conference papers on the topic "Visual coverage estimation"

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Karakaya, Mahmut, and Hairong Qi. "Coverage Estimation in Heterogeneous Visual Sensor Networks." In 2012 IEEE 8th International Conference on Distributed Computing in Sensor Systems (DCOSS). IEEE, 2012. http://dx.doi.org/10.1109/dcoss.2012.61.

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Attar, Ahmad Movahedian, M. Hamed Izadi, Maedeh Movahedian, and Shantia Yarahmadian. "Coverage Estimation in Floorplan Visual Sensor Networks." In 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA). IEEE, 2013. http://dx.doi.org/10.1109/bwcca.2013.32.

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Attar, Ahmad Movahedian, Shantia Yarahmadian, and Shadrokh Samavi. "Coverage estimation in heterogenous floorplan visual sensor networks." In 2013 IEEE Sensors. IEEE, 2013. http://dx.doi.org/10.1109/icsens.2013.6688426.

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Reus, Gereon, Thomas Moller, Jonas Jager, Stewart T. Schultz, Claudia Kruschel, Julian Hasenauer, Viviane Wolff, and Klaus Fricke-Neuderth. "Looking for Seagrass: Deep Learning for Visual Coverage Estimation." In 2018 OCEANS - MTS/IEEE Kobe Techno-Ocean (OTO). IEEE, 2018. http://dx.doi.org/10.1109/oceanskobe.2018.8559302.

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Weidmann, Franz, Jonas Jager, Gereon Reus, Stewart T. Schultz, Claudia Kruschel, Viviane Wolff, and Klaus Fricke-Neuderth. "A Closer Look at Seagrass Meadows: Semantic Segmentation for Visual Coverage Estimation." In OCEANS 2019 - Marseille. IEEE, 2019. http://dx.doi.org/10.1109/oceanse.2019.8867064.

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Journal articles
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