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Добірка наукової літератури з теми "Object characterization"

Автор: Grafiati

Опубліковано: 25 травня 2024

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Статті в журналах з теми "Object characterization"

Grekov, R., and A. Borisov. "CHARACTERIZATION OF THE EFFICIENCY OF THE FEATURES AGGREGATE IN FUZZY PATTERN RECOGNITION TASK." Environment. Technology. Resources. Proceedings of the International Scientific and Practical Conference 1 (June 27, 1997): 78. http://dx.doi.org/10.17770/etr1997vol1.1858.

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Let a set of objects exist each of which is described by N features X1? ..., XN, where each feature X} is a real number. So each object is set by N-dimensional vector (Xl5 ..., XN) and represents a point in the space of object descriptions, RN.There are also set objects for which degrees of membership in either class are unknown. A decision rule should be determined that could enable estimation of the membership of either object with unknown degrees of membership in the given classes (Ozols and Borisov, 1996). To determine the decision rule, such features should be found which give a possibility to distinguish objects belonging to different classes, i.e. features that are specific for each class. That is why a subtask of estimation of the efficiency of features should be solved. A function 5 should be determined which could enable estimation of the efficiency of both separate features and of features groups.Thus, the task is reduced to the determination of a number of features from set N that will best describe groups of objects and will enable possibly correct recognition of the object's membership in a class.

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Peli, T. "Multiscale fractal theory and object characterization." Journal of the Optical Society of America A 7, no. 6 (June 1, 1990): 1101. http://dx.doi.org/10.1364/josaa.7.001101.

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Aijazi, A. K., L. Malaterre, L. Trassoudaine, and P. Checchin. "SYSTEMATIC EVALUATION AND CHARACTERIZATION OF 3D SOLID STATE LIDAR SENSORS FOR AUTONOMOUS GROUND VEHICLES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B1-2020 (August 6, 2020): 199–203. http://dx.doi.org/10.5194/isprs-archives-xliii-b1-2020-199-2020.

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Abstract. 3D LiDAR sensors play an important part in several autonomous navigation and perception systems with the technology evolving rapidly over time. This work presents the preliminary evaluation results of a 3D solid state LiDAR sensor. Different aspects of this new type of sensor are studied and their data are characterized for their effective utilization for object detection for the application of Autonomous Ground Vehicles (AGV). The paper provides a set of evaluations to analyze the characterizations and performances of such LiDAR sensors. After characterization of the sensor, the performance is also evaluated in real environment with the sensors mounted on top of a vehicle and used to detect and classify different objects using a state-of-the-art Super-Voxel based method. The 3D point cloud obtained from the sensor is classified into three main object classes “Building”, “Ground” and “Obstacles”. The results evaluated on real data, clearly demonstrate the applicability and suitability of the sensor for such type of applications.

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MALGOUYRES, RÉMY, and GILLES BERTRAND. "COMPLETE LOCAL CHARACTERIZATION OF STRONG 26-SURFACES: CONTINUOUS ANALOGS FOR STRONG 26-SURFACES." International Journal of Pattern Recognition and Artificial Intelligence 13, no. 04 (June 1999): 465–84. http://dx.doi.org/10.1142/s0218001499000288.

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In Ref. 6, two similar characterizations of discrete surfaces of ℤ3 are proposed which are called strong 18-surfaces and strong 26-surfaces. The proposed characterizations consist in some natural global properties of surfaces. In this paper, we first give local necessary conditions for an object to be a strong 26-surface. An object satisfying these local properties is called a near strong 26-surface. Then we construct continuous analogs for near strong 26-surfaces and, using the continuous Jordan Theorem, we prove that the necessary local conditions previously introduced in fact give a complete local characterization of strong 26-surfaces: the class of near strong 26-surfaces coincides with the class of strong 26-surfaces.

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Hofmann, Martin, and Benjamin Pierce. "A unifying type-theoretic framework for objects." Journal of Functional Programming 5, no. 4 (October 1995): 593–635. http://dx.doi.org/10.1017/s0956796800001490.

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AbstractWe give a direct type-theoretic characterization of the basic mechanisms of object-oriented programming, including objects, methods, message passing, and subtyping, by introducing an explicit constructor for object types and suitable introduction, elimination, and equality rules. The resulting abstract framework provides a basis for justifying and comparing previous encodings of objects based on recursive record types (Cardelli, 1984; Cardelli, 1992; Bruce, 1994; Cook et al., 1990; Mitchell, 1990a) and encodings based on existential types (Pierce & Turner, 1994).

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Rind, F. C. "Intracellular characterization of neurons in the locust brain signaling impending collision." Journal of Neurophysiology 75, no. 3 (March 1, 1996): 986–95. http://dx.doi.org/10.1152/jn.1996.75.3.986.

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1. In response to a rapidly approaching object, intracellular recordings show that excitation in the locust lobula giant movement detecting (LGMD) neuron builds up exponentially, particularly during the final stages of object approach. After the cessation of object motion, inhibitory potentials in the LGMD then help to terminate this excitation. Excitation in the LGMD follows object recession with a short, constant latency but is cut back rapidly by hyperpolarizing potentials. The timing of these hyperpolarizing potentials in the LGMD is variable, and their latency following object recession is shortest with the highest velocities of motion simulated. The hyperpolarizing potentials last from 50-300 ms and are often followed by re-excitation. The observed hyperpolarizations of the LGMD can occur without any preceding excitation and are accompanied by a measurable conductance increase. The hyperpolarizations are likely to be inhibitory postsynaptic potentials (PSPs). The behavior of the intracellularly recorded inhibitory PSPs (IPSPs) closely parallels that of the feed forward inhibitory loop in the neural network described by Rind and Bramwell. 2. The preference of the LGMD for approaching versus receding objects remains over a wide range of starting and finishing distances. The response to object approach, measured both as membrane potential and spike rate, remains single peaked with starting distances of between 200 and 2,100 mm, and approach speeds of 0.5-2 m/s. These results confirm the behavior predicted by the neural network described by Rind and Bramwell but contradicts the findings of Rind and Simmons, forcing a re-evaluation of the suitability of some of the mechanical visual stimuli used in that study. 3. For depolarization of the LGMD neuron to be maintained or increased throughout the motion of image edges, the edges must move with increasing velocity over the eye. Membrane potential declines before the end of edge motion with constant velocities of edge motion. 4. A second identified neuron, the LGMD2 also is shown to respond directionally to approaching objects. In both the LGMD and LGMD2 neurons, postsynaptic inhibition shapes the directional response to object motion.

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Salman, Rahmi, Thorsten Schultze, and Ingolf Willms. "Performance Enhancement of UWB Material Characterization and Object Recognition for Security Robots." Journal of Electrical and Computer Engineering 2010 (2010): 1–6. http://dx.doi.org/10.1155/2010/314695.

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By means of UWB Radar sensors the tasks of material characterisation and object recognition can be performed on the basis of a previous imaging of the whole environment. A UWB version of the microwave ellipsometry method is applied for estimating the permittivity of homogenous objects. The object recognition task is performed using bistatic sensor nodes on the basis of Radar measurements. The simulation-based performance evaluations show a very robust behavior due to suitable preprocessing of Radar data. The applications comprise the detection of fire sources, the detection of metallic object hidden under clothing, and the recognition of building structures.

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Hitchens, Nathan M., Michael E. Baldwin, and Robert J. Trapp. "An Object-Oriented Characterization of Extreme Precipitation-Producing Convective Systems in the Midwestern United States." Monthly Weather Review 140, no. 4 (April 2012): 1356–66. http://dx.doi.org/10.1175/mwr-d-11-00153.1.

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Extreme precipitation was identified in the midwestern United States using an object-oriented approach applied to the NCEP stage-II hourly precipitation dataset. This approach groups contiguous areas that exceed a user-defined threshold into “objects,” which then allows object attributes to be diagnosed. Those objects with precipitation maxima in the 99th percentile (>55 mm) were considered extreme, and there were 3484 such objects identified in the midwestern United States between 1996 and 2010. Precipitation objects ranged in size from hundreds to over 100 000 km2, and the maximum precipitation within each object varied between 55 and 104 mm. The majority of occurrences of extreme precipitation were in the summer (June, July, and August), and peaked in the afternoon into night (1900–0200 UTC) in the diurnal cycle. Consistent with the previous work by the authors, this study shows that the systems that produce extreme precipitation in the midwestern United States vary widely across the convective-storm spectrum.

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Guan, Hongliang, Chengyuan Qian, Tingsong Wu, Xiaoming Hu, Fuzhou Duan, and Xinyi Ye. "A Dynamic Scene Vision SLAM Method Incorporating Object Detection and Object Characterization." Sustainability 15, no. 4 (February 8, 2023): 3048. http://dx.doi.org/10.3390/su15043048.

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Simultaneous localization and mapping (SLAM) based on RGB-D cameras has been widely used for robot localization and navigation in unknown environments. Most current SLAM methods are constrained by static environment assumptions and perform poorly in real-world dynamic scenarios. To improve the robustness and performance of SLAM systems in dynamic environments, this paper proposes a new RGB-D SLAM method for indoor dynamic scenes based on object detection. The method presented in this paper improves on the ORB-SLAM3 framework. First, we designed an object detection module based on YOLO v5 and relied on it to improve the tracking module of ORB-SLAM3 and the localization accuracy of ORB-SLAM3 in dynamic environments. The dense point cloud map building module was also included, which excludes dynamic objects from the environment map to create a static environment point cloud map with high readability and reusability. Full comparison experiments with the original ORB-SLAM3 and two representative semantic SLAM methods on the TUM RGB-D dataset show that: the method in this paper can run at 30+fps, the localization accuracy improved to varying degrees compared to ORB-SLAM3 in all four image sequences, and the absolute trajectory accuracy can be improved by up to 91.10%. The localization accuracy of the method in this paper is comparable to that of DS-SLAM, DynaSLAM and the two recent target detection-based SLAM algorithms, but it runs faster. The RGB-D SLAM method proposed in this paper, which combines the most advanced object detection method and visual SLAM framework, outperforms other methods in terms of localization accuracy and map construction in a dynamic indoor environment and has a certain reference value for navigation, localization, and 3D reconstruction.

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Guo, Xinhua, Yosuke Mizuno, and Kentaro Nakamura. "Object Characterization Based on Multispectral Acoustic Imaging." Japanese Journal of Applied Physics 52, no. 12R (December 1, 2013): 127301. http://dx.doi.org/10.7567/jjap.52.127301.

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Дисертації з теми "Object characterization"

LaPointe, Jamie. "Adaptive estimation techniques for resident space object characterization." Thesis, The University of Arizona, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10250698.

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This thesis proposes a unique method of implementing a top-level gating network in a dual-layer hierarchical mixture of experts. In addition it proposes a decaying learning parameter for use in both the single layer mixture of experts and the dual-layer hierarchical mixture of experts. Both a single layer mixture of experts and dual-layer hierarchical mixture of experts are compared to the multiple model adaptive estimation in estimating resident space object parameters such as size and reflectivity. The hierarchical mixture of experts consists of macromodes. Each macromode can estimate a different parameter in parallel. Each macromode is a single layer mixture of experts with unscented Kalman filters used as the experts. A gating network in each macromode determines a gating weight which is used as a hypothesis tester. Then the output of the macromode gating weights go to a top level gating weight to determine which macromode contains the most probable model. The measurements consist of astrometric and photometric data from non-resolved observations of the target gathered via a telescope with a charge coupled device camera. Each filter receives the same measurement sequence. The apparent magnitude measurement model consists of the Ashikhmin Shirley bidirectional reflectance distribution function. The measurements, process models, and the additional shape, mass, and inertia characteristics allow the algorithm to predict the state and select the most probable fit to the size and reflectance characteristics based on the statistics of the measurement residuals and innovation covariance. A simulation code is developed to test these adaptive estimation techniques. The feasibility of these methods will be demonstrated in this thesis.

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LaPointe, Jamie J., and Jamie J. LaPointe. "Adaptive Estimation Techniques for Resident Space Object Characterization." Thesis, The University of Arizona, 2016. http://hdl.handle.net/10150/623263.

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This thesis investigates using adaptive estimation techniques to determine unknown model parameters such as size and surface material reflectivity, while estimating position, velocity, attitude, and attitude rates of a resident space object. This work focuses on the application of these methods to the space situational awareness problem. This thesis proposes a unique method of implementing a top-level gating network in a dual-layer hierarchical mixture of experts. In addition it proposes a decaying learning parameter for use in both the single layer mixture of experts and the dual-layer hierarchical mixture of experts. Both a single layer mixture of experts and dual-layer hierarchical mixture of experts are compared to the multiple model adaptive estimation in estimating resident space object parameters such as size and reflectivity. The hierarchical mixture of experts consists of macromodes. Each macromode can estimate a different parameter in parallel. Each macromode is a single layer mixture of experts with unscented Kalman filters used as the experts. A gating network in each macromode determines a gating weight which is used as a hypothesis tester. Then the output of the macromode gating weights go to a top level gating weight to determine which macromode contains the most probable model. The measurements consist of astrometric and photometric data from non-resolved observations of the target gathered via a telescope with a charge coupled device camera. Each filter receives the same measurement sequence. The apparent magnitude measurement model consists of the Ashikhmin Shirley bidirectional reflectance distribution function. The measurements, process models, and the additional shape, mass, and inertia characteristics allow the algorithm to predict the state and select the most probable fit to the size and reflectance characteristics based on the statistics of the measurement residuals and innovation covariance. A simulation code is developed to test these adaptive estimation techniques. The feasibility of these methods will be demonstrated in this thesis.

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Mandadi, Bharath Kumar Reddy. "Advanced Object Characterization and Monitoring Techniques Using Polarimetric Imaging." University of Akron / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=akron1243780947.

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Biller, Beth A., Johanna Vos, Esther Buenzli, Katelyn Allers, Mickaël Bonnefoy, Benjamin Charnay, Bruno Bézard, et al. "Simultaneous Multiwavelength Variability Characterization of the Free-floating Planetary-mass Object PSO J318.5−22." IOP PUBLISHING LTD, 2018. http://hdl.handle.net/10150/627034.

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We present simultaneous Hubble Space Telescope (HST) WFC3+Spitzer IRAC variability monitoring for the highly variable young (similar to 20 Myr) planetary-mass object PSO J318.5-22. Our simultaneous HST + Spitzer observations covered approximately two rotation periods with Spitzer and most of a rotation period with the HST. We derive a period of 8.6. +/-. 0.1 hr from the Spitzer light curve. Combining this period with the measuredvsinifor this object, we find an inclination of 56 degrees.2. +/-. 8 degrees.1. We measure peak-to-trough variability amplitudes of 3.4%. +/-. 0.1% for Spitzer Channel 2 and 4.4%-5.8% (typical 68% confidence errors of similar to 0.3%) in the near-IR bands (1.07-1.67 mu m) covered by the WFC3 G141 prism-the mid-IR variability amplitude for PSO J318.5-22 is one of the highest variability amplitudes measured in the mid-IR for any brown dwarf or planetary-mass object. Additionally, we detect phase offsets ranging from 200 degrees to 210 degrees (typical error of similar to 4 degrees) between synthesized near-IR light curves and the Spitzer mid-IR light curve, likely indicating depth-dependent longitudinal atmospheric structure in this atmosphere. The detection of similar variability amplitudes in wide spectral bands relative to absorption features suggests that the driver of the variability may be inhomogeneous clouds (perhaps a patchy haze layer over thick clouds), as opposed to hot spots or compositional inhomogeneities at the top-of-atmosphere level.

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Flasseur, Olivier. "Object detection and characterization from faint signals in images : applications in astronomy and microscopy." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES042.

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La détection et la caractérisation d’objets dans des images à faible rapport signal sur bruit est un problème courant dans de nombreux domaines tels que l’astronomie ou la microscopie. En astronomie, la détection des exoplanètes et leur caractérisation par imagerie directe depuis la Terre sont des sujets de recherche très actifs. Une étoile cible et son environnement proche (abritant potentiellement des exoplanètes) sont observés sur de courtes poses. En microscopie, l’holographie en ligne est une méthode de choix pour caractériser à faibles coûts les objets microscopiques. Basée sur l’enregistrement d’un hologramme, elle permet une mise au point numérique dans n’importe quel plan du volume 3-D imagé. Dans ces deux applications cibles, le problème est rendu difficile par le faible contraste entre les objets et le fond non stationnaire des images enregistrées.Dans cette thèse, nous proposons un algorithme non-supervisé dédié à la détection et à la caractérisation d’exoplanètes par une modélisation statistique des fluctuations du fond. Cette méthode est basée sur une modélisation de la distribution statistique des données à une échelle locale de patchs, capturant ainsi leur covariances spatiales. Testé sur plusieurs jeux de données de l’imageur haut-contraste SPHERE opérant au Très Grand Télescope Européen, cet algorithme atteint de meilleures performances que les méthodes de l’état de l’art. En particulier, les cartes de détection produites sont stationnaires et statistiquement fondées. La détection des exoplanètes peut ainsi être effectuée à probabilité de fausse alarme contrôlée. L’estimation de la distribution d’énergie spectrale des sources détectées est également non biaisée. L’utilisation d’un modèle statistique permet également de déduire des précisions photométriques et astrométriques fiables. Ce cadre méthodologique est ensuite adapté pour la détection de motifs spatialement étendus tels que les motifs de diffraction rencontrés en microscopie holographique qui sont également dominés par un fond non-stationnaire. Nous proposons aussi des approches robustes basées sur des stratégies de pondération afin de réduire l’influence des nombreuses valeurs aberrantes présentes sur les données réelles. Nous montrons sur des vidéos holographiques que les méthodes de pondération proposées permettent d’atteindre un compromis biais/variance. En astronomie, la robustesse améliore les performances de détection, en particulier à courtes séparations angulaires, où les fuites stellaires dominent. Les algorithmes développés sont également adaptés pour tirer parti de la diversité spectrale des données en plus de leur diversité temporelle, améliorant ainsi leurs performances de détection et de caractérisation. Tous les algorithmes développés sont totalement non-supervisés: les paramètres de pondération et/ou de régularisation sont estimés directement à partir des données. Au-delà des applications considérées en astronomie et en microscopie, les méthodes de traitement du signal introduites dans cette thèse sont générales et pourraient être appliquées à d’autres problèmes de détection et d’estimation
Detecting and characterizing objects in images in the low signal-to-noise ratio regime is a critical issue in many areas such as astronomy or microscopy. In astronomy, the detection of exoplanets and their characterization by direct imaging from the Earth is a hot topic. A target star and its close environment (hosting potential exoplanets) are observed on short exposures. In microscopy, in-line holography is a cost-effective method for characterizing microscopic objects. Based on the recording of a hologram, it allows a digital focusing in any plane of the imaged 3-D volume. In these two fields, the object detection problem is made difficult by the low contrast between the objects and the nonstationary background of the recorded images.In this thesis, we propose an unsupervised exoplanet detection and characterization algorithm based on the statistical modeling of background fluctuations. The method, based on a modeling of the statistical distribution of patches, captures their spatial covariances. It reaches a performance superior to state-of-the-art techniques on several datasets of the European high-contrast imager SPHERE operating at the Very Large Telescope. It produces statistically grounded and spatially-stationary detection maps in which detections can be performed at a constant probability of false alarm. It also produces photometrically unbiased spectral energy distributions of the detected sources. The use of a statistical model of the data leads to reliable photometric and astrometric accuracies. This methodological framework can be adapted to the detection of spatially-extended patterns in strong structured background, such as the diffraction patterns in holographic microscopy. We also propose robust approaches based on weighting strategies to reduce the influence of the numerous outliers present in real data. We show on holographic videos that the proposed weighting approach achieves a bias/variance tradeoff. In astronomy, the robustness improves the performance of our detection method in particular at close separations where the stellar residuals dominate. Our algorithms are adapted to benefit from the possible spectral diversity of the data, which improves the detection and characterization performance. All the algorithms developed are unsupervised: weighting and/or regularization parameters are estimated in a data-driven fashion. Beyond the applications in astronomy and microscopy, the signal processing methodologies introduced are general and could be applied to other detection and estimation problems

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Lopes, Alex Cerqueira. "Máscaras: transformações em “Doroteia” de Nelson Rodrigues." Escola de Teatro, 2014. http://repositorio.ufba.br/ri/handle/ri/27042.

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RESUMO Este trabalho trata máscara-objeto e máscara-maquiagem como meiode transformação dos atores no espetáculo “Doroteia”, de Nelson Rodrigues. A dissertação é constituída a partir de experiências com máscaras, tanto objeto quanto maquiagem, apresentando no percurso das vivências a solidificação e base para a concepção e criação dos elementos cênicos em questão. Essainvestigação tem como objetivo abordar a caracterização visual dos personagens no referido espetáculo, com foco nas máscaras supracitadas, destacando sua importância fundamental para a construção do personagem teatral. Posteriormente, são analisadas as aplicabilidades das referidas máscaras nas cenas do espetáculo. As leituras a partir da percepção do espectador acontecem como consequência da consciência coerente com o todo que o portador estabelece.
ABSTRACT This thesis deals with mask-object and mask-make up asinstruments of transformation of the actors at Nelson Rodrigues’ play, “Doroteia”. The dissertation is built over experiences with mask-object and mask-make up, introducing during the process the solidification and basis for conception and creation of the scenic elements being studied. This investigation aims to broach the visual characterization of the characters in the play, focusing on the two previously mentioned masks, emphasizing their fundamental importance for the theatrical character’s construction. Subsequently, the applicability of the masks will be analyzed in the scenes of the play. The readings from the spectator’s perception happen as consequence of the conscience consistent with the whole established by the mask’s wearer.

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Al, Hayek Marianne. "Modélisation optique de signatures spectrales et polarimétriques d'objets pour augmenter les performances d'un système de reconnaissance." Electronic Thesis or Diss., Brest, 2023. http://www.theses.fr/2023BRES0101.

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L’imagerie conventionnelle, qui se limite aux formes et couleurs des objets, montre ses limites en matière de reconnaissance. Pour améliorer les performances des systèmes d’imagerie, l’imagerie hyperspectrale et polarimétrique apporte une richesse d’informations, notamment des grandeurs physiques difficiles à obtenir autrement. Cela permet d’améliorer la détection, la caractérisation quantitative et la classification des objets. Cependant, le traitement des données complexes de ces modalités reste un défi. L’objectif de ce travail est de proposer une méthodologie générique pour analyser les signaux optiques, en se concentrant sur l’imagerie hyperspectrale (HSI) en premier terme. Une classification originale des modèles hyperspectraux inversibles basés sur la physique est présentée, avec description des modèles variés les plus récents pour des applications diverses : MPBOM pour le biofilm d’algues et de bactéries, MARMIT pour le sol, PROSPECT pour les feuilles de plantes, Farrell pour les tissus biologiques turbides, Schmitt pour la peau humaine et Hapke pour les objets du système solaire. Une convergence entre les modèles PROSPECT et Farrell pour des objets intermédiaires (pomme verte et poireau) ouvrant la voie au développement d’une nouvelle modélisation générique et complète. Notamment dans le domaine de la biologie, par une collaboration avec le laboratoire de l’ANSES, nous avons procédé à une détection précoce suivie d’une quantification du biofilm qui se forme dans les bassins d’élevage de poissons en utilisant l’imagerie hyperspectrale et polarimétrique du fait que sa détection actuelle est visuelle et n’est pas assez efficace pour prévenir son accumulation et pour mettre en place des procédures de nettoyage et de désinfection. Ainsi une première version d’une modélisation physique propre nommée "DNA-HSI" a été mise en place
Conventional imaging, limited to object shapes and colors, faces limitations in object recognition. To enhance imaging system performance, hyperspectral and polarimetric imaging provides a wealth of information, includingchallenging-to-obtain physical parameters. This facilitates improved object detection, quantitative characterization, and classification. However, the processing of complex data from these modalities remains a challenge. The aim of this work is to propose a generic methodology for the analysis of optical signals, with a primary focus on hyperspectral imaging (HSI). An original classification of invertible physics-based hyperspectral models is presented, along with descriptions of recent diverse models for various applications: MPBOM for algae and bacteria biofilm, MARMIT for soil, PROSPECT for plant leaves, Farrell for turbid biological tissues, Schmitt for human skin, and Hapke for objects in the solar system. A convergence between the PROSPECT and Farrell models for intermediate objects (green apple and leek) paves the way for the development of a new generic and comprehensive modeling approach.Particularly in the field of biology, in collaboration with the ANSES laboratory, we conducted early detection ollowed by quantification of biofilms forming in fish farming basins using hyperspectral and polarimetric imaging. This is crucial as the current visual detection method is not efficient in preventing biofilm accumulation and implementingcleaning and disinfection procedures. Hence, an initial version of a dedicated physical modeling approach called "DNA-HSI" has been established

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Fernandes, Lénia Janete Oliveira. "Characterization and identification of printed objects." Master's thesis, FCT - UNL, 2008. http://hdl.handle.net/10362/1763.

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A study about the physical appearance of pre-photographic, photomechanical, photographic and digital positive reflective prints was made, relating the obtained images with the history, materials and technology used to create them. The studied samples are from the Image Permanence Institute (IPI) study collection. The digital images were obtained using a digital SLR on a copystand and a compound light microscope, with different lighting angles (0º, 45ºand 90º) and magnifications from overall views on the copystand down to a 20x objective lens on the microscope. Most of these images were originally created by IPI for www.digitalsamplebook.org, a web tool for teaching print identification, and will be used on the www.graphicsatlas.org website, along with textual information on identification, technology and history information about these reproduction processes.

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Goh, Jinzhong Jeremy [Verfasser], Denise [Gutachter] Manahan-Vaughan, and Klaus-Peter [Gutachter] Hoffmann. "Characterization of the effects of novel object-space information on synaptic plasticity in the hippocampal CA1 sub-region of freely behaving mice / Jinzhong Jeremy Goh ; Gutachter: Denise Manahan-Vaughan, Klaus-Peter Hoffmann ; International Graduate School of Neuroscience." Bochum : Ruhr-Universität Bochum, 2013. http://d-nb.info/1212660749/34.

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bin, Ahmad Khairuddin Taufiq. "Characterization of objects by fitting the polarization tensor." Thesis, University of Manchester, 2016. https://www.research.manchester.ac.uk/portal/en/theses/characterization-of-objects-by-fitting-the-polarization-tensor(1ee0de67-fdd4-4fae-ba00-3f2e4f3987a8).html.

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This thesis focuses on some mathematical aspects and a few recent applications of the polarization tensor (PT). Here, the main concern of the study is to characterize objects presented in electrical or electromagnetic fields by only using the PT. This is possible since the PT contains significant information about the object such as shape, orientation and material properties. Two main applications are considered in the study and they are electrosensing fish and metal detection. In each application, we present a mathematical formulation of the PT and briefly discuss its properties. The PT in the electrosensing fish is actually based on the first order generalized polarization tensor (GPT) while the GPT itself generalizes the classical PT called as the P\'lya-Szeg\H PT. In order to investigate the role of the PT in electrosensing fish, we propose two numerical methods to compute the first order PT. The first method is directly based on the quadrature method of numerical integration while the second method is an adaptation of some terminologies of the boundary element method (BEM). A code to use the first method is developed in \textit while a script in \textit is written as an interface for using the new developed code for BEM called as \textit. When comparing the two methods, our numerical results show that the first order PT is more accurate with faster convergence when computed by \textit. During this study, we also give a strategy to determine an ellipsoid from a given first order PT. This is because we would like to propose an experiment to test whether electrosensing fish can discriminate a pair of different objects but with the same first order PT such that the pair could be an ellipsoid and some other object. In addition, the first order PT (or the P\'{o}lya-Szeg\H{o} PT) with complex conductivity (or complex permittivity) which is similar to the PT for Maxwell's equations is also investigated. On the other hand, following recent mathematical foundation of the PT from the eddy current model, we use the new proposed explicit formula to compute the rank 2 PT for a few metallic targets relevance in metal detection. We show that the PT for the targets computed from the explicit formula agree to some degree of accuracy with the PT obtained from metal detectors during experimental works and simulations conducted by the engineers. This suggests to alternatively use the explicit formula which depends only on the geometry and material properties of the target as well as offering lower computational efforts than performing measurements with metal detectors to obtain the PT. By using the explicit formula of the rank 2 PT, we also numerically investigate some properties of the rank 2 PT where, the information obtained could be useful to improve metal detection and also in other potential applications of the eddy current. In this case, if the target is magnetic but non-conducting, the rank 2 PT of the target can also be computed by using the explicit formula of the first order PT.

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Книги з теми "Object characterization"

Scott, Carroll, Zimmt Werner S, Spurgeon David 1962-, and Lane Stacey K, eds. Material characterization tests for objects of art and archaeology. 2nd ed. London: Archetype Publications, 2005.

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Foliage penetration radar: Detection and characterization of objects under trees. Raleigh, NC: SciTech Pub., 2011.

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Gril, Joseph, ed. Wood Science for Conservation of Cultural Heritage – Braga 2008. Florence: Firenze University Press, 2010. http://dx.doi.org/10.36253/978-88-6453-165-6.

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COST Action IE0601 "Wood Science for Conservation of Cultural Heritage" (www.woodculther.org) aims to improve the conservation of European wooden cultural heritage objects, by fostering research and interaction between researchers in various fields of wood science, conservators of wooden artworks, scientists from related fields. These proceedings contain the papers presented in the 2nd International Conference held in Braga (Portugal) 5-7/11/2008, dealing with themes such as material properties, biological degradation, characterization and measurement techniques, conservation, structures. This conference was patronized by the European Society for Wood Mechanics (ESWM), an informal body promoting wood mechanics in Europe by regular organisation of meetings through running COST Actions.

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Rieger, Christopher. Faulkner’s Fashion. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9798765103982.

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The first book-length study of clothing and dress across William Faulkner’s novels and short stories. Clothing is one of the most important and pervasive material items throughout William Faulkner’s fiction. Faulkner's Fashion analyzes the writer’s use of clothing from a variety of critical approaches, considering how clothing and dress intersect with race, class, and gender across Faulkner’s works. It also considers clothes as material objects, using Thing Theory and Object Oriented Ontology to illuminate the role clothing plays as an object in conjunction with its multiple layers of symbolic meaning to both the wearer and the observer. Faulkner's Fashion reveals how much attention Faulkner pays to garments and fashion in his own life and in his fiction, arguing that dress is often a means of characterization for Faulkner, while it also connects his narrative representations of gender, sexuality, class, poverty, race, and modernity.

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Azzouni, Jody. Ontology Without Borders. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190622558.001.0001.

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Part I is metametaphysics. Quantifier variance views are criticized, and it’s shown that ontological debate, to be cogent, requires a single existence concept shared by debate participants. Natural language expresses such a concept which has certain formal properties—univocality among them. It’s shown that an ontological neutralist interpretation of quantifier domains (both formal- and natural-language) is consistent and consistent with usage data. Finally, several puzzles, among them Hob-Nob sentences and truth-talk about fictions, are resolved using the neutralist interpretation. A result established here is crucial to establishing the metaphysics argued for in part II: the general invalidity of indispensability arguments. Part II is metaphysics. An austere metaphysical position—feature metaphysics—is presented and argued for. Features aren’t properties or relations or objects of any sort. They have no individuation conditions. A feature-characterization language, with the expressive strength provided by quantifiers, is given; and using the results of part I, it’s shown that no commitments to objects arise when using this language. Feature-characterization languages supplant predication (properties of objects) with an “is at” relation or a co-occurrence relation between features. It’s shown that the resulting notion doesn’t yield a property-bundle view. Feature metaphysics is argued for by showing that the notion of object borders (central to individuation conditions for objects) cannot be interpreted metaphysically. This is also true of the individuation conditions used by philosophers to argue for tropes over universals, or vice versa. The resulting position allows us to distinguish what we project onto the world from what we find there.

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Azzouni, Jody. Feature-Characterization Languages. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190622558.003.0009.

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The language appropriate to feature metaphysics is described. This language is one that induces no commitments to objects, although it allows an expression of a commitment to the reality of ontological borders. The language resembles, on the surface, weather reports, with apparently pleonastic subject terms. Feature-characterization languages are shown to be as expressively powerful as those that utilize first-order quantification. They differ from first-order languages because the traditional predication relation (which presupposes objects and properties and relations of those objects) is replaced by an “is at” relation that presupposes none of these things. It’s also shown that the presupposition of locations (in space and time) isn’t required either. The language requires, metaphysically, only that features co-occur.

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Pfeiffer, Christian. Body in Categories 6. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198779728.003.0005.

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This chapter expands on the basic theory, which is presented in the Categories. It offers a treatment of the mereotopological properties of bodies, for instance, what belongs to them insofar as they are bodies of physical substances. Bodies are complete and perfect in virtue of being three‐dimensional. Body is prior to surfaces and lines and, because bodies are complete, there cannot be a four‐dimensional magnitude. The explanation offered is that certain topological properties are linked to and determined by the nature of the object in question. Body is a composite of the boundary and the interior or extension. A formal characterization of boundaries as limit entities is offered and it is argued that boundaries are dependent particulars. Similarly, the extension is ontologically dependent on bodies. Aristotle’s argument that the extension of objects is divisible into ever‐divisibles is revisited.

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Odegaard, Nancy. Material Characterization Tests for Objects of Art. 2nd ed. Archetype Books, 2005.

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Bistatic Radar Cross Section (RCS) Characterization of Complex Objects. Storming Media, 1999.

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Odegaard, Nancy, Scott Carroll, and Werner S. Zimmt. Material Characterization Tests for Objects of Art and Archaeology. Archetype Publications Ltd, 2000.

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Частини книг з теми "Object characterization"

Burghouts, Gertjan J. "Task-Specific Novel Object Characterization." In Pattern Recognition. ICPR International Workshops and Challenges, 447–55. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-68799-1_33.

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Sirotti, Paolo. "Optical Joint Fourier Transform Correlation for Phase Object Recognition." In Nondestructive Characterization of Materials II, 753–60. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4684-5338-6_78.

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Andersen, Jens Damgaard. "Combinatorial characterization of perspective projections from polyhedral object scenes." In Computer Vision — ECCV 90, 557–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/bfb0014906.

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Bouchaud, François, Thomas Vantroys, and Alexandre Boe. "Characterization of a Connected Object by Its Acoustic Signature." In Lecture Notes in Networks and Systems, 19–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98015-3_2.

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Landabaso, José Luis, and Montse Pardàs. "Foreground Regions Extraction and Characterization Towards Real-Time Object Tracking." In Machine Learning for Multimodal Interaction, 241–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11677482_21.

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Vascon, Sebastiano, Ylenia Parin, Eis Annavini, Mattia D’Andola, Davide Zoccolan, and Marcello Pelillo. "Characterization of Visual Object Representations in Rat Primary Visual Cortex." In Lecture Notes in Computer Science, 577–86. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-11015-4_43.

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Wu, Shenming, Yishuo Huang, Yu-Min Su, and Yuan-Zhih Lin. "Evaluating the Thermal Characteristics of Rubberized Asphalt by Applying the Object-Based Approach." In Testing and Characterization of Asphalt Materials and Pavement Structures, 12–20. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-95789-0_2.

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Habibi, Golnaz, Sándor P. Fekete, Zachary Kingston, and James McLurkin. "Distributed Object Characterization with Local Sensing by a Multi-robot System." In Distributed Autonomous Robotic Systems, 205–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73008-0_15.

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Osip, David J., S. D. Kern, and J. L. Elliot. "Physical Characterization of the Binary Edgeworth—Kuiper Belt Object 2001 QT297." In The First Decadal Review of the Edgeworth-Kuiper Belt, 409–21. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-3321-2_35.

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Yao, Wei, and Jianwei Wu. "Airborne LiDAR for Detection and Characterization of Urban Objects and Traffic Dynamics." In Urban Informatics, 367–400. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8983-6_22.

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AbstractIn this chapter, we present an advanced machine learning strategy to detect objects and characterize traffic dynamics in complex urban areas by airborne LiDAR. Both static and dynamical properties of large-scale urban areas can be characterized in a highly automatic way. First, LiDAR point clouds are colorized by co-registration with images if available. After that, all data points are grid-fitted into the raster format in order to facilitate acquiring spatial context information per-pixel or per-point. Then, various spatial-statistical and spectral features can be extracted using a cuboid volumetric neighborhood. The most important features highlighted by the feature-relevance assessment, such as LiDAR intensity, NDVI, and planarity or covariance-based features, are selected to span the feature space for the AdaBoost classifier. Classification results as labeled points or pixels are acquired based on pre-selected training data for the objects of building, tree, vehicle, and natural ground. Based on the urban classification results, traffic-related vehicle motion can further be indicated and determined by analyzing and inverting the motion artifact model pertinent to airborne LiDAR. The performance of the developed strategy towards detecting various urban objects is extensively evaluated using both public ISPRS benchmarks and peculiar experimental datasets, which were acquired across European and Canadian downtown areas. Both semantic and geometric criteria are used to assess the experimental results at both per-pixel and per-object levels. In the datasets of typical city areas requiring co-registration of imagery and LiDAR point clouds a priori, the AdaBoost classifier achieves a detection accuracy of up to 90% for buildings, up to 72% for trees, and up to 80% for natural ground, while a low and robust false-positive rate is observed for all the test sites regardless of object class to be evaluated. Both theoretical and simulated studies for performance analysis show that the velocity estimation of fast-moving vehicles is promising and accurate, whereas slow-moving ones are hard to distinguish and yet estimated with acceptable velocity accuracy. Moreover, the point density of ALS data tends to be related to system performance. The velocity can be estimated with high accuracy for nearly all possible observation geometries except for those vehicles moving in or (quasi-)along the track. By comparative performance analysis of the test sites, the performance and consistent reliability of the developed strategy for the detection and characterization of urban objects and traffic dynamics from airborne LiDAR data based on selected features was validated and achieved.

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Тези доповідей конференцій з теми "Object characterization"

Jackin, B. J., P. K. Palanisamy, T. Yatagai, P. Predeep, Mrinal Thakur, and M. K. Ravi Varma. "Retrieving Full Object Information from Partial Object Information using Digital Holography." In OPTICS: PHENOMENA, MATERIALS, DEVICES, AND CHARACTERIZATION: OPTICS 2011: International Conference on Light. AIP, 2011. http://dx.doi.org/10.1063/1.3643532.

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Leu, Jia-Guu, Ishwar K. Sethi, and Tao Hong. "Object Surface Characterization From Range Images." In SPIE International Symposium on Optical Engineering and Industrial Sensing for Advance Manufacturing Technologies, edited by Wayne Wiitanen. SPIE, 1988. http://dx.doi.org/10.1117/12.947688.

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Davoudi, Mahsa Razavi, and Fereidoon Shams Aliee. "Characterization of Enterprise Architecture quality attributes." In 2009 13th Enterprise Distributed Object Computing Conference Workshops, EDOCW. IEEE, 2009. http://dx.doi.org/10.1109/edocw.2009.5332004.

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Alvarez, Yuri, Fernando Las-Heras, Borja Gonzalez-Valdes, Jose Angel Martinez-Lorenzo, and Carey M. Rappaport. "Low permittivity dielectric object on conductor characterization." In 2013 IEEE International Symposium on Antennas and Propagation & USNC/URSI National Radio Science Meeting. IEEE, 2013. http://dx.doi.org/10.1109/aps.2013.6711070.

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Luu, K. Kim, Joshua Snodgrass, Charles L. Matson, S. Maile Giffin, Kris Hamada, and John V. Lambert. "Space object characterization from spectral nonimaging data." In Frontiers in Optics. Washington, D.C.: OSA, 2003. http://dx.doi.org/10.1364/fio.2003.tuk2.

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Akhmedzhanov, I. M., D. V. Baranov, and Evgeny M. Zolotov. "Object characterization with the differential heterodyne microscope." In 19th Congress of the International Commission for Optics: Optics for the Quality of Life, edited by Giancarlo C. Righini and Anna Consortini. SPIE, 2003. http://dx.doi.org/10.1117/12.530763.

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Flasseur, Olivier, Loic Denis, Corinne Fournier, and Eric Thiebaut. "Robust object characterization from lensless microscopy videos." In 2017 25th European Signal Processing Conference (EUSIPCO). IEEE, 2017. http://dx.doi.org/10.23919/eusipco.2017.8081448.

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Ding, Bao Ming, Yixin Huangfu, and Saeid Habibi. "Uncertainty Characterization for 3D Object Detection Algorithms." In 2023 IEEE Transportation Electrification Conference & Expo (ITEC). IEEE, 2023. http://dx.doi.org/10.1109/itec55900.2023.10186985.

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Tseng, Po-Hang, Jin-Tang Lin, Xin-Yu Liao, Sheng-Lin Lee, Mei-Chun Lin, Yen-Lin Huang, Pei-Jen Lou, and Chen-Yuan Dong. "Calculating tumor proportional score of HNSCC patients with deep learning object detection." In Emerging Technologies for Cell and Tissue Characterization, edited by Arjen Amelink, Seemantini K. Nadkarni, and Giuliano Scarcelli. SPIE, 2021. http://dx.doi.org/10.1117/12.2615639.

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Maier, Georg, Florian Pfaff, Florian Becker, Christoph Pieper, Robin Gruna, Benjamin Noack, Harald Kruggel-Emden, et al. "Improving material characterization in sensor-based sorting by utilizing motion information." In OCM 2017 - 3rd International Conference on Optical Characterization of Materials. KIT Scientific Publishing, 2017. http://dx.doi.org/10.58895/ksp/1000063696-11.

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Sensor-based sorting provides state-of-the-art solutions for sorting of cohesive, granular materials. Systems are tailored to a task at hand, for instance by means of sensors and implementation of data analysis. Conventional systems utilize scanning sensors which do not allow for extraction of motionrelated information of objects contained in a material feed. Recently, usage of area-scan cameras to overcome this disadvantage has been proposed. Multitarget tracking can then be used in order to accurately estimate the point in time and position at which any object will reach the separation stage. In this paper, utilizing motion information of objects which can be retrieved from multitarget tracking for the purpose of classification is proposed. Results show that corresponding features can significantly increase classification performance and eventually decrease the detection error of a sorting system.

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Звіти організацій з теми "Object characterization"

Johnson, R. An object-oriented approach to site characterization decision support. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/78721.

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Hofmann, Peter, Robert Marschallinger, Michael Unterwurzacher, and Fritz Zobl. Designation of marble provenance: State-of-the-art rock fabric characterization in thin sections by object based image analysis. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0284.

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Gritzo, L. A., J. L. Moya, and D. Murray. Fire characterization and object thermal response for a large flat plate adjacent to a large JP-4 fuel fire. Office of Scientific and Technical Information (OSTI), January 1997. http://dx.doi.org/10.2172/437679.

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Chinn, D., R. Huber, D. Chambers, G. Cole, O. Balogun, J. Spicer, and T. Murray. Acoustic Characterization of Mesoscale Objects. Office of Scientific and Technical Information (OSTI), March 2007. http://dx.doi.org/10.2172/969531.

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Hsieh, Chung-kao Peter. Laser-ultrasound characterization of spherical objects. G.L. report No. 5097. Office of Scientific and Technical Information (OSTI), June 1993. http://dx.doi.org/10.2172/10143768.

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Bulaevskaya, V. Probabilistic Characterization of Partial Volume Effects in Imaging of Rectangular Objects. Office of Scientific and Technical Information (OSTI), May 2015. http://dx.doi.org/10.2172/1184185.

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Koegler, Wendy S., and W. Philip, Jr Kegelmeyer. One user's report on Sandia data objects : evaluation of the DOL and PMO for use in feature characterization. Office of Scientific and Technical Information (OSTI), November 2003. http://dx.doi.org/10.2172/918321.

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