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You searched for subject:(hierarchical object classification). Showing records 1 – 3 of 3 total matches.

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Universiteit Utrecht

1. Beek, L.L.A.M. van. Object Classification through Probabilistic Common Sense Knowledge Reasoning.

Degree: 2013, Universiteit Utrecht

This thesis presents a manner for object classification by the use of semantic knowledge and probabilistic reasoning with such knowledge. An ontology of object classes and their context and properties is represented as a Markov Logic Network, which is a method of unifying first-order logic with probabilistic reasoning, developed recently. For each scene, the ontology is combined with symbolic observations of objects observed in the scene. Probabilistic inference is then used to infer the class or a superclass of those objects. Advisors/Committee Members: Broersen, J.M., Elfring, J..

Subjects/Keywords: object classification; ontology; markov logic; probabilistic reasoning; hierarchical object classification; semantic knowledge; description logic

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Beek, L. L. A. M. v. (2013). Object Classification through Probabilistic Common Sense Knowledge Reasoning. (Masters Thesis). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/268192

Chicago Manual of Style (16th Edition):

Beek, L L A M van. “Object Classification through Probabilistic Common Sense Knowledge Reasoning.” 2013. Masters Thesis, Universiteit Utrecht. Accessed June 17, 2019. http://dspace.library.uu.nl:8080/handle/1874/268192.

MLA Handbook (7th Edition):

Beek, L L A M van. “Object Classification through Probabilistic Common Sense Knowledge Reasoning.” 2013. Web. 17 Jun 2019.

Vancouver:

Beek LLAMv. Object Classification through Probabilistic Common Sense Knowledge Reasoning. [Internet] [Masters thesis]. Universiteit Utrecht; 2013. [cited 2019 Jun 17]. Available from: http://dspace.library.uu.nl:8080/handle/1874/268192.

Council of Science Editors:

Beek LLAMv. Object Classification through Probabilistic Common Sense Knowledge Reasoning. [Masters Thesis]. Universiteit Utrecht; 2013. Available from: http://dspace.library.uu.nl:8080/handle/1874/268192

2. Odabai Fard, Seyed Hamidreza. Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes.

Degree: Docteur es, Vision pour la Robotique, 2015, Université Blaise-Pascale, Clermont-Ferrand II

Dans cet article, nous présentons une nouvelle approche de détection multi-classes basée sur un parcours hiérarchique de classifieurs appris simultanément. Pour plus de robustesse et de rapidité, nous proposons d’utiliser un arbre de classes d’objets. Notre modèle de détection est appris en combinant les contraintes de tri et de classification dans un seul problème d’optimisation. Notre formulation convexe permet d’utiliser un algorithme de recherche pour accélérer le temps d’exécution. Nous avons mené des évaluations de notre algorithme sur les benchmarks PASCAL VOC (2007 et 2010). Comparé à l’approche un-contre-tous, notre méthode améliore les performances pour 20 classes et gagne 10x en vitesse.

Recent years have witnessed a competition in autonomous navigation for vehicles boosted by the advances in computer vision. The on-board cameras are capable of understanding the semantic content of the environment. A core component of this system is to localize and classify objects in urban scenes. There is a need to have multi-class object detection systems. Designing such an efficient system is a challenging and active research area. The algorithms can be found for applications in autonomous driving, object searches in images or video surveillance. The scale of object classes varies depending on the tasks. The datasets for object detection started with containing one class only e.g. the popular INRIA Person dataset. Nowadays, we witness an expansion of the datasets consisting of more training data or number of object classes. This thesis proposes a solution to efficiently learn a multi-class object detector. The task of such a system is to localize all instances of target object classes in an input image. We distinguish between three major efficiency criteria. First, the detection performance measures the accuracy of detection. Second, we strive low execution times during run-time. Third, we address the scalability of our novel detection framework. The two previous criteria should scale suitably with the number of input classes and the training algorithm has to take a reasonable amount of time when learning with these larger datasets. Although single-class object detection has seen a considerable improvement over the years, it still remains a challenge to create algorithms that work well with any number of classes. Most works on this subject extent these single-class detectors to work accordingly with multiple classes but remain hardly flexible to new object descriptors. Moreover, they do not consider all these three criteria at the same time. Others use a more traditional approach by iteratively executing a single-class detector for each target class which scales linearly in training time and run-time. To tackle the challenges, we present a novel framework where for an input patch during detection the closest class is ranked highest. Background labels are rejected as negative samples. The detection goal is to find the highest scoring class. To this end, we derive a convex problem formulation that combines ranking and…

Advisors/Committee Members: Chateau, Thierry (thesis director), Vacavant, Antoine (thesis director).

Subjects/Keywords: Détection multi-classes d’objets; Classification hiérarchique; Inférence rapide; Arbre de classifieurs; Parcours d’arbre; Apprentissage hiérarchique; SVM structuré; Multi-class object detection; Hierarchical classification; Rapid inference; Tree of classifiers; Tree traversal; Hierarchical learning; Structured SVM

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Odabai Fard, S. H. (2015). Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes. (Doctoral Dissertation). Université Blaise-Pascale, Clermont-Ferrand II. Retrieved from http://www.theses.fr/2015CLF22623

Chicago Manual of Style (16th Edition):

Odabai Fard, Seyed Hamidreza. “Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes.” 2015. Doctoral Dissertation, Université Blaise-Pascale, Clermont-Ferrand II. Accessed June 17, 2019. http://www.theses.fr/2015CLF22623.

MLA Handbook (7th Edition):

Odabai Fard, Seyed Hamidreza. “Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes.” 2015. Web. 17 Jun 2019.

Vancouver:

Odabai Fard SH. Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes. [Internet] [Doctoral dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. [cited 2019 Jun 17]. Available from: http://www.theses.fr/2015CLF22623.

Council of Science Editors:

Odabai Fard SH. Efficient multi-class objet detection with a hierarchy of classes : Détection efficace des objets multi-classes avec une hiérarchie des classes. [Doctoral Dissertation]. Université Blaise-Pascale, Clermont-Ferrand II; 2015. Available from: http://www.theses.fr/2015CLF22623


Virginia Tech

3. van Aardt, Jan Andreas Nicholaas. An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification.

Degree: PhD, Forestry, 2004, Virginia Tech

This study assessed the utility of an object-oriented approach to deciduous and coniferous forest volume and above ground biomass estimation, based solely on small-footprint, multiple return lidar data. The study area is located in Appomattox Buckingham State Forest in the Piedmont physiographic province of Virginia, U.S.A, at 78°41’ W, 37°25’ N. Vegetation is composed of various coniferous, deciduous, and mixed forest stands. The eCognition segmentation algorithm was used to derive objects from a lidar-based canopy height model (CHM). New segment selection criteria, based on between- and within-segment CHM variance, and average field plot size, were developed. Horizontal point samples were used to measure in-field volume and biomass, for 2-class (deciduous-coniferous) and 3-class (deciduous-coniferous-mixed) forest schemes. Per-segment lidar distributional parameters, e.g., mean, range, and percentiles, were extracted from the lidar data and used as input to volume and biomass regression analysis. Discriminant classification was performed using lidar point height and CHM distributions. There was no evident difference between the two-class and three-class approaches, based on similar adjusted R2 values. Two-class forest definition was preferred due to its simplicity. Two-class adjusted R2 and root mean square error (RMSE) values for deciduous volume (0.59; 51.15 m3/ha) and biomass (0.58; 37.41 Mg/ha) were improvements over those found in another plot-based study for the same study area. Although coniferous RMSE values for volume (38.03 m3/ha) and biomass (17.15 Mg/ha) were comparable to published results, adjusted R2 values (0.66 and 0.59) were lower. This was attributed to more variability and a narrower range (6.94 - 350.93 m3/ha) in measured values. Classification accuracy for discriminant classification based on lidar point height distributions (89.2%) was a significant improvement over CHM-based classification (79%). A lack of modeling and classification differences between average segment sizes was attributed to the hierarchical nature of the segmentation algorithm. However, segment-based modeling was distinctly better than modeling based on existing forest stands, with values of 0.42 and 62.36 m3/ha (volume) and 0.46 and 41.18 Mg/ha (biomass) for adjusted R2 and RMSE, respectively. Modeling results and classification accuracies indicated that an object-oriented approach, based solely on lidar data, has potential for full-scale forest inventory applications. Advisors/Committee Members: Wynne, Randolph H. (committeechair), Oderwald, Richard G. (committee member), Campbell, James B. Jr. (committee member), Nelson, Ross F. (committee member), Prisley, Stephen P. (committee member), Seiler, John R. (committee member).

Subjects/Keywords: forest volume and above-ground biomass; lidar distributions; Object-oriented; hierarchical segmentation; multiresolution; classification

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

van Aardt, J. A. N. (2004). An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/11238

Chicago Manual of Style (16th Edition):

van Aardt, Jan Andreas Nicholaas. “An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification.” 2004. Doctoral Dissertation, Virginia Tech. Accessed June 17, 2019. http://hdl.handle.net/10919/11238.

MLA Handbook (7th Edition):

van Aardt, Jan Andreas Nicholaas. “An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification.” 2004. Web. 17 Jun 2019.

Vancouver:

van Aardt JAN. An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification. [Internet] [Doctoral dissertation]. Virginia Tech; 2004. [cited 2019 Jun 17]. Available from: http://hdl.handle.net/10919/11238.

Council of Science Editors:

van Aardt JAN. An Object-Oriented Approach to Forest Volume and Aboveground Biomass Modeling using Small-Footprint Lidar Data for Segmentation, Estimation, and Classification. [Doctoral Dissertation]. Virginia Tech; 2004. Available from: http://hdl.handle.net/10919/11238

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