Relevant bibliographies by topics / Part of an object

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Part of an object'

Author: Grafiati
Published: 4 June 2021
Last updated: 17 February 2022

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Journal articles on the topic "Part of an object"

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Ju, Ginny, and Irving Biederman. "Tests of a Theory of Human Image Understanding: Part I the Perception of Colored and Partial Objects." Proceedings of the Human Factors Society Annual Meeting 30, no. 3 (September 1986): 297–300. http://dx.doi.org/10.1177/154193128603000322.

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Object recognition can be conceptualized as a process in which the perceptual input is successfully matched with a stored representation of the object. A theory of pattern recognition, Recognition by Components(RBC) assumes that objects are represented as simple volumetric primatives (e.g., bricks, cylinders, etc.) in specifed relations to each other. According to RBC, speeded recognition should be possible from only a few components, as long as those components uniquely identify an object. Neither the full complement of an object's components, nor the object's surface characteristics (e.g., color and texture) need be present for rapid identification. The results from two experiments on the perception of briefly presented objects are offered for supporting the sufficiency of the theory. Line drawings are identified about as rapidly and as accurately as full color slides. Partial objects could be rapidly (though not optimally) identified. Complex objects are more readily identified than simple objects.
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Blickle, Thomas W., and Irving Biederman. "Tests of a Theory of Human Image Understanding: Part II. The Perception of Degraded Objects." Proceedings of the Human Factors Society Annual Meeting 30, no. 3 (September 1986): 301–5. http://dx.doi.org/10.1177/154193128603000323.

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Two experiments tested the predictions of a recent theory of object recognition (Recognition-By-Components or RBC). The first experiment supports RBC's predictions that recognition is accomplished by ascertaining the object's 3D components in their specified relations by utilizing certain nonaccidental 2D image properties. When object components were rendered nonrecoverable by deleting their nonaccidental properties recognition in an object naming task was interfered with more than when an equal amount of contour was deleted without erasing such properties. The second experiment supported RBC's assumption that vertices constitute one of these important nonaccidental properties. In a naming task objects were recognized more accurately when contour was deleted from midsegment than when an equal amount was deleted from the objects vertices.
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Pentland, Alex. "Part Segmentation for Object Recognition." Neural Computation 1, no. 1 (March 1989): 82–91. http://dx.doi.org/10.1162/neco.1989.1.1.82.

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Visual object recognition is a difficult problem that has been solved by biological visual systems. An approach to object recognition is described in which the image is segmented into parts using two simple, biologically-plausible mechanisms: a filtering operation to produce a large set of potential object “parts,” followed by a new type of network that searches among these part hypotheses to produce the simplest, most likely description of the image's part structure.
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Li, Yang, Jia Bao Wang, Jian Jiang Lu, Zhuang Miao, and Peng Fei Fang. "Object Tracking with Gradient Part-Based Models." Advanced Materials Research 490-495 (March 2012): 905–9. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.905.

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Partial occlusion and non-rigid variation are challenging problems in object tracking. To address this problem, robust gradient part-based models are proposed for object tracking in this paper. Our models constructed multiple well-chosen parts based on the gradient energy map of the object. And the local optimization algorithm, mean shift, is used to search the best locations of the multiple parts, which can be used to rectify the location of the tracked object by weighted feedback. Meanwhile, the models of the root and parts are updated online, which can improve tracking accuracy and robustness. Further, our models are easy to be embedded into different tracking algorithms and we implement the mean shift based on gradient part-based models. Experiments results show that our gradient part-based models are robust enough for object tracking, even though the objects are non-rigid or occluded.
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Leek, E. Charles, Irene Reppa, Elly Rodriguez, and Martin Arguin. "Surface but not volumetric part structure mediates three-dimensional shape representation: Evidence from part–whole priming." Quarterly Journal of Experimental Psychology 62, no. 4 (April 2009): 814–30. http://dx.doi.org/10.1080/17470210802303826.

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The decomposition of three-dimensional (3-D) objects into shape primitives consisting of geometric volumes is a key proposal of some theories of object recognition. It implicitly assumes that recognition involves volumetric completion—the derivation of a three-dimensional structure that comprises inferred shape properties, such as surfaces, that are not directly visible due to self-occlusion. The goal of this study was to test this claim. In Experiment 1 participants memorized novel objects and then discriminated these from previously unseen objects. Targets were preceded by primes containing a subset of object surfaces that either matched those visible in the whole objects or that could only be inferred through volumetric completion. The results showed performance benefits through priming from visible surfaces but not from inferred surfaces. In Experiment 2, we found equivalent priming for part-primes containing two visible surfaces from the same volumetric part and for primes containing one surface from each of two volumes. These results challenge the view that 3-D object recognition is mediated by shape primitives comprising geometric volumes. Instead, the results support an alternative model that proposes that 3-D shapes are represented as a non-volumetric surface-based structural description.
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Erdogan, Goker, Quanjing Chen, Frank E. Garcea, Bradford Z. Mahon, and Robert A. Jacobs. "Multisensory Part-based Representations of Objects in Human Lateral Occipital Cortex." Journal of Cognitive Neuroscience 28, no. 6 (June 2016): 869–81. http://dx.doi.org/10.1162/jocn_a_00937.

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The format of high-level object representations in temporal-occipital cortex is a fundamental and as yet unresolved issue. Here we use fMRI to show that human lateral occipital cortex (LOC) encodes novel 3-D objects in a multisensory and part-based format. We show that visual and haptic exploration of objects leads to similar patterns of neural activity in human LOC and that the shared variance between visually and haptically induced patterns of BOLD contrast in LOC reflects the part structure of the objects. We also show that linear classifiers trained on neural data from LOC on a subset of the objects successfully predict a novel object based on its component part structure. These data demonstrate a multisensory code for object representations in LOC that specifies the part structure of objects.
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Riabchenko, Ekaterina, and Joni-Kristian Kämäräinen. "Generative part-based Gabor object detector." Pattern Recognition Letters 68 (December 2015): 1–8. http://dx.doi.org/10.1016/j.patrec.2015.08.004.

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Václav, Štefan, Jozef Jurko, and Šimon Lecký. "Part as an Object of Assembly." Research Papers Faculty of Materials Science and Technology Slovak University of Technology 24, no. 38 (September 1, 2016): 57–65. http://dx.doi.org/10.1515/rput-2016-0039.

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Abstract We deal with static prearranged couples in practice quite often. Constructers are using them a lot to improve rigidity of product. Some examples are mentioned in this article. The paper also discusses the models of kinematic couples in plane and models of kinematic couples in space. Spatial movable and non-movable couples are commented on as well. The article particularly focuses on application of statics in the assembly in spatial couples (movable, non-movable) for intentional allowance fix which leads to a non-problem assembly. With fix like this, it is possible to manufacture selected parts in series while assuring non-problem assembly.
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Krivic, Jaka, and Franc Solina. "Part-level object recognition using superquadrics." Computer Vision and Image Understanding 95, no. 1 (July 2004): 105–26. http://dx.doi.org/10.1016/j.cviu.2003.11.002.

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Lazarev, Vladimir. "Bibliometrics, Scientometrics and Informetrics. Part 3. Object (Ending)." Science Management: Theory and Practice 3, no. 2 (June 30, 2021): 99–136. http://dx.doi.org/10.19181/smtp.2021.3.2.5.

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A simplified and sometimes vulgar understanding of the role of scientometrics in science management makes it necessary to better understand its essential characteristics. In this paper, scientometrics is considered in interrelations with bibliometrics and informetrics that are the fields of knowledge closest to it.In relation to bibliometrics, scientometrics and informetrics, this part discusses the representationof their object. Its reinterpretation using the modern broad meaning of the term “document” made us possible to come to the conclusion about (at least) the maximum convergence of interpretations of objects of bibliometrics, scientometrics and informetrics. In any case, such a comparative analysis of objects helps to identify both similarities and differences between these fields of knowledge, which is important, since their awareness is a very obvious initial condition for mutual conceptual enrichment (artificially diverged?) bibliometrics, scientometrics and informetrics with knowledge and concepts.The present, i. e. the third part of the paper provides examples of understanding the objects of bibliometrics, scientometrics, and informetrics using the modern, “broad” meaning of the concept of “document”. It is shown that with the use of this approach, the interpretations of the objects of bibliometrics, scientometrics and informetrics are even closer than with the use of the traditional one. Also, the cognitive capabilities of bibliometrics, scientometrics and informetrics are potentially expanded in this case. The ways of possible further research in this direction are outlined, taking into account the V. S. Stepin’s viewpoints on the subject of research as a concept dependent on the applied methods, and on the empirical object asthe essence allocated in connection with the tasks of cognition.
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Dissertations / Theses on the topic "Part of an object"

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Gonzalez-Garcia, Abel. "Image context for object detection, object context for part detection." Thesis, University of Edinburgh, 2018. http://hdl.handle.net/1842/28842.

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Objects and parts are crucial elements for achieving automatic image understanding. The goal of the object detection task is to recognize and localize all the objects in an image. Similarly, semantic part detection attempts to recognize and localize the object parts. This thesis proposes four contributions. The first two make object detection more efficient by using active search strategies guided by image context. The last two involve parts. One of them explores the emergence of parts in neural networks trained for object detection, whereas the other improves on part detection by adding object context. First, we present an active search strategy for efficient object class detection. Modern object detectors evaluate a large set of windows using a window classifier. Instead, our search sequentially chooses what window to evaluate next based on all the information gathered before. This results in a significant reduction on the number of necessary window evaluations to detect the objects in the image. We guide our search strategy using image context and the score of the classifier. In our second contribution, we extend this active search to jointly detect pairs of object classes that appear close in the image, exploiting the valuable information that one class can provide about the location of the other. This leads to an even further reduction on the number of necessary evaluations for the smaller, more challenging classes. In the third contribution of this thesis, we study whether semantic parts emerge in Convolutional Neural Networks trained for different visual recognition tasks, especially object detection. We perform two quantitative analyses that provide a deeper understanding of their internal representation by investigating the responses of the network filters. Moreover, we explore several connections between discriminative power and semantics, which provides further insights on the role of semantic parts in the network. Finally, the last contribution is a part detection approach that exploits object context. We complement part appearance with the object appearance, its class, and the expected relative location of the parts inside it. We significantly outperform approaches that use part appearance alone in this challenging task.
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Carlsson, Anders. "Object oriented databases : a natural part of object oriented software development?" Thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik och datavetenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-5824.

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The technology of object oriented databases was introduced to system developers in the late 1980?s. Despite that it is rarely used today. This thesis introduces the concept of object oriented databases as the purposed solution to the problems that exist with the use of relational databases. The thesis points to the advantages with storing the application objects in the database without disassembling them to fit a relational data model. Based on that advantages and the cost of introducing such a rarely used technology into a project, a guideline for when to use object oriented databases and when to use relational databases is given.
anders@actk.net
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Sa, Ting. "Object Similarity through Correlated Third-Party Objects." Wright State University / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=wright1219284798.

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McSherry, Dominic. "Part-whole interaction in the recognition of meaningful parts in generic objects." Thesis, Queen's University Belfast, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287362.

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Modolo, Davide. "Advances in detecting object classes and their semantic parts." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/23472.

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Object classes are central to computer vision and have been the focus of substantial research in the last fifteen years. This thesis addresses the tasks of localizing entire objects in images (object class detection) and localizing their semantic parts (part detection). We present four contributions, two for each task. The first two improve existing object class detection techniques by using context and calibration. The other two contributions explore semantic part detection in weakly-supervised settings. First, the thesis presents a technique for predicting properties of objects in an image based on its global appearance only. We demonstrate the method by predicting three properties: aspect of appearance, location in the image and class membership. Overall, the technique makes multi-component object detectors faster and improves their performance. The second contribution is a method for calibrating the popular Ensemble of Exemplar- SVM object detector. Unlike the standard approach, which calibrates each Exemplar- SVM independently, our technique optimizes their joint performance as an ensemble. We devise an efficient optimization algorithm to find the global optimal solution of the calibration problem. This leads to better object detection performance compared to using independent calibration. The third innovation is a technique to train part-based model of object classes using data sourced from the web. We learn rich models incrementally. Our models encompass the appearance of parts and their spatial arrangement on the object, specific to each viewpoint. Importantly, it does not require any part location annotation, which is one of the main limits to training many part detectors. Finally, the last contribution is a study on whether semantic object parts emerge in Convolutional Neural Networks trained for higher-level tasks, such as image classification. While previous efforts studied this matter by visual inspection only, we perform an extensive quantitative analysis based on ground-truth part location annotations. This provides a more conclusive answer to the question.
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Giró, i. Nieto Xavier. "Part-based object retrieval with binary partition trees." Doctoral thesis, Universitat Politècnica de Catalunya, 2012. http://hdl.handle.net/10803/108909.

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This thesis addresses the problem of visual object retrieval, where a user formulates a query to an image database by providing one or multiple examples of an object of interest. The presented techniques aim both at finding those images in the database that contain the object as well as locating the object in the image and segmenting it from the background. Every considered image, both the ones used as queries and the ones contained in the target database, is represented as a Binary Partition Tree (BPT), the hierarchy of regions previously proposed by Salembier and Garrido (2000). This data structure offers multiple opportunities and challenges when applied to the object retrieval problem. A first application of BPTs appears during the formulation of the query, when the user must interactively segment the query object from the background. Firstly, the BPT can assist in adjusting an initial marker, such as a scribble or bounding box, to the object contours. Secondly, BPT can also define a navigation path for the user to adjust an initial selection to the appropriate spatial scale. The hierarchical structure of the BPT is also exploited to extract a new type of visual words named Hierarchical Bag of Regions (HBoR). Each region defined in the BPT is described with a feature vector that combines a soft quantization on a visual codebook with an efficient bottom-up computation through the BPT. These descriptors allow the definition of a novel feature space, the Parts Space, where each object is located according to the parts that compose it. HBoR descriptors have been applied to two scenarios for object retrieval, both of them solved by considering the decomposition of the objects in parts. In the first scenario, the query is formulated with a single object exemplar which is to be matched with each BPT in the target database. The matching problem is solved in two stages: an initial top-down one that assumes that the hierarchy from the query is respected in the target BPT, and a second bottom-up one that relaxes this condition and considers region merges which are not in the target BPT. The second scenario where HBoR descriptors are applied considers a query composed of several visual objects. In this case, the provided exemplars are considered as a training set to build a model of the query concept. This model is composed of two levels, a first one where each part is modelled and detected separately, and a second one that characterises the combinations of parts that describe the complete object. The analysis process exploits the hierarchical nature of the BPT by using a novel classifier that drives an efficient top-down analysis of the target BPTs.
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Noé, Estelle. "3D layered articulated object from a single 2D drawing." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216943.

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Modeling articulated objects made of rigid layered parts used to populate 3D scenes in video games or movie production is a complex and time-consuming task for digital artists. This work proposes a sketch-based approach to efficiently model 3D layered articulated objects, such as animals with rigid shells and armors, in annotating a single 2D photo manually, and eventually fabricate it from automatically computed 2D patterns. In considering symmetrical objects seen under a 3/4 view, and an- notating salient features such as extremities of the rigid articulated parts as a mix of circular and Bézier curve, this approach is able to retrieve depth information, hidden parts, and rotation-articulated structure. The resulting shape consists of a set of quadrangulated polygons that may be flattened in 2D. Details such as ears, tails, and legs were further models using dedicated annotations. The accuracy of the reconstruction has been validated on synthetic cylindrical examples, and its ro- bustness in reconstructing a 3D model of armor, armadillo, and shrimp. The latter was finally fabricated using paper.
Att modellera artikulerade objekt gjorda av styva delar lagda i lager som används till att fylla 3D-scener i datorspel och filmskapande är en komplex och tidsödande uppgift för digitala konstnärer. Den här undersökningen föreslår ett skiss-baserat tillvägagångssätt att effektivt modellera artikulerade 3D-objekt lagda i lager, såsom djur med styva skal och rustning, i att annotera ett 2D-foto manuellt, och eventuellt skapa det från automatiskt beräknade 2D-mönster. Hänsyn är tagen till symmetriska objekt sedda under en 3/4 vy, och annotera framträdande egenskapersåsom extremiteter av de styva artikulerade delarna som en blandning avcirkulära och Bézier-kurvor, kan det här tillvägagångssättet hämta information om djup, gömda delar och rotations-artikulerade strukturer. Den slutliga formen består av ett set av fyrsidiga polygoner som kan bli tillplattade i 2D. Detaljer såsom öron, svansar och ben där framtida modeller använder dedikerade annotationer. Noggrannheten av rekonstruktionen har blivit validerad på syntetiska cylindriska exempeloch dess robusthet i att rekonstruera en 3D-modell av en rustning, ett bältdjur och en räka. Den senare skapades slutligen med hjälp av papper.
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Tang, Yuxing. "Weakly supervised learning of deformable part models and convolutional neural networks for object detection." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSEC062/document.

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Dans cette thèse, nous nous intéressons au problème de la détection d’objets faiblement supervisée. Le but est de reconnaître et de localiser des objets dans les images, n’ayant à notre disposition durant la phase d’apprentissage que des images partiellement annotées au niveau des objets. Pour cela, nous avons proposé deux méthodes basées sur des modèles différents. Pour la première méthode, nous avons proposé une amélioration de l’approche ”Deformable Part-based Models” (DPM) faiblement supervisée, en insistant sur l’importance de la position et de la taille du filtre racine initial spécifique à la classe. Tout d’abord, un ensemble de candidats est calculé, ceux-ci représentant les positions possibles de l’objet pour le filtre racine initial, en se basant sur une mesure générique d’objectness (par region proposals) pour combiner les régions les plus saillantes et potentiellement de bonne qualité. Ensuite, nous avons proposé l’apprentissage du label des classes latentes de chaque candidat comme un problème de classification binaire, en entrainant des classifieurs spécifiques pour chaque catégorie afin de prédire si les candidats sont potentiellement des objets cible ou non. De plus, nous avons amélioré la détection en incorporant l’information contextuelle à partir des scores de classification de l’image. Enfin, nous avons élaboré une procédure de post-traitement permettant d’élargir et de contracter les régions fournies par le DPM afin de les adapter efficacement à la taille de l’objet, augmentant ainsi la précision finale de la détection. Pour la seconde approche, nous avons étudié dans quelle mesure l’information tirée des objets similaires d’un point de vue visuel et sémantique pouvait être utilisée pour transformer un classifieur d’images en détecteur d’objets d’une manière semi-supervisée sur un large ensemble de données, pour lequel seul un sous-ensemble des catégories d’objets est annoté avec des boîtes englobantes nécessaires pour l’apprentissage des détecteurs. Nous avons proposé de transformer des classifieurs d’images basés sur des réseaux convolutionnels profonds (Deep CNN) en détecteurs d’objets en modélisant les différences entre les deux en considérant des catégories disposant à la fois de l’annotation au niveau de l’image globale et l’annotation au niveau des boîtes englobantes. Cette information de différence est ensuite transférée aux catégories sans annotation au niveau des boîtes englobantes, permettant ainsi la conversion de classifieurs d’images en détecteurs d’objets. Nos approches ont été évaluées sur plusieurs jeux de données tels que PASCAL VOC, ImageNet ILSVRC et Microsoft COCO. Ces expérimentations ont démontré que nos approches permettent d’obtenir des résultats comparables à ceux de l’état de l’art et qu’une amélioration significative a pu être obtenue par rapport à des méthodes récentes de détection d’objets faiblement supervisées
In this dissertation we address the problem of weakly supervised object detection, wherein the goal is to recognize and localize objects in weakly-labeled images where object-level annotations are incomplete during training. To this end, we propose two methods which learn two different models for the objects of interest. In our first method, we propose a model enhancing the weakly supervised Deformable Part-based Models (DPMs) by emphasizing the importance of location and size of the initial class-specific root filter. We first compute a candidate pool that represents the potential locations of the object as this root filter estimate, by exploring the generic objectness measurement (region proposals) to combine the most salient regions and “good” region proposals. We then propose learning of the latent class label of each candidate window as a binary classification problem, by training category-specific classifiers used to coarsely classify a candidate window into either a target object or a non-target class. Furthermore, we improve detection by incorporating the contextual information from image classification scores. Finally, we design a flexible enlarging-and-shrinking post-processing procedure to modify the DPMs outputs, which can effectively match the approximate object aspect ratios and further improve final accuracy. Second, we investigate how knowledge about object similarities from both visual and semantic domains can be transferred to adapt an image classifier to an object detector in a semi-supervised setting on a large-scale database, where a subset of object categories are annotated with bounding boxes. We propose to transform deep Convolutional Neural Networks (CNN)-based image-level classifiers into object detectors by modeling the differences between the two on categories with both image-level and bounding box annotations, and transferring this information to convert classifiers to detectors for categories without bounding box annotations. We have evaluated both our approaches extensively on several challenging detection benchmarks, e.g. , PASCAL VOC, ImageNet ILSVRC and Microsoft COCO. Both our approaches compare favorably to the state-of-the-art and show significant improvement over several other recent weakly supervised detection methods
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Asbach, Mark [Verfasser]. "Modeling for part-based visual object detection based on local features / Mark Asbach." Aachen : Hochschulbibliothek der Rheinisch-Westfälischen Technischen Hochschule Aachen, 2012. http://d-nb.info/1021938211/34.

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Yao, Suqin. "Computer-Aided Manufacturing Planning (CAMP)of Mass Customization for Non-rotational Part Production." Link to electronic dissertation, 2003. http://www.wpi.edu/Pubs/ETD/Available/etd-1216103-153523/.

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Dissertation (Ph.D.) -- Worcester Polytechnic Institute.
Keywords: Computer aided manufacturing planning; Object-oriented systems analysis (OSA); Feature; manufacturing resource capability; setup planning; multi-part fixture Includes bibliographical references (p.146-154).
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Books on the topic "Part of an object"

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Lambrix, Patrick. Part-Whole Reasoning in an Object-Centered Framework. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46440-9.

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Loomis, Mary E. S. Object databases in practice. Upper Saddle River, N.J: Prentice Hall PTR, 1998.

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Pohl, Ira. Object-oriented programming using C++. 2nd ed. Reading, Mass: Addison Wesley, 1997.

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Pohl, Ira. Object-oriented programming using C++. Redwood City, Calif: Benjamin/Cummings Pub. Co., 1993.

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Piñeiro, Antonio González. 50 años de literatura ufológica en España: Una guia para el coleccionista. 2nd ed. A Coruña: A Pobra do Caraminal, 2005.

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Allen, Peter K. Robotic object recognition using vision and touch. Boston: Kluwer Academic Publishers, 1987.

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The structure of objects. Oxford: Oxford University Press, 2008.

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Koslicki, Kathrin. The structure of objects. Oxford: Oxford University Press, 2008.

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Koslicki, Kathrin. The structure of objects. Oxford: Oxford University Press, 2008.

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Object oriented program design: With examples in C++. Reading, Mass: Addison-Wesley, 1989.

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Book chapters on the topic "Part of an object"

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Bennamoun, M., and G. J. Mamic. "Recognition by Parts and Part Segmentation Techniques." In Object Recognition, 221–59. London: Springer London, 2002. http://dx.doi.org/10.1007/978-1-4471-3722-1_6.

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Wisskirchen, Peter. "Graphics Part Hierarchies." In Object-Oriented Graphics, 102–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84247-4_6.

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Cheatle, Emma. "Theories: part-object, part-architecture." In Part-architecture, 45–56. New York: Routledge, 2016. | Based on the author's thesis (Ph.D.—University of London, 2013) under title: Part-architecture: the Maison de Verre through the Large Glass.: Routledge, 2017. http://dx.doi.org/10.4324/9781315599434-3.

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Wisskirchen, Peter. "PHIGS and Part Hierarchies." In Object-Oriented Graphics, 111–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-84247-4_7.

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Drol, William. "Part One Overview." In Object-Oriented Macromedia Flash MX, 3–4. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-0838-9_1.

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Drol, William. "Part Three Overview." In Object-Oriented Macromedia Flash MX, 153. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-0838-9_11.

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Drol, William. "Part Four Overview." In Object-Oriented Macromedia Flash MX, 241–42. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-0838-9_16.

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Drol, William. "Part Five Overview." In Object-Oriented Macromedia Flash MX, 323–25. Berkeley, CA: Apress, 2002. http://dx.doi.org/10.1007/978-1-4302-0838-9_21.

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Smith, Michael A. "Introduction — part 1." In Object-Oriented Software in C++, 1–13. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-6629-2_1.

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Smith, Michael A. "Introduction — part 2." In Object-Oriented Software in C++, 15–28. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-6629-2_2.

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Conference papers on the topic "Part of an object"

1

Koyuncu, Murat, and Basar Cetinkaya. "Part-based object extraction for complex objects." In 2014 22nd Signal Processing and Communications Applications Conference (SIU). IEEE, 2014. http://dx.doi.org/10.1109/siu.2014.6830307.

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Liu, Yi, Qiang Zhang, Dingwen Zhang, and Jungong Han. "Employing Deep Part-Object Relationships for Salient Object Detection." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00132.

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Xing, Weiwei, Weibin Liu, and Baozong Yuan. "Volumetric Part Based 3D Object Classification." In 2006 5th IEEE International Conference on Cognitive Informatics. IEEE, 2006. http://dx.doi.org/10.1109/coginf.2006.365524.

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Teich, Florian, Timo Lüddecke, and Florentin Wörgötter. "3D Object Classification via Part Graphs." In 16th International Conference on Computer Vision Theory and Applications. SCITEPRESS - Science and Technology Publications, 2021. http://dx.doi.org/10.5220/0010232604170426.

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Zhang, Yan, Andreas Koschan, and Mongi A. Abidi. "Superquadrics-based 3D object representation of automotive parts utilizing part decomposition." In Quality Control by Artificial Vision, edited by Kenneth W. Tobin, Jr. and Fabrice Meriaudeau. SPIE, 2003. http://dx.doi.org/10.1117/12.515043.

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Xing, Weiwei, Baozong Yuan, Ming Liu, and Xiaofang Tang. "3D Object Classification by Part Features Fusion." In 2006 8th international Conference on Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/icosp.2006.345660.

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Zhou, Chunluan, and Junsong Yuan. "Non-rectangular Part Discovery for Object Detection." In British Machine Vision Conference 2014. British Machine Vision Association, 2014. http://dx.doi.org/10.5244/c.28.51.

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Li, Kun, and Max Meng. "Multilevel part-based model for object manipulation." In 2013 IEEE International Conference on Information and Automation (ICIA). IEEE, 2013. http://dx.doi.org/10.1109/icinfa.2013.6720462.

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Koyuncu, Murat, and Basar Cetinkaya. "Feature fusion in part-based object detection." In 2015 23th Signal Processing and Communications Applications Conference (SIU). IEEE, 2015. http://dx.doi.org/10.1109/siu.2015.7129887.

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Felzenszwalb, Pedro F., Ross B. Girshick, and David McAllester. "Cascade object detection with deformable part models." In 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2010. http://dx.doi.org/10.1109/cvpr.2010.5539906.

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Reports on the topic "Part of an object"

1

Huang, Chien-Yuan, Octavia I. Camps, and Tapas Kanungo. Object Representation Using Appearance-Based Parts and Relations. Fort Belvoir, VA: Defense Technical Information Center, January 1999. http://dx.doi.org/10.21236/ada458670.

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Amphay, Sengvieng, and David Gray. Infrared Stationary Object Acquisition and Moving Object Tracking. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada538516.

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Bormann, C. Concise Binary Object Representation (CBOR) Tags for Object Identifiers. RFC Editor, July 2021. http://dx.doi.org/10.17487/rfc9090.

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Abelson, L. A. Object-Oriented Metrics. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada400493.

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Wells, III, and William M. Statistical Object Recognition. Fort Belvoir, VA: Defense Technical Information Center, January 1993. http://dx.doi.org/10.21236/ada270887.

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Ali, Z., and S. Sivabalan. Definition of a Record Route Object (RRO) Node-Id Sub-Object. Edited by J. P. Vasseur. RFC Editor, June 2006. http://dx.doi.org/10.17487/rfc4561.

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D`Azevedo, E. F., and C. H. Romine. DOLIB: Distributed Object Library. Office of Scientific and Technical Information (OSTI), October 1994. http://dx.doi.org/10.2172/10108508.

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Archer, Clark, and Michael Stinson. Object-Oriented Software Measures. Fort Belvoir, VA: Defense Technical Information Center, April 1995. http://dx.doi.org/10.21236/ada294737.

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Crocker, S., N. Freed, J. Galvin, and S. Murphy. MIME Object Security Services. RFC Editor, October 1995. http://dx.doi.org/10.17487/rfc1848.

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SCHAFER (W J) ASSOCIATES INC ALBUQUERQUE NM. Space Object Identification (SOI). Fort Belvoir, VA: Defense Technical Information Center, February 1999. http://dx.doi.org/10.21236/ada361614.

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