Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. Bolovinou, A., Pratikakis, I., Perantonis, S.: Bag of spatio-visual words for context inference in scene classification. We overcome its closed-set limitations by complementing the network with a series of one-vs-all … 116–127. 1–8. In addition, signi cant progress towards object categorization from images has been made in the recent years [17]. surface recognition model based on these features. Pattern Anal. Wu, L., Hoi, S.C., Yu, N.: Semantics-preserving bag-of-words models and applications. 987–1008. : Short-term conceptual memory for pictures. In a nutshell, our results con- rm the remarkable improvements yield by deep learn- formed category    Yoshida, K.: Achievements in space robotics. Humans recognize a multitude of objects in images with little effort, despite the fact that the image of the objects may vary somewhat in different view … 1379–1386. IEEE (2011). correct category    IEEE Robot. 2, pp. Int. In short, our contributions are as follows: 1) We introduce a novel pre-processing pipeline for RGB-D images facilitating CNN use for object cat-egorization, instance recognition, and pose regression. Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. Using unsupervised hierarchical clustering, the robot is able to form a hierarchical taxonomy of the objects that it interacts with. 2155–2162. Vis. This is a preview of subscription content, Aldoma, A., Tombari, F., Rusu, R., Vincze, M.: OUR-CVFH–oriented, unique and repeatable clustered viewpoint feature histogram for object recognition and 6DOF pose estimation. 2, IEEE, pp. Proceedings, pp. For the visual recognition of the goods also the shape-based object categorization approach (cf. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. puter vision and robotics. jrodrig@ualg.pt In this paper we present a new model for invariant object categorization and recognition. Automatica. Semantic scene graphs are extracted from image sequences and used to find the characteristic main graphs of the action sequence via an exact graph-matching technique, thus providing an event table of the action … In: Advances in Neural Information Processing Systems, pp. Vis. (TOIS), © Springer International Publishing AG 2018, Advances in Soft Computing and Machine Learning in Image Processing, LIMIARF Laboratory, Faculty of Sciences Rabat, NTNU, Norwegian University of Science and Technology, https://doi.org/10.1007/978-3-319-63754-9_26. Eng. This video presents a demonstration of the outcome of the collaboration between our Robotics Group and the AI Group of the Institute for Artificial Intelligence of the University Bremen (cf. appearance or shape to a corresponding category. unsupervised hierarchical clustering, Developed at and hosted by The College of Information Sciences and Technology, © 2007-2019 The Pennsylvania State University, by In: Advances in Neural Information Processing Systems, pp. During the last years, there has been a rapid and successful expansion on computer vision research. II–264 (2003), Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. 1261–1266. IEEE (2006), Zheng, L., Wang, S., Liu, Z., Tian, Q.: Packing and padding: Coupled multi-index for accurate image retrieval. 3921–3926. 2909–2912. 1–2 (2004), Dunbabin, M., Corke, P., Vasilescu, I., Rus, D.: Data muling over underwater wireless sensor networks using an autonomous underwater vehicle. : Object recognition from local scale-invariant features. Int. IEEE ROBOTICS AND AUTOMATION LETTERS. 356–369. Object Categorization Recent work in cognitive science [6] and neuroscience [7] In: Springer Handbook of Robotics, pp. Twenty different surfaces, which were made of various ma-terials, were used in the experiments. This is one of the first papers that tests the hypothesis that a robot can learn meaningful object categories using ACM (2006). Mem. 1, Prague, pp. hierarchical taxonomy    J. By studying both object categorization and identification problems, we highlight key differences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. Springer (2013), Jaulin, L.: Robust set-membership state estimation; application to underwater robotics. Springer (2010), Tombari, F., Salti, S., Stefano, L.: A combined texture-shape descriptor for enhanced 3d feature matching. The results show that the formed categories capture certain physical properties of the objects and allow the robot to quickly recognize the correct category for a novel object after a single interaction with it. Biederman, I.: Recognition-by-components: a theory of human image understanding. @INPROCEEDINGS{Sinapov09fromacoustic,    author = {Jivko Sinapov and Er Stoytchev},    title = {From acoustic object recognition to object categorization by a humanoid robot},    booktitle = {in Proceedings of the Workshop on Mobile Manipulation, part of 2009 Robotics Science and Systems conference},    year = {2009}}. It is infeasible to pre-program a robot with knowledge about every single object that might appear in a home or an office. : Context-based vision system for place and object recognition. Int. Foundations and trends. Mach. If robots are to succeed in human inhabited environments, they would also need the ability to form object categories and relate them to one another. Object recognition is a cornerstone task in autonomous and/or assistance systems like robots, autonomous vehicles, or those assisting to visually impaired, … The present works gives a perspective on object det… IEEE (2011). Remote Sens. Psychol. This process is experimental and the keywords may be updated as the learning algorithm improves. IEEE (2012). Bay, H., Tuytelaars, T., Van Gool, L.: Surf: Speeded up robust features. In: IEEE International Conference on Robotics and Automation, 2009. The problem of action recognition has been addressed in pre-vious works, but only rarely in conjunction with object categorization. 2091–2098. novel object    In: Ninth IEEE International Conference on Computer Vision, 2003. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. Khan, R., Barat, C., Muselet, D., Ducottet, C.: Spatial orientations of visual word pairs to improve bag-of-visual-words model. In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. IEEE (1999), Madai-Tahy, L., Otte, S., Hanten, R., Zell, A.: Revisiting deep convolutional neural networks for rgb-d based object recognition. Basu, J.K., Bhattacharyya, D., Kim, T.-H.: Use of artificial neural network in pattern recognition. J. Comput. : 3d object recognition with deep belief nets. models that can perform object recognition using sound alone, as well as detect certain physical properties of the object (e.g., material type). 809–812. how an object sounds and feels to a robot, which can be used for recognition [1] and categorization tasks [2]. In this paper, we propose new methods for visual recognition and categorization. 821–826. : Discrete language models for video retrieval. : Discovering object categories in image collections, Sivic, J., Zisserman, A.: Video google: a text retrieval approach to object matching in videos. LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. We present a pipeline from the detection of object candidates in a domestic scene In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. Springer (2012), Toldo, R., Castellani, U., Fusiello, A.: A bag of words approach for 3d object categorization. 2987–2992. The method is evaluated on an upper-torso humanoid robot which performs five different manipulation behaviors (grasp, shake, drop, push, and tap) on 36 common household objects (e.g., cups, balls, boxes, pop cans, etc.). Object recognition and categorization is a very challenging problem, as 3-D objects often give rise to ambiguous, 2-D views. Moreover, we develop a new global descriptor called VFH-Color that combines the original version of Viewpoint Feature Histogram (VFH) descriptor with the color quantization histogram, thus adding the appearance information that improves the recognition rate. Lowe, D.G. IEEE (2006), Eitel, A., Springenberg, J.T., Spinello, L., Riedmiller, M., Burgard, W.: Multimodal deep learning for robust rgb-d object recognition. Not logged in Intell. Neural Comput. functional property    Bay, H., Ess, A., Tuytelaars, T., Van Gool, L.: Speeded-up robust features (surf). Modayil et al. 3212–3217. In: International Conference on Intelligent Robots and Systems (IROS) (2013) Google Scholar Springer (2009), Tombari, F., Salti, S., Stefano, D.L. I. object category    IEEE (2001), Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3d object classification. Larlus, D., Verbeek, J., Jurie, F.: Category level object segmentation by combining bag-of-words models with dirichlet processes and random fields. Psychol: Hum Learn. IEEE (2009), Rusu, R., Blodow, N., Marton, Z., Beetz, M.: Aligning point cloud views using persistent feature histograms. Er Stoytchev, The College of Information Sciences and Technology, in Proceedings of the Workshop on Mobile Manipulation, part of 2009 Robotics Science and Systems conference. 89–1. In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. By studying both object categorization and identification problems, we highlight key differences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. Automat. Object categorization and manipulation are critical tasks for a robot to operate in the household environment. Proceedings, vol. Li, T., Mei, T., Kweon, I.-S., Hua, X.-S.: Contextual bag-of-words for visual categorization. human inhabited environment    In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. Image Underst. Author information: (1)Vision Laboratory, Institute for Systems and Robotics (ISR), University of the Algarve, Campus de Gambelas, FCT, 8000-810, Faro, Portugal. BMVA Press (2012), Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. IEEE (2010), Visentin, G., Van Winnendael, M., Putz, P.: Advanced mechatronics in esa’s space robotics developments. IEEE (2003), Smolensky, P. Information processing in dynamical systems: Foundations of harmony theory, Socher, R., Huval, B., Bath, B., Manning, C.D., Ng, A.Y. IEEE (2010), Rusu, R., Cousins, S.: 3D is here: point cloud library (PCL). Video Technol. CVPR 2004, vol. In this chapter, we propose new methods for visual recognition and categorization. 2, pp. Operate in the household environment with invariance to pose and lighting twenty different surfaces, which were made of ma-terials! 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