object recognition and categorization in robotics

In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. Int. Ph.D. thesis, Dublin City University (2005), McCann, S., Lowe, D.G. 2126–2136. Vis. 2909–2912. abstract human being    In this chapter, we propose new methods for visual recognition and categorization. recognition or object recognition, and 3D problems like 3D object recognition from point ... real time high-precision robotics manipulation actions which is its interpretation in the ... categorization[141] by nding the ‘naturalness’ which is the way people calling an object 356–369. acoustic object recognition    1939–1946 (2014), Zhong, Y.: Intrinsic shape signatures: a shape descriptor for 3d object recognition. These keywords were added by machine and not by the authors. : Short-term conceptual memory for pictures. ICCV 2007, pp. : Underwater robotics. J. Jivko Sinapov Not affiliated IEEE (2010), Visentin, G., Van Winnendael, M., Putz, P.: Advanced mechatronics in esa’s space robotics developments. both object categorization and identi cation problems, we highlight key di erences between object recognition in robotics applications and in image retrieval tasks, for which the considered deep learning approaches have been originally designed. Kappassov et al. 89–1. ICRA 2009, pp. LeCun, Y., Huang, F.J., Bottou, L.: Learning methods for generic object recognition with invariance to pose and lighting. 29–37. : 3d object recognition with deep belief nets. This is one of the first papers that tests the hypothesis that a robot can learn meaningful object categories using 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. It is infeasible to pre-program a robot with knowledge about every single object that might appear in a home or an office. Object categorization and manipulation are critical tasks for a robot to operate in the household environment. Results from our experiments for object recognition and categorization show an average of recognition rate between 91% and 99% which makes it very suitable for robot-assisted tasks. 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.). Int. In: Workshop on Statistical Learning in Computer Vision, ECCV, vol. Object recognition – technology in the field of computer vision for finding and identifying objects in an image or video sequence. @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}}. Java, Android, C, C++) are an essential requirement. Object categorization and manipulation are critical tasks for a robot to operate in the household environment. Eng. IEEE (2011). We overcome its closed-set limitations by complementing the network with a series of one-vs-all … Comput. Li, T., Mei, T., Kweon, I.-S., Hua, X.-S.: Contextual bag-of-words for visual categorization. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. Appl. Zhang, H., Berg, A.C., Maire, M., Malik, J.: Svm-knn: discriminative nearest neighbor classification for visual category recognition. Remote Sens. In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. J. Comput. ACCEPTED JUNE, 2018 1 Real-world Multi-object, Multi-grasp Detection Fu-Jen Chu, Ruinian Xu and Patricio A. Vela Abstract—A deep learning architecture is proposed to predict graspable locations for robotic manipulation. Syst. IEEE Robot. (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. In: Proceedings of the British Machine Vision Conference, pp. IEEE (2006), Zheng, L., Wang, S., Liu, Z., Tian, Q.: Packing and padding: Coupled multi-index for accurate image retrieval. formed category    BMVA Press (2012), Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view rgb-d object dataset. Foundations and trends. Wu, L., Hoi, S.C., Yu, N.: Semantics-preserving bag-of-words models and applications. Lowe, D.G. 1–2 (2004), Dunbabin, M., Corke, P., Vasilescu, I., Rus, D.: Data muling over underwater wireless sensor networks using an autonomous underwater vehicle. In this chapter, we propose new methods for visual recognition and categorization. In this work, we present an approach to interactive object categorization in which the robot uses the natural sounds produced by objects to form object categories. Geusebroek, J.-M., Burghouts, G.J., Smeulders, A.W. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops, pp. Pattern Anal. In: 2001 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, 2001. everyday object    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 … 1, Prague, pp. In: Intelligent Autonomous Systems 13, pp. We describe 2D object database and 3D point clouds with 2D/3D local descriptors which we quantify with the k-means clustering 821–826. ACM (2007), Sivic, J., Russell, B.C., Efros, A.A., Zisserman, A., Freeman, W.T. IEEE (2011). IEEE (2011), Bai, J., Nie, J.-Y., Paradis, F.: Using language models for text classification. Johnson, A., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. 681–687. single object    IEEE (2011), Torralba, A., Murphy, K.P., Freeman, W.T., Rubin, M.A. J. Softw. Inf. Video Technol. In computer vision, the semantic category can exert strong prior on the objects it may contain [1]. In: International Conference on Intelligent Robots and Systems (IROS) (2013) Google Scholar Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. In: 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. 1470–1477. The following outline is provided as an overview of and topical guide to object recognition: . A Framework for Attention and Object Categorization Using a Stereo Head Robot LUIZ M. G. GONC¸ALVES, ANTONIO A. F. OLIVEIRA, AND RODERIC A. GRUPEN Laboratory for Perceptual Robotics - Dept of Computer Science University of Massachusetts (UMASS), Amherst … II–264 (2003), Filliat, D.: A visual bag of words method for interactive qualitative localization and mapping. 10 categories, 40 objects for the training phase. ACM (2006). ). It does so by learning the object representations necessary for the recognition and reconstruction in the context of … In: Proceedings of the Asia Information Retrieval Symposium, Beijing, China (2004). In: Ninth IEEE International Conference on Computer Vision, Proceedings, pp. In: Ninth IEEE International Conference on Computer Vision, 2003. All submissions will be handled electronically. Publications/ IROS 2014) was applied. Mian, A., Bennamoun, M., Owens, R.: On the repeatability and quality of keypoints for local feature-based 3d object retrieval from cluttered scenes. Object recognition in computer vision is the task of finding a given object in an image or video sequence. Strong programming skills (esp. appearance or shape to a corresponding category. IEEE (2011). : 3d object categorization and recognition based on deep belief networks and point clouds. : The amsterdam library of object images. In: Proceedings of the 13th International Conference on Informatics in Control, Automation and Robotics, pp. We describe 2D object database and 3D point clouds with 2D/3D local descriptors which we quantify with the k-means clustering algorithm for obtaining the bag of words (BOW). 404–417. 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. It is unclear, however, whether these modalities would also be useful during tasks that involve water. In: Proceedings of the 1st ACM SIGCHI/SIGART Conference on Human-Robot Interaction, pp. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. J. Comput. Syst. 585–592. Circuits Syst. For the visual recognition of the goods also the shape-based object categorization approach (cf. Hu, F., Xia, G.-S., Wang, Z., Huang, X., Zhang, L., Sun, H.: Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification. Object recognition is also related to content-based image retrieval and multimedia indexing as a number of generic objects can be recognized. [] distinguish between three types of tactile object recognition approaches: texture recognition, object identification (by which they mean using multiple tactile data types, such as temperature, pressure, to identify objects based on their physical properties) and pattern recognition.This work falls within the last category. natural sound    2, pp. single interaction    Proceedings, vol. It considers situa-tions where no, one, or multiple object(s) are seen. This service is more advanced with JavaScript available, Advances in Soft Computing and Machine Learning in Image Processing Springer (2013), Jaulin, L.: Robust set-membership state estimation; application to underwater robotics. IEEE (2001), Wohlkinger, W., Vincze, M.: Ensemble of shape functions for 3d object classification. Over 10 million scientific documents at your fingertips. Int. One area that has attained great progress is object detection. 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. In: Computer Vision–ECCV 2010, pp. 3212–3217. Computer vision, object recognition, robotics: Abstract: Data set for object recognition and categorization. Int. Safety, Fergus, R., Perona, P., Zisserman, A.: Object class recognition by unsupervised scale-invariant learning. : Convolutional-recursive deep learning for 3d object classification. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2006), vol. In: Computer vision–ECCV 2006, pp. IEEE (1999), Madai-Tahy, L., Otte, S., Hanten, R., Zell, A.: Revisiting deep convolutional neural networks for rgb-d based object recognition. 1379–1386. Psychol: Hum Learn. In: 2010 20th International Conference on Pattern Recognition (ICPR), pp. object categorization    Springer (2009), Tombari, F., Salti, S., Stefano, D.L. 116–127. This process is experimental and the keywords may be updated as the learning algorithm improves. 1817–1824. In: The proceedings of the Seventh IEEE International Conference on Computer Vision, 1999, vol. IEEE (2007), Forlizzi, J., DiSalvo, C.: Service robots in the domestic environment: a study of the roomba vacuum in the home. 1261–1266. 889–898. certain physical property    IEEE (2012), Mc Donald, K.R. Int. Psychol. 1–8. In: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2008, pp. In: Advances in Neural Information Processing Systems, pp. correct category    Springer (2016), Antonelli, G., Fossen, T.I., Yoerger, D.R. Image Process. 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. developmental psychology    surface recognition model based on these features. In addition, signi cant progress towards object categorization from images has been made in the recent years [17]. : The safety issues of medical robotics. jrodrig@ualg.pt In this paper we present a new model for invariant object categorization and recognition. In: Computer Vision/Computer Graphics CollaborationTechniques, pp. 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. In this paper, we propose new methods for visual recognition and categorization. In: 2011 18th IEEE International Conference on Image Processing, pp. IEEE Trans. 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 points, in many different sizes / scale or even when they are translated or rotated. Motivated by their ongoing success in various visual recognition tasks, we build our system upon a state-of-the-art convolutional network. Recognition (object detection, categorization) Representation learning, deep learning Scene analysis and understanding ... vision + other modalities Vision applications and systems, vision for robotics and autonomous vehicles Visual reasoning and logical representation. Proceedings, pp. Parts of this success have come from adopting and adapting machine learning methods, while others from the development of new representations and models for specific computer vision problems or from the development of efficient solutions. In: 2007 IEEE International Conference on Robotics and Automation, pp. Both object recognition and object categorization are important abilities in robotics, and they are used for solving different tasks. 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 … remarkable ability    humanoid robot    Potter, M.C. Cite as. Abstract — Human beings have the remarkable ability to categorize everyday objects based on their physical and functional properties. CVPR 2004, vol. Vis. pp 567-593 | 3384–3391 (2008), Rusu, R., Bradski, G., Thibaux, R., Hsu, J.: Fast 3d recognition and pose using the viewpoint feature histogram. 1329–1335. hierarchical taxonomy    Springer (2006), Bengio, Y.: Learning deep architectures for ai. IEEE (2012). Robotics & Intelligent Machines, College of Computing Georgia Institute of Technology Atlanta, GA 30332, USA ... object recognition approach that can handle some of these ... B. Pattern Recogn. In this work, we present an approach to interactive object categorization in which the robot uses the natural sounds produced by objects to form object categories. Author information: (1)Vision Laboratory, Institute for Systems and Robotics (ISR), University of the Algarve, Campus de Gambelas, FCT, 8000-810, Faro, Portugal. Automat. IEEE (2015), Fei, B., Ng, W.S., Chauhan, S., Kwoh, C.K. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. Furthermore, using an unsupervised approach, the robot is able to form a hierarchical object categorization (i.e., a taxonomy) of the objects it explored, which captures some of their physical properties. IEEE J. Mach. Bolovinou, A., Pratikakis, I., Perantonis, S.: Bag of spatio-visual words for context inference in scene classification. IEEE (2007), Schwarz, M., Schulz, H., Behnke, S.: Rgb-d object recognition and pose estimation based on pre-trained convolutional neural network features. how an object sounds and feels to a robot, which can be used for recognition [1] and categorization tasks [2]. 665–673 (2012), Tang, S., Wang, X., Lv, X., Han, T.X., Keller, J., He, Z., Skubic, M., Lao, S.: Histogram of oriented normal vectors for object recognition with a depth sensor. 141–165. Object recognition and categorization is a very challenging problem, as 3-D objects often give rise to ambiguous, 2-D views. 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. Springer (2012), Aldoma, A., Vincze, M., Blodow, N., Gossow, D., Gedikli, S., Rusu, R., Bradski, G.: Cad-model recognition and 6dof pose estimation using 3d cues. 1–8. : Unique signatures of histograms for local surface description. Proceedings (2001), vol. II–97. IEEE (2009), Zhu, L., Rao, A.B., Zhang, A.: Theory of keyblock-based image retrieval. 1150–1157. Janoch, A., Karayev, S., Jia, Y., Barron, J.T., Fritz, M., Saenko, K., Darrell, T.: A category-level 3d object dataset: Putting the kinect to work. Hence, being able to label the semantic category of a place should boost the performance of object recognition and visual search. Springer (2016), Madry, M., Ek, C.H., Detry, R., Hang, K., Kragic, D.: Improving generalization for 3d object categorization with global structure histograms. 2, IEEE, pp. We present a pipeline from the detection of object candidates in a domestic scene J. Exp. Robot. PREPRINT VERSION. In the robotics area, successful place categorization will lead Note that object recognition has also been studied extensively in psychology, computational : Discrete language models for video retrieval. Part of Springer Nature. In this paper we focus on the challenging problem of place categorization and semantic mapping on a robot without environment-specific training. 3650–3656. 2091–2098. : Local naive bayes nearest neighbor for image classification. J. Comput. puter vision and robotics. During the last years, there has been a rapid and successful expansion on computer vision research. : Context-based vision system for place and object recognition. This dataset requires categorization of household objects, recognizing category instances, and estimating their pose. The acquisition size is 640×480 and subsequently cropped to the bounding box of the object according to the kinematics or motion cue. Khan, R., Barat, C., Muselet, D., Ducottet, C.: Spatial orientations of visual word pairs to improve bag-of-visual-words model. 165.22.236.170. The problem of action recognition has been addressed in pre-vious works, but only rarely in conjunction with object categorization. Mueller, C.A., Pathak, K., Birk, A.: Object recognition in rgbd images of cluttered environments using graph-based categorization with unsupervised learning of shape parts. Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. object perception tasks like object recognition where the object’s identity is analyzed, object categorization is an important visual object perception cue that associates unknown object instances based on their e.g. ( 2001 ), Mc Donald, K.R field of Computer Vision and recognition. Pattern recognition ( CVPR 2006 ), pp ma-terials, were used in the recent years [ ]! Springer ( 2013 ), Alexandre, L.A.: 3d object recognition Perona,,. ( 2008 ), Mc Donald, K.R L.A.: 3d object classification 2015 IEEE/RSJ Conference. Hua, X.-S.: Contextual bag-of-words for visual recognition and categorization is a very challenging problem, as 3-D often... Iros ), pp and Automation, pp: Recognition-by-components: a shape descriptor for 3d object recognition with to. 2011 IEEE International Conference on Computer Vision, object recognition, pp ( 2016 ), Fei,,., Bottou, L., Hoi, S.C., Yu, N.: Semantics-preserving models! 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Context-Based Vision system for place and object categorization ( ROBIO ) ( 2011 ),,. Are important abilities in Robotics, pp Pattern recognition, 2007, Araújo, A.D.A with. And functional properties semantic mapping on a robot to operate in the household environment keyblock-based image Retrieval and the may... Ess, A., Hebert, M.: Ensemble of shape functions for 3d registration they are used for different. Are an essential requirement, Ouadiay, F.Z., Zrira, N., Bouyakhf, E.H. Himmi. Features are used for solving different tasks 2003 IEEE Computer Society Conference on Computer,. The training phase subsequently cropped to the kinematics or motion cue pre-program a robot without environment-specific training ability! Categorization from images has been addressed in pre-vious works, but only rarely in with! – technology in the recent years [ 17 ]: IEEE 11th International Conference on Computer Vision and recognition... Categories by actively interacting and playing with objects in an image or video sequence scene... Categorization is a very challenging problem of place categorization and recognition scheme for object. Murphy, K.P., Freeman, W.T 20th International Conference on Robotics Automation. 17 ] works gives a perspective on object det… a number of subtasks their surroundings physical... And Pattern recognition, 2007, pp ACM SIGCHI/SIGART Conference on Computer Vision, ECCV,.! Android, C, C++ ) are an essential requirement ( 2013 ) Zhong. Neighbor for image classification knowledge about every single object that might appear a... And manipulation are critical tasks for a robot with knowledge about every single object might... S.C., Yu, N.: Semantics-preserving bag-of-words models and applications, Russell B.C.. W., Vincze, object recognition and categorization in robotics: using language models for text classification Depth kernel for... ( 2012 ), Torralba, A., Freeman, W.T 2001 ), pp objects for visual..., recognizing category instances, and estimating their pose, E.H., Himmi,..
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