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In this paper we propose a new method for detecting multiple specific 3D objects in real time. We start from the template-based approach based on the LINE2D/LINEMOD representation introduced recently by Hinterstoisser et al., yet extend it in two ways. First, we propose to learn the templates in a discriminative fashion. We show that this can be done online during the collection of the example images, in just a few milliseconds, and has a big impact on the accuracy of the detector. Second, we propose a scheme based on cascades that speeds up detection. Since detection of an object is fast, new objects can be added with very low cost, making our approach scale well. In our experiments, we easily handle 10-30 3D objects at frame rates above 10fps using a single CPU core. We outperform the state-of-the-art both in terms of speed as well as in terms of accuracy, as validated on 3 different datasets. This holds both when using monocular color images (with LINE2D) and when using RGBD images (with LINEMOD). Moreover, we propose a challenging new dataset made of 12 objects, for future competing methods on monocular color images. © 2013 IEEE. Rios-Cabrera R., Tuytelaars T., ''Discriminatively trained templates for 3D object detection: A real time scalable approach'', Proceedings 14th international conference on computer vision - ICCV 2013, pp. 2048-2055, December 3-6, 2013, Sydney, Australia. ispartof: pages:2048-2055 ispartof: Proceedings ICCV 2013 pages:2048-2055 ispartof: International conference on computer vision - ICCV 2013 location:Sydney, Australia date:3 Dec - 6 Dec 2013 status: published

In this paper we present a new method for object retrieval starting from multiple query images. The use of multiple queries allows for a more expressive formulation of the query object including, e.g., different viewpoints and/or viewing conditions. This, in turn, leads to more diverse and more accurate retrieval results. When no query images are available to the user, they can easily be retrieved from the internet using a standard image search engine. In particular, we propose a new method based on pattern mining. Using the minimal description length principle, we derive the most suitable set of patterns to describe the query object, with patterns corresponding to local feature configurations. This results in a powerful object-specific mid-level image representation. The archive can then be searched efficiently for similar images based on this representation, using a combination of two inverted file systems. Since the patterns already encode local spatial information, good results on several standard image retrieval datasets are obtained even without costly re-ranking based on geometric verification. © 2013 IEEE. Fernando B., Tuytelaars T., ''Mining multiple queries for image retrieval: On-the-fly learning of an object-specific mid-level representation'', Proceedings 14th international conference on computer vision - ICCV 2013, pp. 2544-2551, December 3-6, 2013, Sydney, Australia. ispartof: pages:2544-2551 ispartof: Proceedings ICCV 2013 pages:2544-2551 ispartof: International conference on computer vision - ICCV 2013 location:Sydney, Australia date:3 Dec - 6 Dec 2013 status: published