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Showing papers by "David G. Lowe published in 2010"


Proceedings ArticleDOI
03 May 2010
TL;DR: This paper proposes a model that utilizes a chamfer-type silhouette classifier which is weighted by a prior on scale, which is robust to missing stereo depth information, and is validated on a set of challenging indoor scenes containing mugs and shoes.
Abstract: There has been significant progress recently in object recognition research, but many of the current approaches still fail for object classes with few distinctive features, and in settings with significant clutter and viewpoint variance. One such setting is visual search in mobile robotics, where tasks such as finding a mug or stapler require robust recognition. The focus of this paper is on integrating stereo vision with appearance based recognition to increase accuracy and efficiency. We propose a model that utilizes a chamfer-type silhouette classifier which is weighted by a prior on scale, which is robust to missing stereo depth information. Our approach is validated on a set of challenging indoor scenes containing mugs and shoes, where we find that priors remove a significant number of false positives, improving the average precision by 0.2 on each dataset. We additionally experiment with an additional classifer by Felzenszwalb et al.[1] to demonstrate the approach's robustness.

58 citations


Book ChapterDOI
08 Nov 2010
TL;DR: It is argued that such a multi-view labeling approach is a better fit for applications such as robotics and surveillance than traditional object recognition where only a single image of each scene is available.
Abstract: This paper presents a novel approach for labeling objects based on multiple spatially-registered images of a scene. We argue that such a multi-view labeling approach is a better fit for applications such as robotics and surveillance than traditional object recognition where only a single image of each scene is available. To encourage further study in the area, we have collected a data set of well-registered imagery for many indoor scenes and have made this data publicly available. Our multiview labeling approach is capable of improving the results of a wide variety of image-based classifiers, and we demonstrate this by producing scene labelings based on the output of both the Deformable Parts Model of [1] as well as a method for recognizing object contours which is similar to chamfer matching. Our experimental results show that labeling objects based on multiple viewpoints leads to a significant improvement in performance when compared with single image labeling.

26 citations


Proceedings ArticleDOI
31 May 2010
TL;DR: An integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world, including the ability to access web-based training data automatically and in near real-time, and to correctly label those objects with high accuracy.
Abstract: This paper describes an integrated robot system, known as Curious George, that has demonstrated state-of-the-art capabilities to recognize objects in the real world. We describe the capabilities of this system, including: the ability to access web-based training data automatically and in near real-time, the ability to model the visual appearance and 3D shape of a wide variety of object categories, navigation abilities such as exploration, mapping and path following, the ability to decompose the environment based on 3D structure, allowing for attention to be focused on regions of interest, the ability to capture high-quality images of objects in the environment, and finally, the ability to correctly label those objects with high accuracy. The competence of the combined system has been validated by entry into an international competition where Curious George has been among the top performing systems each year. We discuss the implications of such successful object recognition for society, and provide several avenues for potential improvement.

20 citations