| 1. |
- Danelljan, Martin, 1989-, et al.
(författare)
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A Probabilistic Framework for Color-Based Point Set Registration
- 2016
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Ingår i: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). - : Institute of Electrical and Electronics Engineers (IEEE). - 9781467388511 - 9781467388528 ; , s. 1818-1826
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Konferensbidrag (refereegranskat)abstract
- In recent years, sensors capable of measuring both color and depth information have become increasingly popular. Despite the abundance of colored point set data, state-of-the-art probabilistic registration techniques ignore the available color information. In this paper, we propose a probabilistic point set registration framework that exploits available color information associated with the points. Our method is based on a model of the joint distribution of 3D-point observations and their color information. The proposed model captures discriminative color information, while being computationally efficient. We derive an EM algorithm for jointly estimating the model parameters and the relative transformations. Comprehensive experiments are performed on the Stanford Lounge dataset, captured by an RGB-D camera, and two point sets captured by a Lidar sensor. Our results demonstrate a significant gain in robustness and accuracy when incorporating color information. On the Stanford Lounge dataset, our approach achieves a relative reduction of the failure rate by 78% compared to the baseline. Furthermore, our proposed model outperforms standard strategies for combining color and 3D-point information, leading to state-of-the-art results.
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| 2. |
- Danelljan, Martin, 1989-, et al.
(författare)
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Aligning the Dissimilar: A Probabilistic Feature-Based Point Set Registration Approach
- 2016
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Ingår i: Proceedings of the 23rd International Conference on Pattern Recognition (ICPR) 2016. - : IEEE. - 9781509048472 - 9781509048489 ; , s. 247-252
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Konferensbidrag (refereegranskat)abstract
- 3D-point set registration is an active area of research in computer vision. In recent years, probabilistic registration approaches have demonstrated superior performance for many challenging applications. Generally, these probabilistic approaches rely on the spatial distribution of the 3D-points, and only recently color information has been integrated into such a framework, significantly improving registration accuracy. Other than local color information, high-dimensional 3D shape features have been successfully employed in many applications such as action recognition and 3D object recognition. In this paper, we propose a probabilistic framework to integrate high-dimensional 3D shape features with color information for point set registration. The 3D shape features are distinctive and provide complementary information beneficial for robust registration. We validate our proposed framework by performing comprehensive experiments on the challenging Stanford Lounge dataset, acquired by a RGB-D sensor, and an outdoor dataset captured by a Lidar sensor. The results clearly demonstrate that our approach provides superior results both in terms of robustness and accuracy compared to state-of-the-art probabilistic methods.
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| 3. |
- Meneghetti, Giulia, et al.
(författare)
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Image alignment for panorama stitching in sparsely structured environments
- 2015
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Ingår i: Image Analysis. SCIA 2015.. - Cham : Springer. - 9783319196640 - 9783319196657 ; , s. 428-439
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Konferensbidrag (refereegranskat)abstract
- Panorama stitching of sparsely structured scenes is an open research problem. In this setting, feature-based image alignment methods often fail due to shortage of distinct image features. Instead, direct image alignment methods, such as those based on phase correlation, can be applied. In this paper we investigate correlation-based image alignment techniques for panorama stitching of sparsely structured scenes. We propose a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking. Two versions of the proposed DCF-based approach are evaluated on two real and one synthetic panorama dataset of sparsely structured indoor environments. All three datasets consist of images taken on a tripod rotating 360 degrees around the vertical axis through the optical center. We show that the proposed DCF-based methods outperform phase correlation-based approaches on these datasets.
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