By Rahul Nair, Frank Lenzen, Stephan Meister, Henrik Schäfer, Christoph Garbe (auth.), Andrea Fusiello, Vittorio Murino, Rita Cucchiara (eds.)
The 3 quantity set LNCS 7583, 7584 and 7585 contains the Workshops and Demonstrations which happened in reference to the eu convention on desktop imaginative and prescient, ECCV 2012, held in Firenze, Italy, in October 2012. the full of 179 workshop papers and 23 demonstration papers was once conscientiously reviewed and chosen for inclusion within the complaints. They the place held at workshops with the subsequent topics: non-rigid form research and deformable picture alignment; visible research and geo-localization of large-scale imagery; Web-scale imaginative and prescient and social media; video occasion categorization, tagging and retrieval; re-identification; organic and desktop imaginative and prescient interfaces; the place computing device imaginative and prescient meets paintings; purchaser intensity cameras for computing device imaginative and prescient; unsolved difficulties in optical movement and stereo estimation; what is in a face?; colour and photometry in machine imaginative and prescient; computing device imaginative and prescient in car expertise: from earth to mars; elements and attributes; research and retrieval of tracked occasions and movement in imagery streams; motion attractiveness and pose estimation in nonetheless photographs; higher-order types and international constraints in computing device imaginative and prescient; details fusion in machine imaginative and prescient for inspiration acceptance; 2.5D sensing applied sciences in movement: the search for 3D; benchmarking facial photograph research technologies.
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Extra resources for Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part II
We can the deﬁne validity measures as follows. g. αlum = 70, and a bandwidth hlum = 2 γlum (P ) = exp − (fL − αlum )2 hlum . (4) Similarly, the modulation amplitude of CW ToF imaging describes reliably the noise level of the depth measurements and its inﬂuence in the validity measure f2 γmod (P ) = 1 − exp − hmod is controlled with a bandwidth parameter hmod . mod Another indicator of the quality of the depth measurement is the variance over f2 time, which is exploited in γvar (P ) = exp − hσσ .
Pietik¨ ainen After extracting information from the 2D and 3D representations, gathering them to obtain a single descriptor is the next stage. We employed following strategies to combine these descriptors: i) Point Description Fusion, ii) Scene Description Fusion, and iii) Decision Level Fusion. 1 Point Description Fusion A keypoint can be described locally by its textural and geometrical properties if both the 2D image and the 3D information are available. These descriptors can be concatenated to form a single vector as is done in  and in .
The two-step strategy has been proven to be a fast segmentation method with high accuracy. The experimental results demonstrate that our method can segment planes and detect obstacles in real-time with high accuracy for indoor scenes. Keywords: plane segmentation, point cloud, obstacle detection. 1 Introduction Understanding the structural information of the surrounding environment is a principal issue for indoor service robots and wearable obstacle avoidance devices. There are many man-made planes in indoor scene.
Computer Vision – ECCV 2012. Workshops and Demonstrations: Florence, Italy, October 7-13, 2012, Proceedings, Part II by Rahul Nair, Frank Lenzen, Stephan Meister, Henrik Schäfer, Christoph Garbe (auth.), Andrea Fusiello, Vittorio Murino, Rita Cucchiara (eds.)