By George Azzopardi, Nicolai Petkov
The quantity set LNCS 9256 and 9257 constitutes the refereed court cases of the sixteenth foreign convention on computing device research of pictures and styles, CAIP 2015, held in Valletta, Malta, in September 2015. The 138 papers awarded have been conscientiously reviewed and chosen from quite a few submissions. CAIP 2015 is the 16th within the CAIP sequence of biennial overseas meetings dedicated to all features of computing device imaginative and prescient, snapshot research and processing, development acceptance, and comparable fields.
Read or Download Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I PDF
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Additional info for Computer Analysis of Images and Patterns: 16th International Conference, CAIP 2015, Valletta, Malta, September 2-4, 2015 Proceedings, Part I
The quarter- or the half-width classiﬁers’ responses are taken as appearance cues. Given a candidate occludee’s 3D position (Xi , Zi ), assuming the disparity value for a vehicle (considered to be a vertically planar object), the measured disparity value within the corresponding hee i region should be aligned with the expected disparity value. We model disparities by a Gaussian distribution with respect to diﬀerences between expected disparity and measured disparity values. So far we have multiple weak cues, context priors, classiﬁers, and disparities.
The appearance may change considerably caused by ee deﬁne a set Tjee dynamic occlusions. For occluder j, the occludee trackers Tkj (of up to two elements). Context-Based Multiple-cue Occludee Detector. Occludees do not appear everywhere in the image. The occluder gives hints for scanning for its occludees. Assuming a (nearly) planar road surface, the occludees are located further away (in longitudinal direction), and the occludee is occluded by this occluder in the er image plane. The bounding box of an occludee bee kj and that of its occluder bj are expected to be overlapping, or adjacent to each other.
In: CVPR (2010) 9. : Learning an image-based motion context for multiple people tracking. In: CVPR (2014) 10. : Modeling occlusion by discriminative and-or structures. In: ICCV (2013) 11. : Integrating context and occlusion for car detection by hierarchical and-or model. , Tuytelaars, T. ) ECCV 2014, Part VI. LNCS, vol. 8694, pp. 652–667. Springer, Heidelberg (2014) 12. : Coupling-and-decoupling: A hierarchical model for occlusion-free object detection. PR 47(10), 3254–3264 (2014) 13. : Single-pedestrian detection aided by multi-pedestrian detection.