Localised Mixture Models in Region-Based Tracking
Proceedings of the 31st DAGM Symposium on Pattern Recognition
SIAM Journal on Imaging Sciences
Computer vision for fruit harvesting robots state of the art and challenges ahead
International Journal of Computational Vision and Robotics
PWP3D: Real-Time Segmentation and Tracking of 3D Objects
International Journal of Computer Vision
Simultaneous monocular 2d segmentation, 3d pose recovery and 3d reconstruction
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
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In this work, we present an approach to jointly segment a rigid object in a 2D image and estimate its 3D pose, using the knowledge of a 3D model. We naturally couple the two processes together into a unique energy functional that is minimized through a variational approach. Our methodology differs from the standard monocular 3D pose estimation algorithms since it does not rely on local image features. Instead, we use global image statistics to drive the pose estimation process. This confers a satisfying level of robustness to noise and initialization for our algorithm, and bypasses the need to establish correspondences between image and object features. Moreover, our methodology possesses the typical qualities of region-based active contour techniques with shape priors, such as robustness to occlusions or missing information, without the need to evolve an infinite dimensional curve. Another novelty of the proposed contribution is to use a unique 3D model surface of the object, instead of learning a large collection of 2D shapes to accommodate for the diverse aspects that a 3D object can take when imaged by a camera. Experimental results on both synthetic and real images are provided, which highlight the robust performance of the technique on challenging tracking and segmentation applications.