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An emerging problem in computer vision is the reconstruction of 3D shape and pose of an object from a single image. Hitherto, the problem has been addressed through the application of canonical deep learning methods to regress from the image directly to the 3D shape and pose labels. These approaches, however, are problematic from two perspectives. First, they are minimizing the error between 3D shapes...
Inferring the motion and shape of non-rigid objects from images has been widely explored by Non-Rigid Structure from Motion (NRSfM) algorithms. Despite their promising results, they often utilize additional constraints about the camera motion (e.g. temporal order) and the deformation of the object of interest, which are not always provided in real-world scenarios. This makes the application of NRSfM...
Text detection is a difficult task due to the significant diversity of the texts appearing in natural scene images. In this paper, we propose a novel text descriptor, SPP-net, extracted by equipping the Convolutional Neural Network (CNN) with spatial pyramid pooling. We first compute the feature maps from the original text lines without any cropping or warping, and then generate the fixed-size representations...
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