Unsupervised Joint Object Discovery and Segmentation in Internet Images

Cloud computing is a model for enabling ubiquitous, convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers,

We have presented an efficient coupling of centroidal Voronoi diagram (CVD) clustering and multi-label “Graph Cut” opti- mization algorithms applied to the simultaneous segmentation

En El casamiento engañoso, el alférez Campuzano relata a su amigo el licenciado peralta la historia de sus amores y desgracias con una dama que dice llamarse

Unsupervised joint deconvolution and segmentation method for textured images: a Bayesian approach and an advanced sampling algorithm... The images are composed of regions containing

We propose a hybrid solution based on different CNNs strate- gies: a segmentation model based on the U-Net architecture [1] for a better detection rate and an object detection

Despite the use of AdaBoost, false detec- tions are too numerous (there are roughly 4 times more false detections than right detections) and most of them cannot be discarded

This paper aims to explore these two challenges using Mask R-CNN trained with a data augmentation method designed to provide good performance on both rectilinear and fisheye images..

We have proposed a categorization for these methods, highlighting the key role of the type of prior information used during segmentation, and have distinguished three levels