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,

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

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