Currently, the network traffic control systems are mainly composed of the Internet core and
wired/wireless heterogeneous backbone networks. Recently, these packet-switched …

Nucleus segmentation is an imperative step in the qualitative study of imaging datasets,
considered as an intricate task in histopathology image analysis. Segmenting a nucleus is …

To reduce data collection time for deep learning of robust robotic grasp plans, we explore
training from a synthetic dataset of 6.7 million point clouds, grasps, and analytic grasp …

This paper addresses the problem of depth estimation from a single still image. Inspired by
recent works on multi-scale convolutional neural networks (CNN), we propose a deep model …

We present iGibson 1.0, a novel simulation environment to develop robotic solutions for
interactive tasks in large-scale realistic scenes. Our environment contains 15 fully interactive …

The study of hand-object interaction requires generating viable grasp poses for high-
dimensional multi-finger models, often relying on analytic grasp synthesis which tends to …

The rise of deep learning has brought remarkable progress in estimating hand geometry
from images where the hands are part of the scene. This paper focuses on a new problem …

Depth cues have been proved very useful in various computer vision and robotic tasks. This
paper addresses the problem of monocular depth estimation from a single still image …

Traditional place categorization approaches in robot vision assume that training and test
images have similar visual appearance. Therefore, any seasonal, illumination, and …

We propose a monocular depth estimation algorithm, which extracts a depth map from a
single image, based on whole strip masking (WSM) and reliability-based refinement. First …