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20142024
most citedEmbedded real-time stereo estimation via Semi-Global Matching on the GPU

168 citations · 208 across the 16 of their papers we have counts for

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14 papers · 1 filter

cs.CV20231 cited

CARLA-BSP: a simulated dataset with pedestrians

Maciej Wielgosz, Antonio M. López, Muhammad Naveed Riaz

We present a sample dataset featuring pedestrians generated using the ARCANE framework, a new framework for generating datasets in CARLA (0.9.13). We provide use cases for pedestri…

cs.CV20233 cited

On the Metrics for Evaluating Monocular Depth Estimation

Akhil Gurram, Antonio M. Lopez

Monocular Depth Estimation (MDE) is performed to produce 3D information that can be used in downstream tasks such as those related to on-board perception for Autonomous Vehicles (A…

cs.CV20223 cited

Unstructured Road Segmentation using Hypercolumn based Random Forests of Local experts

Prassanna Ganesh Ravishankar, Antonio M. Lopez, Gemma M. Sanchez

Monocular vision based road detection methods are mostly based on machine learning methods, relying on classification and feature extraction accuracy, and suffer from appearance, i…

cs.CV2016

From Virtual to Real World Visual Perception using Domain Adaptation -- The DPM as Example

Antonio M. Lopez, Jiaolong Xu, Jose L. Gomez +2

Supervised learning tends to produce more accurate classifiers than unsupervised learning in general. This implies that training data is preferred with annotations. When addressing…

cs.CV20165 cited

A Benchmark for Endoluminal Scene Segmentation of Colonoscopy Images

David Vázquez, Jorge Bernal, F. Javier Sánchez +5

Colorectal cancer (CRC) is the third cause of cancer death worldwide. Currently, the standard approach to reduce CRC-related mortality is to perform regular screening in search for…

cs.CV20161 cited

Node-Adapt, Path-Adapt and Tree-Adapt:Model-Transfer Domain Adaptation for Random Forest

Azadeh S. Mozafari, David Vazquez, Mansour Jamzad +1

Random Forest (RF) is a successful paradigm for learning classifiers due to its ability to learn from large feature spaces and seamlessly integrate multi-class classification, as w…