Subject
Creator
- Jonas Hepp 2
- Mireille Boutin, 0000-0002-0837-6577 2
- Yellamraju Tarun 2
- Adnan Firoze, 0000-0002-2751-009X 1
- Akshaj Uppala 1
- Bedrich Benes, 0000-0002-5293-2112 1
- Brady Hardiman 1
- Daniel Aliaga 1
- Lindsay Darling 1
- Raymond Yeh 1
Type
- Dataset 3
Date
Total is 3 Results
Code and Dataset for Pattern Recognition Benchmarks
10.4231/R7610X8M
Jonas Hepp
,
Mireille Boutin
,
Yellamraju Tarun
02/03/2016
This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.
classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification
Code and Dataset for Pattern Recognition Benchmarks
10.4231/R7G73BPN
Jonas Hepp
,
Mireille Boutin
,
Yellamraju Tarun
12/12/2016
This code computed a sequence of bounds for the error rate of a pattern recognition method. The bounds correspond to the error rate that one would expect to achieve by simply selecting features at random and thresholding the feature (TARP) approach.
classification Computer Science Electrical and Computer Engineering feature evaluation image processing image recognition Pattern Recognition Pedestrian Classification
Localization of Urban Trees across the USA with Generative AI
10.4231/7AK0-1P48
Adnan Firoze
,
Akshaj Uppala
,
Bedrich Benes
,
Brady Hardiman
,
Daniel Aliaga
,
Lindsay Darling
,
Raymond Yeh
,
Songlin Fei
12/12/2023
We use deep learning to provide a novel solution to map all trees on both public and private lands across 330 United States (U.S.) cities. This repository contains the dataset and code base.
Computer Science Computer Vision Ecology Pattern Recognition urban forestry urban tree
Display #
Results 1 - 3 of 3