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ICPR 2020

IWBDAF 2020 - International Workshop on Biometric Data Analysis and Forensics

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
For the first time, ICPR has employed a two-round review process similar to journal submissions, with new papers allowed to be submitted in either the first or second round and papers submitted in the first round and not accepted allowed to be revised and re-submitted for second round review. 

In this edition of the **ICPR 2020** Conference, there are 1407 Oral/Poster presentations organized in five Tracks: 
- Artificial Intelligence, Machine Learning for Pattern Analysis; 
- Biometrics, Human Analysis and Behavior Understanding; Computer Vision, Robotics and Intelligent Systems; 
- Document and Media Analysis; and 
- Image and Signal Processing. 

The **ICPR 2020** Main Conference program also includes 7 Keynotes from prominent world-renowned scientists, 8 Tutorials, 28 Demos, 8 Challenges, 2 Panels, as well as an Industry/Sponsor Exhibition. In addition, 41 Workshops on a range of important current and emerging research topics complete the ICPR 2020 program. 


technical paper

WMWB - TC4 Workshop on Mobile and Wearable Biometrics 2020

IUC 2020 - The 1st International Workshop on Human and Vehicle Analysis for Intelligent Urban Computing

PATCAST - International Workshop on Pattern Forecasting

RRPR - Third Workshop on Reproducible Research in Pattern Recognition

WAAMI - Workshop on Analysis of Aerial Motion Imagery

Change and Anomaly Detection in Images, Signals and datastreams

Multiple Parametric Models Fitting

More than 20 years of High-Dynamic-Range imaging

Synchronization: a general framework for mosaicking, 3D reconstruction, matching and segmentation problems

In this work we compare the performance of a location-specific and a location-agnostic machine learning metamodel for crop nitrogen response rate prediction. We conduct a case study for grass-only pasture in several locations in New Zealand. We generate a large dataset of APSIM simulation outputs and train machine learning models based on that data. Initially, we examine how the models perform at the location where the location-specific model was trained. We then perform the Mann–Whitney U test to see if the difference in the predictions of the two models (i.e. location-specific and location-agnostic) is significant. We expand this procedure to other locations to investigate the generalization capability of the models. We find that there is no statistically significant difference in the predictions of the two models. This is both interesting and useful because the location-agnostic model generalizes better than the location-specific model which means that it can be applied to virgin sites with similar confidence to experienced sites.

Location-specific vs location-agnostic machine learning metamodels for predicting pasture nitrogen response rate

tutorial

https://d3smihljt9218e.cloudfront.net/transcript/9842/subtitle/28740520ea772c12e8e5b58090e49194.vtt

English (automatic)

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IWBDAF 2020 - International Workshop on Biometric Data Analysis and Forensics

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