Italy

ICPR 2020

WMWB - TC4 Workshop on Mobile and Wearable Biometrics 2020

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

Noisy image processing is a fundamental task of computer vision. The first
example is the detection of faint edges in noisy images, a challenging
problem studied in the last decades. A recent study introduced a fast method
to detect faint edges in the highest accuracy among all the existing
approaches. Their complexity is nearly linear in the image's pixels and
their runtime is seconds for a noisy image. Their approach utilizes a
multi-scale binary partitioning of the image. By utilizing the multi-scale
U-net architecture, we show in this paper that their method can be
dramatically improved in both aspects of run time and accuracy. By training
the network on a dataset of binary images, we developed an approach for
faint edge detection that works in linear complexity. Our runtime of a
noisy image is milliseconds on a GPU. Even though our method is orders of
magnitude faster, we still achieve higher accuracy of detection under many
challenging scenarios. In addition, we show that our approach to performing
multi-scale preprocessing of noisy images using U-net improves the ability
to perform other vision tasks under the presence of noise. We prove it on
the problems of noisy objects classification and classical image denoising.
We show that multi-scale denoising can be carried out by a novel edge
preservation loss. As our experiments show, we achieve high-quality results
in the three aspects of faint edge detection, noisy image classification, and
natural image denoising.

Multi-scale Processing of Noisy Images using Edge Preservation Losses

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

poster

tutorial

https://d3smihljt9218e.cloudfront.net/transcript/9821/subtitle/be07d01dd246e9554ca75443cea49f81.vtt

English (automatic)

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