Italy

ICPR 2020

Real-time Cross-dataset Quality Production Assessment in Industrial Laser Cutting Machines

unsupervised domain adaptive re-identification feature adversarial learning·self-similarity clustering

technical paper

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; 
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- 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. 


SURVANT: an innovative semantics-based Surveillance Video Archives Investigation Assistant

Assessement of CNN-Based Methods for Poverty Estimation from Satellite images


This paper presents chewing data analysis based on the new approach of chewing detection for diet monitoring applications. The proposed approach is based on chewing detection using a proximity sensor in capturing the temporalis muscle movement during chewing. The aim is to support the development of non-contact-based chewing detection. A wearable device of eye-glass is used with the sensor mounted at the right side of the eyeglass temple using a 3D printed housing. The main activity involved in this study is resting and eating, three test food that represents different food hardness (carrot, banana, and apple) with a portion of one spoonful. Several upper cut-off frequencies (fc2) of bandpass filters were used during the analyses. The signals were evaluated using accuracy and F1-score for classification and the absolute mean of error for chewing count estimation. In the classification stage, using the setting of a 10-fold cross-validation method and a 3s segmented window, fc2 of 6Hz gives the highest accuracy with 97.6%, while, 2.5Hz gives the lowest accuracy of 92.6%. However, in the chewing count estimation stage, which is based on a 240s segmented window, 2.4Hz able to give a smaller percentage absolute error of 2.69%, compare to 6Hz with 12.11%. It can be concluded that the chewing frequency was under 2.5Hz, but, the self-reporting labeling approach used in this study reduced the accuracy of the system as fc2 equal to 2.5Hz is used. Further analysis of chewing count shows that might in relating the total chewing count with different food hardness.

Analysis of Chewing Signals Based on Chewing Detection Using Proximity Sensor for Diet Monitoring

King-Sun Fu Award: Ching Yee Suen

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https://d3smihljt9218e.cloudfront.net/transcript/9115/subtitle/fef6d27c13eb25a735e449982bddfd67.vtt

English (automatic)

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Real-time Cross-dataset Quality Production Assessment in Industrial Laser Cutting Machines

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