Italy

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. 


ICPR 2020 MAIN CONFERENCE

Low-light is a challenging environment for both human and computer vision to perform tasks such as object classification and detection. Recent works have shown potential in employing enhancements algorithms to support and improve such tasks in low-light, however there has not been any focused analysis to understand the direct effects that low-light enhancement have on an object detector. This work aims to quantify and visualize such effects on the multi-level abstractions involved in network-based object detection. First, low-light image enhancement algorithms are employed to enhance real low-light images, and then followed by deploying an object detection network on the low-light as well as the enhanced counterparts. A comparison of the activations in different layers, representing the detection features, are used to generate statistics in order to quantify the enhancements' contribution to detection. Finally, this framework was used to analyze several low-light image enhancement algorithms and identify their impact on the detection
model and task. This framework can also be easily generalized to any convolutional neural network-based models for the analysis of different enhancements algorithms and tasks.

Exploring the Contributions of Low-light Image Enhancement to Network-based Object Detection

technical paper

Deep Gait Relative Attribute using a Signed Quadratic Contrastive Loss

Mixed Reality-Based Dataset Generation for Learning Based Scan-to-BIM

An Augmented Reality-based Remote Collaboration Platform for Worker Assistance

PRRoad tracking in semi-structured environments using spatial distribution of Lidar data

Respiratory rate estimation using a low-resolution infrared sensor array

Computer-Aided Visual Analysis of Feathers

Multiple Monkey Pose Estimation Using OpenPose

Automatic Analysis of Bees’ Waggle Dance

Fine-tuning for one-look regression vehicle counting in low-shot aerial datasets

City-Scale Point Cloud Stitching Using 2D/3D Registration for Large Geographical Coverage

poster

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Exploring the Contributions of Low-light Image Enhancement to Network-based Object Detection

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Deep Gait Relative Attribute using a Signed Quadratic Contrastive Loss

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