PaMMO - Perception and Modelling for Manipulation of Objects

The pervasiveness of cameras and associated sensors is offering incredible opportunities to improve services and our quality of life, while however simultaneously posing important societal challenges. Enabling multimedia applications and services while protecting privacy is a major challenge that must be addressed to adapt to increased capabilities of classifiers and changing demands from users. In this talk I will cover new privacy preserving analytical frameworks for single and multimedia sensor data, such as images, videos, audio, as well as accelerometer and gyroscope data. Specifically, I will discuss as applications image sharing in social media, mobile-based health, and video capturing, and talk about desirable (and undesirable) inferences from various types of data.

Privacy-preserving Machine Learning for Multimedia Data

One of the challenging problems in digital image forensics is the capability to identify images that are captured by the same camera device. This knowledge can help forensic experts in gathering intelligence about suspects by analyzing digital images. In this paper, we propose a two-part network to quantify the likelihood that a given pair of images have the same source camera, and we evaluated it on the benchmark Dresden data set containing 1851 images from 31 different cameras. To the best of our knowledge, we are the first ones addressing the challenge of device-based image matching. Though the proposed approach is not yet forensics ready, our experiments show that this direction is worth pursuing, achieving at this moment 85 percent accuracy. This ongoing work is part of the EU-funded project 4NSEEK concerned with forensics against child sexual abuse.

People-centric, Trustworthy AI: Humans Enhancing AI and AI Enhancing Humans

[![](https://assets.underline.io/uploads/markdown_image/1/image/98bbd29b3a7633de61894a3a21feba0c.png)](https://sites.google.com/view/pammo-icpr2020) [![](https://assets.underline.io/uploads/markdown_image/1/image/60d54d8c0e3dd52db4cf1815fa980546.png)](https://sites.google.com/view/pammo-icpr2020)

Object recognition, modelling, and pose estimation of objects for robot manipulation has achieved great advances in recent vision and robotics conferences (e.g.,  6D pose challenges at vision conferences). However, vision still struggles with recognition under partial occlusions (e.g., in clutter or when an object is held by a human) and robots are still unable to handle everyday objects under realistic conditions in home, care, and service settings. Despite significant recent advances in data-driven grasping and manipulation, there are still considerable challenges to be addressed at the intersection of perception, robotics hardware, learning and grasp prediction. For example, learning to approach and grasp objects does not generalize beyond constrained objects and settings, object grasps are limited to specific robot embodiments,and perception does not generalize to cluttered scenes and objects touching or partially occludingeach other. Furthermore, evaluation and benchmarking (e.g., with the YCB object set and the YCB and Amazon challenges) is yet to become a standard for publishing advances in this domain. 

PaMMO will focus on recent advances, open problems, and next steps to be taken in  recognitionand modelling for the handover and manipulation of everyday objects. The Workshop includes invited talks by prominent researchers from different disciplines (e.g., vision and robotics for object manipulation), an open discussion, interactive videos and demos accompanied by posters.

WS22 - PaMMO - Perception and Modelling for Manipulation of Objects // HALF DAY

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; 
- 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

Keynotes

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.
Contributions describing applications of Pattern Recognition techniques to real-world problems, interdisciplinary research, experimental and/or theoretical studies yielding new insights that advance Pattern Recognition methods are especially encouraged.

ICPRAM 2020

The International Conference on Pattern Recognition Applications and Methods is a major point of contact between researchers, engineers and practitioners on the areas of Pattern Recognition and Machine Learning, both from theoretical and application perspectives.

Andrea Cavallaro

3

SHORT BIO

Presentations

PaMMO - Perception and Modelling for Manipulation of Objects

Privacy-preserving Machine Learning for Multimedia Data

People-centric, Trustworthy AI: Humans Enhancing AI and AI Enhancing Humans

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