Emerging Topics in Pattern Recognition and Artificial Intelligence

The unique compendium covers a wide range of recent advanced contributions in Pattern Recognition and Artificial Intelligence, both in theoretical aspects and applications. It highlights the importance of Deep Learning in various domains, from acquisition to Decision Making.

Written by world renowned contributors, this high-quality research works presents case studies that can potentially help them find approaches and resources to address their scientific problems.

It is a useful reference text for professionals, researchers, academics and graduate students in the fields of artificial intelligence, machine learning and deep learning.

Contents:

• Graph Augmentation Using Matching-Graphs (Mathias Fuchs and Kaspar Riesen)
• Controlling Topology Preserving Graph Pyramids (Walter G Kropatsch, Majid Banaeyan, and Rocio Gonzalez-Diaz)
• A Sensor-Independent Multi-Modal Fusion Scheme for Human Activity Recognition (Anastasios Alexiadis, Alexandros Nizamis, Dimitra Zotou, Dimitrios Giakoumis, Konstantinos Votis, and Dimitrios Tzovaras)
• Metrics for Saliency Map Evaluation of Deep Learning Explanation Methods (Tristan Gomez, Thomas Freour, and Harold Mouchere)
• Efficient Segmentation of E-Waste Devices With Deep Learning for Robotic Recycling (Cristof Rojas, Antonio Rodriguez-Sanchez, and Erwan Renaudo)
• Shop Signboard Detection Using the ShoS Dataset (Mrouj Almuhajri and Ching Y Suen)
• Self-Distilled Self-Supervised Monocular Depth Estimation (Julio Mendoza and Helio Pedrini)
• An Encoder-Decoder Approach to Offline Handwritten Mathematical Expression Recognition with Residual Attention (Qiqiang Lin, Chunyi Wang, Ning Bi, Ching Y Suen, and Jun Tan)
• A Complexity Analysis on the General Feasibility of Patch-Based Adversarial Attacks on Semantic Segmentation Problems (Andras Horvath and Soma Kontar)
• Sentiment and Word Cloud Analysis of Tweets Related to COVID-19 Vaccines before, during, and after the Second Wave in India (Anmol Bansal, Arjun Choudhry, Anubhav Sharma, and Seba Susan)
• Reinforcement Learning and Sequential QAP-Based Graph Matching for Semantic Segmentation of Images (Jeremy Chopin, Jean-Baptiste Fasquel, Harold Mouchere, Rozenn Dahyot, and Isabelle Bloch)
• FEM and Multi-Layered FEM: Feature Explanation Methods with Statistical Filtering of Important Features (Alexey Zhukov, Jenny Benois-Pineau, Romain Giot, Romain Bourqui, and Luca Bourroux)

Readership: Researchers, professionals, academics and graduate students in artificial intelligence, machine learning and deep learning.