Siamese Convolutional Neural Networks to Quantify Crack Pattern Similarity in Masonry Facades

This paper proposes an automated approach to predict crack pattern similarities that correlate well with assessment by structural engineers. We use Siamese convolutional neural networks (SCNN) that take two crack pattern images as inputs and output scalar similarity measures. We focus on 2D masonry facades with and without openings. The image...

Measuring the dynamics of camouflage in natural scenes using convolutional neural networks

Natural scenes are typically highly heterogeneous, making it difficult to assess camouflage effectiveness for moving objects since their local contrast varies with their momentary position. Camouflage performance is usually assessed through visual search and detection experiments involving human observers. However, such studies are time...

Measuring the dynamics of camouflage in natural scenes using convolutional neural networks

Natural scenes are typically highly heterogeneous, making it difficult to assess camouflage effectiveness for moving objects since their local contrast varies with their momentary position. Camouflage performance is usually assessed through visual search and detection experiments involving human observers. However, such studies are time...

Conditioning of Deep-Learning Surrogate Models to Image Data with Application to Reservoir Characterization

Machine Learning for Digital Twins to Predict Responsiveness of Cyber-Physical Energy Systems

Cyber-Physical Systems are becoming more autonomous, interconnected, complex and adaptive, and are expected to operate in highly dynamic environments. This is especially challenging for energy ecosystems that are increasingly difficult to control and maintain as the number of participating manufacturers and users grows. Digital Twins help analyze...

Fast and accurate person re-identification with xception conv-net and C2F

Person re-identification (re-id) is the task of identifying a person of interest across disjoint camera views in a multi-camera system. This is a challenging problem due to the different poses, viewpoints and lighting conditions. Deeply learned systems have become prevalent in the person re-identification field as they are capable to deal with...

Fast and accurate person re-identification with Xception and Conv-Net and C2F

Flexible image analysis for law enforcement agencies with deep neural networks to determine: Where, who and what

Due to the increasing need for effective security measures and the integration of cameras in commercial products, a huge amount of visual data is created today. Law enforcement agencies (LEAs) are inspecting images and videos to find radicalization, propaganda for terrorist organizations and illegal products on darknet markets. This is time...

On the importance of visual explanation and segmentation for SAR ATR using deep learning

Gesture recognition with a low power FMCW radar and a deep convolutional neural network

Gesture recognition with radar enables remote control of consumer devices such as audio equipment, television sets and gaming consoles. In this paper, experimental results of hand gesture recognition with a low power FMCW radar and a deep convolutional neural network (CNN) are presented. The FMCW radar operates in the 24 GHz ISM frequency band...