Image Processing

Computer vision and image processing have made significant progress in many real-world applications, including environmental monitoring and protection. Recent studies have shown that computer vision and image processing can be used to quantify water turbidity, a crucial physical parameter in water quality assessment. This paper presents a procedure to determine water turbidity using deep learning methods, specifically, convolutional neural network (CNN). At first, water samples were located inside a dark cabin before digital images of the samples were captured with a smartphone camera.

ABSTRACT

Europe is covered by distinct climatic zones which include semiarid, the Mediterranean, humid subtropical, marine,

humid continental, subarctic, and highland climates. Land use and land cover change have been well documented in the

past 200 years across Europe1where land cover grassland and cropland together make up 39%2. In recent years, the

agricultural sector has been affected by abnormal weather events. Climate change will continue to change weather

Surveillance video captured by Multi-intensity infrared illuminator.

GT(ground-truths) :bounding boxes of 'person' in channel 2,4 and 6 by following the Pascal VOC format.

Most of Facial Expression Recognition (FER) systems rely on machine learning approaches that require large databases (DBs) for an effective training. As these are not easily available, a good solution is to augment the DBs with appropriate techniques, which are typically based on either geometric transformation or deep learning based technologies (e.g., Generative Adversarial Networks (GANs)). Whereas the first category of techniques have been fairly adopted in the past, studies that use GAN-based techniques are limited for FER systems.