Application of Digital Images and Corresponding Image Retrieval Paradigm

We live in a world where digital images are constantly generated during our daily activities, whether private or business. They play an important role in our private life, showing important moments, people, places, or events and keeping their memory. Images are unavoidable in business, especially in digital marketing, web sales, social networks, medicine, security, and education. In general, images contribute to a better understanding of the message, increase the attractiveness of textual content, provide a better user experience, and can convey emotion quickly. The key advantage of the image is that very often, even a cursory glance at the image is enough to convey a message and arouse emotion and interest. But with the increase in digital image numbers, storage, organization, and retrieval problems arise. The paper describes the importance of images in different areas of application and different image retrieval paradigms that include text-based, content-based, and combined approaches. Also, the most popular image search tools and cloud storage services are compared and discussed. The conclusion comments on the applicability of existing approaches to image searches in different application domains and highlights the advantages and disadvantages of each of the approaches

Application of Deep Learning Methods for Detection and Tracking of Players

This chapter deals with the application of deep learning methods in sports scenes for the purpose of detecting and tracking the athletes and recognizing their activities. The scenes recorded during handball games and training activities will be used as an example. Handball is a team sport played with the ball with well-defined goals and rules, with a given number of players who can participate in the game as well as their roles. Athletes move quickly throughout the field during the game, change position and roles from defensive to offensive, use different techniques and actions, and very often are partially or completely occluded by another athlete. If artificial lighting and cluttered background are additionally taken into account, it is clear that these are very challenging tasks for object detectors and trackers. The chapter will present the results of various experiments that include player and ball detection using state-of-the-art deep convolutional neural networks such as YOLO v3 or Mask R-CNN, player tracking using Deep Sort, key player determination using activity measures, and action recognition using LSTM. In the conclusion, open issues and challenges in applying deep learning methods in such a dynamic sports environment will be discussed

Development and evaluation of a brine mining equipment monitoring and control system using wireless sensor network and fuzzy logic

The brine mining equipment failure can seriously affect the productivity of the salt lake chemical industry. Traditional monitoring and controlling method mainly depends on manned patrol that is offline and ineffective. With the rapid advancement of information and communication technologies, it is possible to develop more efficient online systems that can automatically monitor and control the mining equipment and to prevent equipment damage from mechanical failure and unexpected interruptions with severe consequences. This paper describes a Wireless Monitoring and feedback fuzzy logic-based Control System (WMCS) for monitoring and controlling the brine well mining equipment. Based on the field investigations and requirement analysis, the WMCS is designed as a Wireless Sensors Network module, a feedback fuzzy logic controller, and a remote communication module together with database platform. The system was deployed in existing brine wells at demonstration area without any physical modification. The system test and evaluation results show that WMCS enables to track equipment performance and collect real-time data from the spot, provides decision support to help workers overhaul the equipment and follows the deployment of fuzzy control in conjunction with remote data logging. It proved that WMCS acts as a tool to improve management efficiency for mining equipment and underground brine resources