Название: Cognitive Systems and Signal Processing in Image ProcessingАвтор: Yu-Dong Zhang, Arun Kumar Sangaiah
Издательство: Academic Press/Elsevier
Год: 2022
Страниц: 378
Язык: английский
Формат: pdf (true)
Размер: 16.3 MB
Cognitive Systems and Signal Processing in Image Processing presents different frameworks and applications of cognitive signal processing methods in image processing. This book provides an overview of recent applications in image processing by cognitive signal processing methods in the context of Big Data and Cognitive AI. It presents the amalgamation of cognitive systems and signal processing in the context of image processing approaches in solving various real-word application domains. This book reports the latest progress in cognitive big data and sustainable computing. Various real-time case studies and implemented works are discussed for better understanding and more clarity to readers. The combined model of cognitive data intelligence with learning methods can be used to analyze emerging patterns, spot business opportunities, and take care of critical process-centric issues for computer vision in real-time.
A crowd-counting web application based on the convolutional neural network (CNN) model can quickly solve the user’s crowd-counting problem, while increasing the user’s understanding of the crowd density in the image, reducing the time to count the number of people and avoiding safety hazards. For example, when a company organizes an employee party, due to the large number of participants taking photos, the photos can be uploaded to the network application for crowd counting to avoid employee accidents caused by counting errors. The application of CNNs in crowd counting is one of the research directions of CNN models, and crowd density is one of the indicators of safety in public places. CNNs are robust to image processing and classification, and the convolution operation is the main difference between CNNs and traditional neural networks.
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