Gør som tusindvis af andre bogelskere
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.Du kan altid afmelde dig igen.
This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.This book provides numerous ways that deep learners can use for logo recognition, including: Deep learning-based end-to-end trainable architecture for logo detection Weakly supervised logo recognition approach using attention mechanisms Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images Approach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.
This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems.In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
Tilmeld dig nyhedsbrevet og få gode tilbud og inspiration til din næste læsning.
Ved tilmelding accepterer du vores persondatapolitik.