Bøger i Advances in Computer Vision an serien

DEFECT PROPORTION OF DETECTION INITIAL RATE DETECTION RATE INSPECTOR 3 COMPLEXITY OF TIMES PAN OF PERFORMING o~ ________________________ o~ ______________________ __ -;. INSPECTION TASK -;. VISUAL INSPECTION Fagure 1. Trends in relations between the complexity of inspection tasks, defect detection rates (absolute and relative), and inspection time. Irrespective of the necessities described above, and with the excep­ tion of specific generic application systems (e.g., bare-board PCB inspection, wafer inspection, solder joint inspection, linewidth measure­ ment), vision systems are still not found frequently in today's electronics factories. Besides cost, some major reasons for this absence are: 1. The detection robustness or accuracy is still insufficient. 2. The total inspection time is often too high, although this can frequently be attributed to mechanical handling or sensing. 3. There are persistent gaps among process engineers, CAD en­ gineers, manufacturing engineers, test specialists, and computer vision specialists, as problems dominate the day-to-day interac­ tions and prevent the establishment of trust. 4. Computer vision specialists sometimes still believe that their contributions are universal, so that adaptation to each real problem becomes tedious, or stumbles over the insufficient availabIlity of multidisciplinary expertise. Whether we like it or not, we must still use appropriate sensors, lighting, and combina­ tions of algorithms for each class of applications; likewise, we cannot design mechanical handling, illumination, and sensing in isolation from each other.

With recent advances in multimedia technology, on-line libraries of digital images are assuming an ever increasing relevance within a wide range of information systems. Effective access to these archives requires not only conventional searching techniques based on keywords, but also content-based queries which allow visual features of data to be searched. This new comprehensive reference introduces the intricate science of implementing image modeling and retrieval based on the shape, on the color and texture, or on the spatial arrangement of imaged objects. Building on this introduction, six chapters by major research groups in the field detail and compare different modeling approaches.

Visual Question Answering (VQA) usually combines visual inputs like image and video with a natural language question concerning the input and generates a natural language answer as the output. This is by nature a multi-disciplinary research problem, involving computer vision (CV), natural language processing (NLP), knowledge representation and reasoning (KR), etc.Further, VQA is an ambitious undertaking, as it must overcome the challenges of general image understanding and the question-answering task, as well as the difficulties entailed by using large-scale databases with mixed-quality inputs. However, with the advent of deep learning (DL) and driven by the existence of advanced techniques in both CV and NLP and the availability of relevant large-scale datasets, we have recently seen enormous strides in VQA, with more systems and promising results emerging.This book provides a comprehensive overview of VQA, covering fundamental theories, models, datasets, and promising future directions. Given its scope, it can be used as a textbook on computer vision and natural language processing, especially for researchers and students in the area of visual question answering. It also highlights the key models used in VQA.

Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.