توضیحات
Computer Vision Metrics: Survey, Taxonomy, and Analysis provides a technical tour through computer vision, with a survey of nearly 100 types of local, regional, and global feature descriptors, blending history of the field with state-of-the-art analysis of contemporary methods, rather than just another how-to book with source code shortcuts and performance analysis. Observations are provided to develop intuition behind the methods and mathematics, interesting questions are raised for future research rather than providing all the answers, and a Vision Taxonomy is suggested to draw a conceptual map of the field. Extensive illustrations are included, with over 540 references to the literature in the comprehensive bibliography to dig deeper.
Computer Vision Metrics explores the key questions behind the design and mathematics of computer vision metrics and feature descriptors, providing a comprehensive survey and taxonomy of what methods are used, with analysis and observations about why the methods work. Several 3D depth sensing methods are surveyed including MVS, stereo, and structured light.
This work focuses on a slice through the field from the view of feature description metrics, or how to describe, compute, and design the macro-features and micro-features
that make up larger objects in images. The focus is on the pixel-side of the vision pipeline, with a light introduction to the back-end training, classification, machine learning, and matching stages.
Computer Vision Metrics is written for engineers, scientists, and academic researchers in areas including video analytics, scene understanding, machine vision, face recognition, gesture recognition, pattern recognition, general object analysis, media processing, and computational photography.
What You’ll Learn
Current status, brief history, and future directions for computer vision metrics
Taxonomy of local binary, gradient & other spectra, shape features, and basis spaces
Overview of 2D image sensing, 3D depth sensing, and image preprocessing
Vision pipeline optimization methods for computer vision applications
Characterization of ten OpenCV detectors using synthetic feature alphabets
About the Author
Scott Krig is a pioneer in computer imaging, computervision, and graphics visualization. He founded KrigResearch in 1988 (krigresearch.com), providing theworlds first imaging and vision systems based on
high-performance engineering workstations,super-computers, and dedicated imaging hardware,serving customers worldwide in 25 countries. Scott hasprovided imaging and vision solutions around the globe,and has worked closely with many industries, includingaerospace, military, intelligence, law enforcement,government research, and academic organizations.
More recently, Scott has worked for major corporations and startups servingcommercial markets, solving problems in the areas of computer vision, imaging, graphics,visualization, robotics, process control, industrial automation, computer security,cryptography, and consumer applications of imaging and machine vision to PCs, laptops,mobile phones, and tablets. Most recently, Scott provided direction for Intel Corporationin the area of depth-sensing and computer vision methods for embedded systems andmobile platforms.
Scott is the author of many patent applications worldwide in the areas of embeddedsystems, imaging, computer vision, DRM, and computer security, and studied at Stanford.
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ترجمه ماشینی :
معیارهای بینایی رایانه: بررسی، طبقهبندی و تجزیه و تحلیل یک گشت و گذار فنی از طریق بینایی رایانه ارائه میکند، با بررسی نزدیک به 100 نوع توصیفگر ویژگی محلی، منطقهای و جهانی، تاریخچه را ترکیب میکند. این رشته با تجزیه و تحلیل پیشرفته روشهای معاصر، بهجای اینکه فقط یک کتاب چگونه با میانبرهای کد منبع و تجزیه و تحلیل عملکرد باشد. مشاهدات برای توسعه شهود در پس روشها و ریاضیات ارائه میشوند، سوالات جالب برای تحقیقات آینده به جای ارائه همه پاسخها مط
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