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VDI/VDE/VDMA 2632 BLATT 3 - MACHINE VISION/INDUSTRIAL IMAGE PROCESSING - ACCEPTANCE TEST OF CLASSIFYING MACHINE VISION SYSTEMS Organization: VDI
Date: 2017-10-01
Description: The usual legal provisions apply first of all to the acceptance of a classificatory machine vision (industrial image processing, MV system) system (see e.g. § 640 BGB).
SAE/TP - 2006-01-3175 - NEW CONCEPT OF ARTIFICIAL VISION SYSTEM APPLICABILITY TO MEASURE AND CONTROL THE QUALITY OF AUTOMATED FASTENING Organization: SAE/TP
Date: 2006-09-12
Description: Moreover, feeding this information back to the machine controller will lead to a real self-controlled automated process. The Artificial Vision System herein explained has been developed within the frame of AIRBUS ESPAÑA Research and Technology project IC-ASSY.
IEC TS 61496-4-3 - SAFETY OF MACHINERY – ELECTRO-SENSITIVE PROTECTIVE EQUIPMENT – PART 4-3: PARTICULAR REQUIREMENTS FOR EQUIPMENT USING VISION BASED PROTECTIVE DEVICES (VBPD) – ADDITIONAL REQUIREMENTS WHEN USING STEREO VISION TECHNIQUES (VBPDST) - EDITION 1.0 Organization: IEC
Date: 2015-05-01
Description: Replacement: This part of IEC 61496 specifies requirements for the design, construction and testing of electro-sensitive protective equipment (ESPE) designed specifically to detect persons or parts of persons as part of a safety-related system, employing vision-based protective devices (VBPDs) using stereo vision techniques (VBPDST) for the sensing function.
IEC/TR 61496-4 - SAFETY OF MACHINERY – ELECTRO-SENSITIVE PROTECTIVE EQUIPMENT – PART 4: PARTICULAR REQUIREMENTS FOR EQUIPMENT USING VISION BASED PROTECTIVE DEVICES (VBPD) - EDITION 1.0 Organization: IEC
Date: 2007-07-01
Description: It is restricted to the functioning of the ESPE and how it interfaces with the machine. • It is limited to automatic vision-based ESPEs that do not require human intervention for detection.
IEC TS 61496-4-2 - SAFETY OF MACHINERY – ELECTRO-SENSITIVE PROTECTIVE EQUIPMENT – PART 4-2: PARTICULAR REQUIREMENTS FOR EQUIPMENT USING VISION BASED PROTECTIVE DEVICES (VBPD) – ADDITIONAL REQUIREMENTS WHEN USING REFERENCE PATTERN TECHNIQUES (VBPDPP) - EDITION 1.0 Organization: IEC
Date: 2014-06-01
Description: This part of IEC 61496 specifies requirements for the design, construction and testing of electro-sensitive protective equipment (ESPE) designed specifically to detect persons as part of a safety-related system, employing vision-based protective devices (VBPDs) using passive reference patterns (VBPDPP) for the sensing function.
PACKT - PRACTICAL COMPUTER VISION - PRACTICAL COMPUTER VISION - EXTRACT INSIGHTFUL INFORMATION FROM IMAGES USING TENSORFLOW, KERAS, AND OPENCV Organization: PACKT
Date: 2018-02-05
Description: About This Book • Master the different tasks associated with Computer Vision and develop your own Computer Vision applications with ease • Leverage the power of Python, Tensorflow, Keras, and OpenCV to perform image processing, object detection, feature detection and more • With real-world datasets and fully functional code, this book is your one-stop guide to understanding Computer Vision Who This Book Is For This book is for machine learning practitioners and deep learning enthusiasts who want to understand and implement various tasks associated with Computer Vision and image processing in the most practical manner possible.
CRC - NE10738 - CREATION OF A CONSCIOUS ROBOT : MIRROR IMAGE COGNITION AND SELF-AWARENESS Organization: CRC
Date: 2012-08-07
Description: Present-day computers lack well-defined functions to accept various kinds of sensual information such as vision, hearing, and smelling (binding problem).
PACKT - LEARNING OPENCV 3 APPLICA - LEARNING OPENCV 3 APPLICATION DEVELOPMENT Organization: PACKT
Date: 2016-12-19
Description: What You Will Learn • Explore the steps involved in building a typical computer vision/machine learning application • Understand the relevance of OpenCV at every stage of building an application • Harness the vast amount of information that lies hidden in images into the apps you build • Incorporate visual information in your apps to create more appealing software • Get acquainted with how large-scale and popular image editing apps such as Instagram work behind the scenes by getting a glimpse of how the image filters in apps can be recreated using simple operations in OpenCV • Appreciate how difficult it is for a computer program to perform tasks that are trivial for human beings • Get to know how to develop applications that perform face detection, gender detection from facial images, and handwritten character (digit) recognition In Detail Computer vision and machine learning concepts are frequently used in practical computer vision based projects.
ASHRAE - LV-11-C019 - ENVIRONMENTALLY OPPORTUNISTIC COMPUTING: COMPUTATION AS CATALYST FOR SUSTAINABLE DESIGN Organization: ASHRAE
Date: 2011-01-01
Description: Instead of expanding active measures (i.e. mechanical systems) to contend with thermal demands, the EOC concept utilizes existing high performance computing and information communications technology coupled with system controls to enable energy hungry, heat producing data systems to become service providers to a building while concurrently utilizing aspects of a building's HVAC infrastructure to cool the machines; essentially, the building receives ‘free' heat, and the machines receive ‘free' cooling.
CRC - E2230 - AUTOMATION FOR FOOD ENGINEERING : FOOD QUALITY QUANTIZATION AND PROCESS CONTROL Organization: CRC
Date: 2001-06-28
Description: Automation for Food Engineering: Food Quality Quantization and Process Control explores the usage of advanced methods, such as wavelet analysis and artificial neural networks, to automated food quality evaluation and process control. It introduces novel system prototypes, such as machine vision, elastography, and the electronic nose, for food quality measurement, analysis, and prediction.
PACKT - DEEP LEARNING ESSENTIALS - DEEP LEARNING ESSENTIALS - YOUR HANDS-ON GUIDE TO THE FUNDAMENTALS OF DEEP LEARNING AND NEURAL NETWORK MODELING Organization: PACKT
Date: 2018-01-30
Description: What You Will Learn • Get to grips with the core concepts of deep learning and neural networks • Set up deep learning library such as TensorFlow • Fine-tune your deep learning models for NLP and Computer Vision applications • Unify different information sources, such as images, text, and speech through deep learning • Optimize and fine-tune your deep learning models for better performance • Train a deep reinforcement learning model that plays a game better than humans • Learn how to make your models get the best out of your GPU or CPU In Detail Deep Learning a trending topic in the field of Artificial Intelligence today and can be considered to be an advanced form of machine learning, which is quite tricky to master.
CRC - KE38249 - BIG DATA OF COMPLEX NETWORKS Organization: CRC
Date: 2016-08-19
Description: Intended for computer scientists, statisticians and mathematicians interested in the big data and networks, Big Data of Complex Networks is also a valuable tool for researchers in the fields of visualization, data analysis, computer vision and bioinformatics. Key features: Provides a complete discussion of both the hardware and software used to organize big data Describes a wide range of useful applications for managing big data and resultant data sets Maintains a firm focus on massive data and large networks Unveils innovative techniques to help readers handle big data Matthias Dehmer received his PhD in computer science from the Darmstadt University of Technology, Germany.

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