Newsletters

LETTER FROM THE EDITOR Dear Colleague, Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks, but these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them. The Embedded Vision Alliance will delve into these […]

LETTER FROM THE EDITOR Dear Colleague, Deep neural networks (DNNs) are proving very effective for a variety of challenging machine perception tasks, but these algorithms are very computationally demanding. To enable DNNs to be used in practical applications, it’s critical to find efficient ways to implement them. The Embedded Vision Alliance will delve into these

LETTER FROM THE EDITOR Dear Colleague, TensorFlow has become a popular framework for creating machine learning-based computer vision applications, especially for the development of deep neural networks (DNNs). If you’re planning to develop computer vision applications using deep learning and want to understand how to use TensorFlow to do it, then don’t miss an upcoming

LETTER FROM THE EDITOR Dear Colleague, TensorFlow has become a popular framework for creating machine learning-based computer vision applications, especially for the development of deep neural networks. If you’re planning to develop computer vision applications using deep learning and want to understand how to use TensorFlow to do it, then don’t miss an upcoming full-day,

COMPUTER VISION FOR IMAGE UNDERSTANDING Semantic Segmentation for Scene Understanding: Algorithms and Implementations Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very

COMPUTER VISION FOR IMAGE UNDERSTANDING Semantic Segmentation for Scene Understanding: Algorithms and Implementations Recent research in deep learning provides powerful tools that begin to address the daunting problem of automated scene understanding. Modifying deep learning methods, such as CNNs, to classify pixels in a scene with the help of the neighboring pixels has provided very

DEEP LEARNING FOR VISION Software Frameworks and Toolsets for Deep Learning-based Vision Processing Deep learning is an increasingly popular and robust alternative to classical computer vision algorithms. This technical article from the Embedded Vision Alliance and member companies Au-Zone Technologies, BDTI, MVTec, Synopsys and Xilinx covers the leading deep learning software frameworks, the reasons for

DEEP LEARNING FOR VISION Software Frameworks and Toolsets for Deep Learning-based Vision Processing Deep learning is an increasingly popular and robust alternative to classical computer vision algorithms. This technical article from the Embedded Vision Alliance and member companies Au-Zone Technologies, BDTI, MVTec, Synopsys and Xilinx covers the leading deep learning software frameworks, the reasons for

LETTER FROM THE EDITOR Dear Colleague, TensorFlow has become a popular framework for creating machine learning-based computer vision applications, especially for the development of deep neural networks. If you’re planning to develop computer vision applications using deep learning and want to understand how to use TensorFlow to do it, then don’t miss next Thursday's full-day,

LETTER FROM THE EDITOR Dear Colleague, TensorFlow has become a popular framework for creating machine learning-based computer vision applications, especially for the development of deep neural networks. If you’re planning to develop computer vision applications using deep learning and want to understand how to use TensorFlow to do it, then don’t miss next month's full-day,