Khronos Releases OpenVX 1.2
Khronos has today unveiled the OpenVX 1.2 specification from this week's Embeded Vision Alliance Summit (EVS).
OpenVX is Khronos' specification for cross-platform accelerated vision processing. OpenVX 1.2 features improvements around object detection and recognition, classification operations for detection/recognition based on a set of features, enhanced range of image processing operations, and more.
"Three new extensions released alongside OpenVX 1.2 enable the import and export of verified, optimized graphs, 16-bit image operations, and neural network inferencing acceleration. The import/export extension enables a user to “compile” a graph offline, save or “export” it, and then at run-time efficiently “import” and execute it. The 16-bit extension provides signed 16-bit image data support for most image operations. The neural network extension introduces OpenVX graph nodes corresponding to common neural network operation layers, e.g. convolution, deconvolution, activation, normalization, pooling, and softmax, to enable the expression and low-power acceleration of neural network-based algorithms such as object detection and recognition," more details at Khronos.org.
OpenVX is Khronos' specification for cross-platform accelerated vision processing. OpenVX 1.2 features improvements around object detection and recognition, classification operations for detection/recognition based on a set of features, enhanced range of image processing operations, and more.
"Three new extensions released alongside OpenVX 1.2 enable the import and export of verified, optimized graphs, 16-bit image operations, and neural network inferencing acceleration. The import/export extension enables a user to “compile” a graph offline, save or “export” it, and then at run-time efficiently “import” and execute it. The 16-bit extension provides signed 16-bit image data support for most image operations. The neural network extension introduces OpenVX graph nodes corresponding to common neural network operation layers, e.g. convolution, deconvolution, activation, normalization, pooling, and softmax, to enable the expression and low-power acceleration of neural network-based algorithms such as object detection and recognition," more details at Khronos.org.
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