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HALCON Progress18.05深度学习的CPU版
来自:未知    2018-06-19 15:40
 
最新HALCON 18.05 版本于2018年5月22日发布,新增功能如下:

CPU Inference
 
With HALCON 18.05, customers will be able to perform deep learning inference on a CPU.
This CPU inference has been highly optimized for Intel®-compatible x86 CPUs. In tests, this resulted in a typical inference execution time on a standard Intel CPU (8 threads) that achieves performance similar to a midrange GPU.
Removing the need for a dedicated GPU greatly increases the operational flexibility. E.g., industrial PCs that usually are not designed for housing large and powerful GPUs can now easily be used for deep-learning-powered classification (inference).

 
Improved Bar Code Reader

HALCON 18.05 features optimized edge detection, which improves the ability to reliably read bar codes with very small line widths as well as strongly blurred codes. Moreover, the quality of the bar codes is also verified in accordance with the most recent version of the ISO/IEC 15416 standard.
 
Enhanced Deflectometry
 
The deflectometry functionality introduced in HALCON 17.12 now includes a new pattern type that improves the precision and robustness of error detection especially on partially specular surfaces like varnished metal sheets.
 
3D Improvements
 
The deflectometry functionality introduced in HALCON 17.12 now includes a new pattern type that improves the precision and robustness of error detection especially on partially specular surfaces like varnished metal sheets.
  
Automatic Handle Clearning

HALCON 18.05 also makes it much more comfortable to work with handles by clearing these automatically once they are no longer required. This significantly reduces the risk of creating memory leaks and makes writing "safe code" much simpler.
 
 Support for Hypercentric Lenses
 
A new camera model within HALCON now allows the corrections of distortions in images that were recorded with hypercentric (also known as pericentric) camera lenses. These lenses can depict several sides of an object simultaneously, thus enabling a convergent view of the test object. With this technology, users only need a single camera system for inspection and identification tasks, e.g., the inspection of cylindrical objects.
 
HDevEngine Improvements

The HDevelop library export feature has been expanded: Developers can now access HDevelop procedures not just in C++, but also in .NET via an exported wrapper – as easily and intuitively as a native function. This significantly facilitates the development process.
 
 
 
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