2022年國立體育大學選擇Filamento燈具，進行場館燈光全面換裝，除了提供給選手優質環境，並在環保意識日益高漲，各國正普遍面臨能源有限問題時，讓有限能源運用的更具效率。高效節能的優質燈光提供選手更健康與安全的練習環境，僅原有耗電之37%，亮度增加3倍每小時節電9度。Filamento燈具應用範圍涵蓋航空業、零售業、科技廠、公共場所，全球知名指標客戶有Las Vegas McCarran Airport、Costco好市多、TOSHIBA等。
Author Archive for: Ruby
About Kuo Ruby
This author has yet to write their bio.
Meanwhile lets just say that we are proud Kuo Ruby contributed a whooping 40 entries.
Computer vision and machine vision are overlapping technologies. A machine vision system requires a computer and specific software to operate while computer vision doesn’t need to be integrated with a machine. Computer vision can, for example, analyze digital online images or videos as well as “images” from motion detectors, infrared sensors or other sources, not just a photo or video. Machine vision is a sub-category of computer vision.
Allows system integrator to demonstrate functions by carrying box easily. The plug and play is ideally quickly installation. The customization design for different vertical markets and running product line will be all welcome.
The very core of every machine vision application is the software the performs the actual processing and analysis of the image. At this point specific software tools (“algorithms,” “operators,” etc. depending on the terminology used by the application or library vendor) are configured or programmed to perform specific analysis on the pixel-based data in the acquired image.
In terms of practical implementation, one construct for machine vision software can be described as an application that “configures” the system components and how they execute machine vision functions and tasks. These apps tend to have graphical user interfaces (GUI) devoted to “ease of use” with intuitive and graphically manipulated application configuration steps.