The main difference between machine vision and computer vision is their focus and application:
Machine Vision: Primarily used in industrial settings for tasks like quality control, inspection, and automation. It focuses on capturing images for specific applications, such as detecting defects on a production line.
Computer Vision: A broader field that involves enabling machines to understand and interpret visual information from the world. It includes applications like facial recognition, object detection, and scene understanding, and is used in various industries beyond just manufacturing.
computer graphic is a branch of computer science thats deals with theory and technique of computer image synthesis computer vision is a field that include method for acquiring processing ,analysing and understanding image
The output as a result of IMAGE PROCESSING is an image ie a transformed image(enhanced) but in case of COMPUTER VISION the output is usually a decsion
What is the difference between the artillerymans Vision and look at this
Nello Zuech has written: 'Understanding and applying machine vision' -- subject(s): Computer vision
explain the difference between binocular and panoramic vision
Liang Wang has written: 'Machine learning for human motion analysis' -- subject(s): Motion perception (Vision), Computer vision, Image analysis, Human locomotion, Machine learning
In idea is more restricted than a vision. That is to say, a vision may consist of a number of interrelated ideas.
●Active Stereo Vision ●Two or more cameras placed at different locations ●One is usually stationery other can be moving ●Passive Stereo Vision ●Two cameras separated by a distance known as base length in the same plane
Mission is what you are DOING and in some cases how you do it. Vision is where you are going as an organization.
That's such a rediculise question! They are the same thing!
Vision Statement is the big picture--what you or the organization want to become. Mission Statement is how this vision will be implemented.
Masashi Sugiyama has written: 'Density ratio estimation in machine learning' -- subject(s): COMPUTERS / Computer Vision & Pattern Recognition, Estimation theory, Machine learning