Machine Vision with Dimensional Data Reduction
A machine vision and data technology, applied in digital video signal modification, instruments, computer components, etc., can solve problems such as utility limitations
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
example 1
[0069] Example 1 - Navigation Handler
[0070] Machine Vision Applications Describes Navigators Effective in Virtual Reality Environments. However, according to one example of the present disclosure, the navigator is also effective in real world environments. Accordingly, various embodiments of the present disclosure may be used in a variety of environments and in a variety of applications.
[0071] For example, a mobile robot called Turtlebot can be used. Turtlebot is an open source personal robot designed for robotics development and testing. Turtlebot runs on the Robot Operating System (ROS), which facilitates hardware and communication mechanisms, and brings data from sensors and hardware components on the robot together into a single software framework. The robot includes a 3-wheeled circular locomotive base from a Yujin robot called iClebo Kobuki, a Microsoft Kinect sensor including a camera, and an onboard factory-calibrated gyroscope for better sensor input and stat...
example 2
[0089] Example 2 - Vision Task
[0090] As mentioned above, the methods described in machine vision applications allow machine learning algorithms to learn features of the visual world efficiently and in a generalized manner. Such methods do this by reducing the dimensionality of the visual input (eg, using retinal encoding). This application is concerned with applying one or more additional dimensionality reduction methods to the encoded data in such a way that a machine learning algorithm (such as a convolutional neural network, or CNN) when searching the parameter space (such as discovering weights in a CNN), finds a general solution instead of falling into a local solution (e.g. due to a local minimum in the parameter space).
[0091] For example, in various embodiments, the solution for a given training data set may be a set of weights that capture transformations (or calculations or mappings). Reducing the dimensionality of the training set allows the algorithm to find...
PUM
Login to View More Abstract
Description
Claims
Application Information
Login to View More 


