Abstract: There is provided a detection system including a detection device and a post processor. The detection device and the post processor exchange data therebetween using a predetermined communication protocol. The detection device outputs at least one of calculated data and raw data to the post processor in response to each polling according to a request from the post processor. The raw data is provided to the post processor for the machine learning.

Abstract: An optical navigation device control method comprising: (a) computing brightness contrast information of original images captured by an image sensor of an optical navigation device; (b) computing brightness variation levels of the original images; (c) improving image qualities of the original images based on the brightness contrast information and the brightness variation levels, to generate adjusted images; and (d) computing movements of the optical navigation device based on displacement between the adjusted images. The optical navigation device is located on a surface. The step (d) comprises: collecting reference images of different parts of the surface for a plurality of combinations of moving directions of the optical navigation device and placement directions of the surface; and determining a type of the surface via comparing images of a current surface with the reference images.

Abstract: An optical navigation device control method comprising: (a) computing brightness contrast information of original images captured by an image sensor of an optical navigation device; (b) computing brightness variation levels of the original images; (c) improving image qualities of the original images based on the brightness contrast information and the brightness variation levels, to generate adjusted images; and (d) computing movements of the optical navigation device based on displacement between the adjusted images. The optical navigation device is located on a surface. The step (d) comprises: collecting reference images of different parts of the surface for a plurality of combinations of moving directions of the optical navigation device and placement directions of the surface; and determining a type of the surface via comparing images of a current surface with the reference images.

Abstract: A computer readable recording medium comprising at least one program stored therein. An image quality improving method is performed when the program is executed. The image quality improving method comprises: (a) computing brightness contrast information of an original image captured by an image sensor of an optical navigation device; (b) computing a brightness variation level of the original image; and (c) improving an image quality of the original image based on the brightness contrast information and the brightness variation level.

Abstract: There is provided a detection system including a detection device and a post processor. The detection device and the post processor exchange data therebetween using a predetermined communication protocol. The detection device outputs at least one of calculated data and raw data to the post processor in response to each polling according to a request from the post processor. The raw data is provided to the post processor for the machine learning.

Abstract: A computer readable recording medium comprising at least one program stored therein. An image quality improving method is performed when the program is executed. The image quality improving method comprises: (a) computing brightness contrast information of an original image captured by an image sensor of an optical navigation device; (b) computing a brightness variation level of the original image; and (c) improving an image quality of the original image based on the brightness contrast information and the brightness variation level.

Abstract: An image segmentation threshold value determining method, comprising: defining a plurality of image regions of a first sensing image; determining a first, a second part image segmentation threshold values according to a first, second image regions of the image regions; performing a first, a second image segmentation operation to the first sensing image according to the first, the second part image segmentation threshold values to acquire a first, a second segmented images; and selecting one of the first part image segmentation threshold value and the second part image segmentation threshold value as the first image segmentation threshold value according to the first segmented image and the second segmented image.

Abstract: An image segmentation threshold value determining method, comprising: defining a plurality of image regions of a first sensing image; determining a first, a second part image segmentation threshold values according to a first, second image regions of the image regions; performing a first, a second image segmentation operation to the first sensing image according to the first, the second part image segmentation threshold values to acquire a first, a second segmented images; and selecting one of the first part image segmentation threshold value and the second part image segmentation threshold value as the first image segmentation threshold value according to the first segmented image and the second segmented image.