An image processing method performed by a processor and including: a step of acquiring a choroidal vascular image; a step of detecting a vortex vein position from the choroidal vascular image; a step of identifying a choroidal vessel related to the vortex vein position; and a step of finding a size of the choroidal vessel.

An image processing method performed by a processor and including: a step of acquiring a choroidal vascular image; a step of detecting a vortex vein position from the choroidal vascular image; a step of identifying a choroidal vessel related to the vortex vein position; and a step of finding a size of the choroidal vessel.

The present disclosure relates to a method for expanding an image database for evaluation of eyewear compatibility. In particular, the present disclosure relates to a method, comprising receiving a user image, receiving a frame image, processing the received frame image by setting, as transparent, pixels of the received frame image except for an anterior face of the frame, defining, within the processed frame image, a left boundary and a right boundary of the anterior face of the frame, the left boundary and the right boundary corresponding to the left eye and the right eye, respectively, receiving a filter image, processing the received filter image by setting, as transparent, pixels in the received filter image outside the frame based on the left boundary and the right boundary, merging the processed frame image and the processed filter image, and overlaying the merged image onto the received user image.

The present disclosure relates to a method for expanding an image database for evaluation of eyewear compatibility. In particular, the present disclosure relates to a method, comprising receiving a user image, receiving a frame image, processing the received frame image by setting, as transparent, pixels of the received frame image except for an anterior face of the frame, defining, within the processed frame image, a left boundary and a right boundary of the anterior face of the frame, the left boundary and the right boundary corresponding to the left eye and the right eye, respectively, receiving a filter image, processing the received filter image by setting, as transparent, pixels in the received filter image outside the frame based on the left boundary and the right boundary, merging the processed frame image and the processed filter image, and overlaying the merged image onto the received user image.

An image calibration method applied to a wide-angle image and executed by an image calibration apparatus includes applying primary lens distortion correction for the wide-angle image to generate a corrected image, segmenting an foreground image from the corrected image to generate a background image, applying secondary distortion correction for the foreground image based on the pre-defined object to generate a calibrated foreground image, fusing the background image with the calibrated foreground image to generate a fused image, detecting at least one residual empty pixel not overlapped by the calibrated foreground image within the fused image, and utilizing a machine learning algorithm to fill the at least one residual empty pixel of the fused image by extending the background image to provide an output image. The foreground image contains feature pixels relate to a pre-defined object and the background image has empty pixels corresponding to the foreground image.

An image calibration method applied to a wide-angle image and executed by an image calibration apparatus includes applying primary lens distortion correction for the wide-angle image to generate a corrected image, segmenting an foreground image from the corrected image to generate a background image, applying secondary distortion correction for the foreground image based on the pre-defined object to generate a calibrated foreground image, fusing the background image with the calibrated foreground image to generate a fused image, detecting at least one residual empty pixel not overlapped by the calibrated foreground image within the fused image, and utilizing a machine learning algorithm to fill the at least one residual empty pixel of the fused image by extending the background image to provide an output image. The foreground image contains feature pixels relate to a pre-defined object and the background image has empty pixels corresponding to the foreground image.

Methods, systems, and non-transitory computer readable media for computing physical dimensions of feet based on user-captured images are described. In at least one embodiment, an exemplary method comprises: receiving, by a server from a user device, an image of the user's foot or feet; segmenting the image to identify the user's foot or feet; computing the one or more physical parameters of the user's foot or feet.

The method for generating a road edge line includes: acquiring a road image; recognizing lane line information from the road image; recognizing key point information related to the road edge from the road image; and generating the road edge line according to the lane line information and the key point information.

The method for generating a road edge line includes: acquiring a road image; recognizing lane line information from the road image; recognizing key point information related to the road edge from the road image; and generating the road edge line according to the lane line information and the key point information.

A device to determine a height disparity between features of an image includes a memory including instructions and processing circuitry. The processing circuitry is configured by the instructions to obtain an image including a first repetitive feature and a second repetitive feature. The processing circuitry is further configured by the instructions to determine a distribution of pixels in a first area of the image, where the first area includes an occurrence of the repetitive features, and to determine a distribution of pixels in a second area of the image, where the second area includes another occurrence of the repetitive features. The processing circuitry is further configured by the instructions to evaluate the distribution of pixels in the first area and the distribution of pixels in the second area to determine a height difference between the first repetitive feature and the second repetitive feature.