A CNN operates on the disparity or motion outputs of a block matching hardware module, such as a DMPAC module, to produce refined disparity or motion streams which improve operations in images having ambiguous regions. As the block matching hardware module provides most of the processing, the CNN can be small and thus able to operate in real time, in contrast to CNNs which are performing all of the processing. In one example, the CNN operation is performed only if the block hardware module output confidence level is below a predetermined amount. The CNN can have a number of different configurations and still be sufficiently small to operate in real time on conventional platforms.

A CNN operates on the disparity or motion outputs of a block matching hardware module, such as a DMPAC module, to produce refined disparity or motion streams which improve operations in images having ambiguous regions. As the block matching hardware module provides most of the processing, the CNN can be small and thus able to operate in real time, in contrast to CNNs which are performing all of the processing. In one example, the CNN operation is performed only if the block hardware module output confidence level is below a predetermined amount. The CNN can have a number of different configurations and still be sufficiently small to operate in real time on conventional platforms.

Disclosed is a real-time detection method of a block motion based on a feature point recognition, including following steps: S1, calibrating a camera; S2, shooting digital images of a block on a surface of a breakwater by the camera, and sending the digital images to a digital signal processing system based on a field programmable gate array; S3, carrying out a feature point detection of the block in the images by the digital signal processing system; S4, carrying out a coordinate conversion after the feature point detection; S5, comparing position changes of the feature points of the block before and after a test, making a difference between coordinates of the two images before and after the test to obtain a change value of the feature points, and obtaining a displacement of the block; and S6, displaying displacement calculation results on a data processor.

Disclosed is a real-time detection method of a block motion based on a feature point recognition, including following steps: S1, calibrating a camera; S2, shooting digital images of a block on a surface of a breakwater by the camera, and sending the digital images to a digital signal processing system based on a field programmable gate array; S3, carrying out a feature point detection of the block in the images by the digital signal processing system; S4, carrying out a coordinate conversion after the feature point detection; S5, comparing position changes of the feature points of the block before and after a test, making a difference between coordinates of the two images before and after the test to obtain a change value of the feature points, and obtaining a displacement of the block; and S6, displaying displacement calculation results on a data processor.

An image signal processor, an operating method thereof, and an image processing system are provided. The image processing system includes: a control processor configured to generate and output setting information corresponding to N (where N is an integer of 2 or more) image frames; and an image signal processor configured to perform image processing on the N image frames received from an image sensor based on the setting information, and generate an interrupt signal and transmit the interrupt signal to the control processor based on completion of the image processing performed on the N image frame.

An image signal processor, an operating method thereof, and an image processing system are provided. The image processing system includes: a control processor configured to generate and output setting information corresponding to N (where N is an integer of 2 or more) image frames; and an image signal processor configured to perform image processing on the N image frames received from an image sensor based on the setting information, and generate an interrupt signal and transmit the interrupt signal to the control processor based on completion of the image processing performed on the N image frame.

An apparatus includes at least one memory configured to store instructions, and at least one processor in communication with the at least one memory and configured to execute the instructions to perform same area dividing on each of a plurality of images, and create a composite image from the plurality of images. A first area of the composite image is composited from corresponding areas in a first number of image among the plurality of images. A second area of the composite image is composited from corresponding areas in a second number of image among the plurality of images.

An image pickup apparatus includes a lens, an image pickup device, and a processor. The processor acquires a plurality of pieces of first image data and second image data with an exposure time period longer than an exposure time period of the first image data from the image pickup device, detects a motion region by using the first image data and the second image data, and acquires motion detection information by using the plurality of pieces of first image data. The processor changes a synthesis method in accordance with whether the motion region is detected and generates one piece of synthesized image data.

An image pickup apparatus includes a lens, an image pickup device, and a processor. The processor acquires a plurality of pieces of first image data and second image data with an exposure time period longer than an exposure time period of the first image data from the image pickup device, detects a motion region by using the first image data and the second image data, and acquires motion detection information by using the plurality of pieces of first image data. The processor changes a synthesis method in accordance with whether the motion region is detected and generates one piece of synthesized image data.

A computer-implemented method for tracking includes obtaining an infrared image and a visible image from an imaging device supported by a carrier of an unmanned aerial vehicle (UAV), obtaining a combined image based on the infrared image and the visible image, identifying a target in the combined image, and generating control signals for tracking the identified target using the imaging device.