Abstract: A system, device and method are provided for generating image processing models for selected hardware. The method, illustratively, includes obtaining a reference model, a desired image resolution based on target hardware, and a training set of images comprising images with the desired image resolution and images with a higher resolution. The method includes generating an updated model by: iteratively training the reference model with a combined set of features, the combined set of features comprising features determined from the images with the higher resolution with at least one stem and features determined from the images with the desired resolution. The method includes outputting the trained updated model to the target hardware to process images with the desired image resolution.

Abstract: According to an aspect of the present invention, there is provided a method of detecting a repetitive pattern. The method includes: clustering a plurality of pixels that form an input image according to color and obtaining one or more color layers composed of pixels included in each cluster; selecting one or more effective layers from the color layers, wherein each of the effective layers includes a predetermined number or more of pixel components, each composed of a plurality of pixels and having a predetermined shape or a predetermined size of area; selecting a unit pattern repeatedly disposed at different locations in each effective layer from the pixel components included in each effective layer; calculating distances between the unit patterns in each effective layer; and calculating a repetition cycle of the unit pattern of the input image based on the calculated distances in each effective layer.

Abstract: Provided is a data processing method which can increase data processing speed without adding a new node to a distributed system. The data processing method may include: calculating a conversion number of cores corresponding to a number of processing blocks included in a graphics processing unit (GPU) of a node of a distributed system; calculating a adding up number of cores by adding up a number of cores included in a central processing unit (CPU) of the node of the distributed system and the conversion number of cores; splitting job data allocated to the node of the distributed system into a number of job units data equal to the adding up number of cores; and allocating a number of job units data equal to the number of cores included in the CPU to the CPU of the node of the distributed system and a number of job units data equal to the conversion number of cores to the GPU of the node of the distributed system.

Abstract: According to an aspect of the present invention, there is provided a method of detecting a repetitive pattern. The method includes: clustering a plurality of pixels that form an input image according to color and obtaining one or more color layers composed of pixels included in each cluster; selecting one or more effective layers from the color layers, wherein each of the effective layers includes a predetermined number or more of pixel components, each composed of a plurality of pixels and having a predetermined shape or a predetermined size of area; selecting a unit pattern repeatedly disposed at different locations in each effective layer from the pixel components included in each effective layer; calculating distances between the unit patterns in each effective layer; and calculating a repetition cycle of the unit pattern of the input image based on the calculated distances in each effective layer.

Abstract: According to an aspect of the present invention, there is provided a method of detecting a repetitive pattern. The method includes: clustering a plurality of pixels that form an input image according to color and obtaining one or more color layers composed of pixels included in each cluster; selecting one or more effective layers from the color layers, wherein each of the effective layers includes a predetermined number or more of pixel components, each composed of a plurality of pixels and having a predetermined shape or a predetermined size of area; selecting a unit pattern repeatedly disposed at different locations in each effective layer from the pixel components included in each effective layer; calculating distances between the unit patterns in each effective layer; and calculating a repetition cycle of the unit pattern of the input image based on the calculated distances in each effective layer.

Abstract: Provided is a data processing method which can increase data processing speed without adding a new node to a distributed system. The data processing method may include: calculating a conversion number of cores corresponding to a number of processing blocks included in a graphics processing unit (GPU) of a node of a distributed system; calculating a adding up number of cores by adding up a number of cores included in a central processing unit (CPU) of the node of the distributed system and the conversion number of cores; splitting job data allocated to the node of the distributed system into a number of job units data equal to the adding up number of cores; and allocating a number of job units data equal to the number of cores included in the CPU to the CPU of the node of the distributed system and a number of job units data equal to the conversion number of cores to the GPU of the node of the distributed system.

Abstract: According to an aspect of the present invention, there is provided a method of detecting a repetitive pattern. The method includes: clustering a plurality of pixels that form an input image according to color and obtaining one or more color layers composed of pixels included in each cluster; selecting one or more effective layers from the color layers, wherein each of the effective layers includes a predetermined number or more of pixel components, each composed of a plurality of pixels and having a predetermined shape or a predetermined size of area; selecting a unit pattern repeatedly disposed at different locations in each effective layer from the pixel components included in each effective layer; calculating distances between the unit patterns in each effective layer; and calculating a repetition cycle of the unit pattern of the input image based on the calculated distances in each effective layer.