According to an embodiment, an image processing device includes one or more processors. The one or more processors are configured to: acquire an image; detect a first repeated pattern from the image; detect an object included in the first repeated pattern; and output the object as a second repeated pattern.

A material determining device comprising a first image sensor, a second image sensor, and a light source is provided. The material determining method comprises: (a) sensing a first image by the first image sensor according to light from the light source; (b) sensing a second image by the second image sensor according to the light; and (c) determining whether material corresponding to material images in the first image and the second image is first type of material or second type of material, according to locations of the material images in the first image and the second image and according to shapes of the material images in the first image and the second image. By this way an electronic device using the material determining device can properly operate according to the type of material.

A material determining device comprising a first image sensor, a second image sensor, and a light source is provided. The material determining method comprises: (a) sensing a first image by the first image sensor according to light from the light source; (b) sensing a second image by the second image sensor according to the light; and (c) determining whether material corresponding to material images in the first image and the second image is first type of material or second type of material, according to locations of the material images in the first image and the second image and according to shapes of the material images in the first image and the second image. By this way an electronic device using the material determining device can properly operate according to the type of material.

Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

Image modification using detected symmetry is described. In example implementations, an image modification module detects multiple local symmetries in an original image by discovering repeated correspondences that are each related by a transformation. The transformation can include a translation, a rotation, a reflection, a scaling, or a combination thereof. Each repeated correspondence includes three patches that are similar to one another and are respectively defined by three pixels of the original image. The image modification module generates a global symmetry of the original image by analyzing an applicability to the multiple local symmetries of multiple candidate homographies contributed by the multiple local symmetries. The image modification module associates individual pixels of the original image with a global symmetry indicator to produce a global symmetry association map. The image modification module produces a manipulated image by manipulating the original image under global symmetry constraints imposed by the global symmetry association map.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, using the model to predict the low-probability stochastic defect, determining a process window based on the low-probability stochastic defect, and controlling, based on the process window, a lithography tool to manufacture a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

In one embodiment, a method includes generating a model trained to predict a low-probability stochastic defect, calibrating, using unbiased measurement data, the model to a specific lithography process, patterning process, or both to generate a calibrated model, using the calibrated model to predict the low-probability stochastic defect; and modifying, based on the low-probability stochastic defect, a variable, parameter, setting, or some combination of a manufacturing process of a device.

The texture data acquired by the first acquisition unit is processed so as to accord with a size of the region set by the setting unit, and the illumination data acquired by the second acquisition unit is processed so as to accord with the size of the region set by the setting unit. The decoration data is generated from the texture data processed by the first processing unit and the illumination data processed by the second processing unit, and the decoration data is applied to the region set by the setting unit are comprised.