Corel Corporation

Other

Leonid Borisovich Lalyko

Mikhail Ivanovich Trifonov

Krzysztof Antoni Zaklika

Pavel Vladimirovich Gruzdev

Viktor Antol'evich Nesterov

Kryzstof Antoni Zaklika

Vladimir Alezandrovich Khvatov

Viktor Antol'eylch Nesterov

Victor Anatol'evich Nesterov

Properties of pixels in a digital image are sampled within different subdivisions of an editing tool impression to produce different property distributions. The different subdivisions can automatically alter their size, geometry, and/or location, based on image content within one or more of the subdivisions, in order to encompass a set of pixels having a substantially uniform distribution of a pixel property. Uniformity can be defined relative to the editing operation or context of the image. The property distributions from each region are classified to identify different edit classes within the property space, which are then used to apply an edit effect to the digital image within the tool impression. The edit classes may be represented by an edit profile in two or more dimensions.

Adaptive sampling region for a region editing tool

Properties of pixels of a digital image are sampled within different subdivisions of an editing tool impression to produce different pixel property distributions. The property distributions from each region may be automatically classified to identify different edit classes within the property space, which are then used to apply an edit effect to the digital image within the tool impression. The edit classes are represented by an edit profile, the generation of which may be completely automated based on selection of a tool impression, or partially automated using the selection of the tool impression and receipt of classification guidance input, such as one or more parameters received from user input or a configuration file. The edit classes may also be generated without reference to the pixel property distributions, such as via user input.

Assisted adaptive region editing tool

An exemplary method includes receiving an image data set that comprises a multidimensional property space and data classifiable into data classes, determining a projection vector for data of the data set wherein the projection vector maximizes a ratio of between-class scatter to within-class scatter, selecting a reference for the vector, projecting at least some of the data onto the vector, measuring distances from the reference to at least some of the data, classifying at least some of the data into data classes based on a nesting analysis of the distances, eliminating the classified data from the image data set to produce a modified image data set and deciding whether to determine another projection vector for the modified image data set. Various other exemplary methods, devices, systems, etc, are also disclosed.

Iterative fisher linear discriminant analysis

Adaptive sampling region

Properties of pixels of a digital image are sampled within different subdivisions of an editing tool impression to produce different property distributions. The subdivisions may be differently-located within the tool impression. The property distributions from each region are classified to identify different edit classes within the property space, which are then used to apply an edit effect to the digital image within the tool impression. The edit classes may be represented by an edit profile in two or more dimensions (e.g., applying to one or more pixel properties).

Adaptive region editing tool

An exemplary method includes receiving an image data set that comprises a multidimensional property space and data classifiable into data classes, determining a projection vector for data of the data set wherein the projection vector maximizes a ratio of between-class scatter to within-class scatter, selecting a reference for the vector, projecting at least some of the data onto the vector, measuring distances from the reference to at least some of the data, classifying at least some of the data into data classes based on a nesting analysis of the distances, eliminating the classified data from the image data set to produce a modified image data set and deciding whether to determine another projection vector for the modified image data set. Various other exemplary methods, devices, systems, etc., are also disclosed.

After identification of an image or image area where correction is to be effected for red-eye defects, and identification of eye regions within the image or the image area, color data from the area is provided, collected or analyzed according to color channel information. The color channel information may be defined for specific or collections of wavelength values or color content values, usually with at least two channels, up to four or more color channels used to carry all of the information (and with digital images, pixel information) for each color. By using color channel information, local color content information can be readily accessed and evaluated, and after the exact nature or degree of a red-eye effect is determined from evaluation of the color channel information, the data may be readily corrected by a number of different techniques. Templates of colors and/or templates for features (such as glint, pigmentation, or artistic features) may be added to identified regions to be corrected for red-eye

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St. Petersburg, Russia

Leonid Borisovich Lalyko