Corel Corporation

Other

Jasc Software, Inc.

Mikhail Ivanovich Trifonov

Leonid Borisovich Lalyko

Krzysztof Antoni Zaklika

Kryzstof Antoni Zaklika

Pavel Vladimirovich Gruzdev

Olga Vadimovna Sharonova

Viktor Antol'evich Nesterov

Viktor Antol'eylch 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

A boundary in an image is identified by identifying a search region within the image. The process continues by determining image gradients in the search region and determining multiple color regions within the search region. An active contour representing the boundary is created based on the image gradients and the multiple color regions.

Method and apparatus for selecting an object in an image

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.

A method of automatic contrast correction comprises providing digital image data, representing the data in the form of a brightness histogram, determining a measure of central tendency for said histogram, adding a shift value to the measure of central tendency to provide an adjusted measure of central tendency, using the both the determined and adjusted measures of central tendency to determine the exponent of a tone reproduction curve, and applying the tone reproduction curve to the brightness values of the image. Additionally, the method may provide further correction by applying to the brightness values of the image an S-shaped tone reproduction curve that does not change a measure of central tendency of the brightness histogram.

Automatic contrast enhancement

A method for reducing image noise in a digital image including establishing image data of image pixel values; specifying a local virtual surface patch; fitting the local virtual surface patch to the image data; using the local virtual surface patch to estimate a palliative value of at least one pixel in the region of the local virtual surface patch; replacing an original pixel value with the palliative value.

Fine moire correction in images

Line defects are removed from an image by providing image data in digital form, analyzing segments of the image data as groups of pixels, detecting line defects in the image by application of a line detector, such as a local radial angular transform, and adjusting the image data to correct the detected line defects within the determined limits.

Assisted scratch removal

A computer with a monitor viewable program executes to display a histogram of image data on the monitor. The monitor contemporaneously displays a screen image viewed on a monitor of the histogram, and displays a digital or analog control element that controls both compression and expansion of midtones. Activation of the midtone compression/expansion element alters the displayed image according to effects of programs underlying the control of the element. This histogram display and element are associated on a single logical screen display in which at least one other image data modification effect is present on the screen image as an addressable image data modification effect.

Growing community of inventors

Sosnovyi Bor, Russia

Mikhail Ivanovich Trifonov