Canon Kabushiki Kaisha

Alibaba Group Holding Limited

Xin Ji

Xinwu Chen

Libing Wang

Yali Wang

Yu Wang

Wenlong Xie

Honghui He

Yukun Chen

Pengjie Zhao

Chunxia Li

Yoshihiro Ishida

Jianmin Wang

Jiahuan Ye

Linlin Liu

Embodiments of the present application relate to a system for implementing screen capture, a method for implementing screen capture, and a computer program product for implementing screen capture. A method for implementing screen capture is provided. The method includes providing multiple types of screen capture adapters, selecting a screen capture adapter that is of a type corresponding to a preset mobile terminal application type as a target type screen capture adapter, the screen capture adapters being selected from among the provided multiple types of screen capture adapters, registering the target type screen capture adapter, providing screen capture implementing techniques that correspond to the multiple types of screen capture adapters, and performing screen capture by invoking a screen capture implementing technique that corresponds to the registered target type screen capture adapter, the screen capture implementing technique being selected from among the provided screen capture implementing techniques.

System and method for implementing screen capture

In an image classification method, dividing an input image into blocks; obtaining block features of each block of the image; performing an evaluation of each block based on the block features thereof; obtaining image features based on the evaluations of the blocks of the image; and classifying the image based on the image features into pre-defined categories.

Method, apparatus and storage medium for image classification

The invention relates to a human eye detection method, apparatus, system and storage medium. According to the invention, candidate eye areas are detected from a given image by: reading in the image; analyzing the image and getting a list of candidate eye areas; selecting one unverified candidate eye area from said list; determining a neighborhood region of the selected candidate eye area; calculating the neighborhood region's size, which is recorded as S; processing the region, obtaining dark areas; counting the number of dark areas, which number is recorded as N; comparing the ratio N/S to a predetermined first threshold, if the ratio N/S is smaller than said first threshold, the candidate eye area is determined as a false eye area and deleted from the list; else, the candidate eye area is determined as a real eye area and remains in the list; repeating foresaid selecting step to comparing step until there is no unverified candidate eye area in the list; and outputting the list for purpose of subsequent processing of the image. The invention may be applied to human body detection.

Human eye detection method, apparatus, system and storage medium

A method of processing an image includes steps of identifying one candidate for a human face region within an image; calculating a probability that the candidate for human face region represents a human face; and saving the probability as attached information to the image. The method of processing an image can also include steps of identifying one candidate for human face region within an image; calculating a probability that the candidate for human face region represents a human face; judging whether or not the candidate for human face region represents a human face by comparing the probability with a threshold; and saving a result of the step of judging as attached information to the image. According to these methods, results of identifying candidates for human face regions will be saved to the image, and further processes to be conducted on the image can be facilitated.

Image processing method, apparatus and storage medium

The present invention provides a method of processing an image, which includes the steps of identifying one candidate for human face region within the image, selecting a mouth neighborhood within the candidate for human face region, processing the mouth neighborhood, and classifying the candidate for human face region based on results of the processing step. According to the method of the present invention, human faces are detected just based on pixels included in the mouth neighborhood, but not all the pixels of the entire face.

Method and apparatus for processing an image

For a subset of pixels in an image in which it is desired to detect a human face if one is present, a first variable is derived from the gray-level distribution of the image, and a second variable is derived from a preset reference distribution that is characteristic of the object. The correspondence between the first variable and the second variable is then evaluated over the subset of pixels, and a determination is made as to whether the image contains the object, based on the result of this evaluation.

Image processing methods and apparatus for detecting human eyes, human face, and other objects in an image

Disclosed is a method, apparatus, and system for detecting a human face in an image. The method includes, for a subset of pixels in the image, deriving a first variable from the gray-level distribution of the image and deriving a second variable from a preset reference distribution that is characteristic of the object. The method further includes evaluating the correspondence between the first variable and the second variable over the subset of pixels and determining if the image contains the object based on the result of this evaluation.

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Beijing, China

Xin Ji