Adobe, Inc.

Alok Kumar Singh

Angad Kumar Gupta

Ram Prasad Purumala

Vikas Yadav

Swapnil Shrivastava

Nitish Singla

A technique for selecting a representative image from a group of digital images includes extracting data representing an image of a face of a person from each image in the group using a face recognition algorithm, determining a score for each image based on one or more quality parameters that are satisfied for the respective image, and selecting the image having the highest score as the representative image for the group. The quality parameters may be based on any quantifiable characteristics of the data. Each of these quality parameters may be uniquely weighted, so as to define the relative importance of one parameter with respect to another. The score for determining the representative image of the group may be obtained by adding together the weights corresponding to each quality parameter that is satisfied for a given image. Once selected, the representative image may be displayed in a graphical user interface.

Representative image selection for image management using face recognition

Photograph organization based on facial recognition is described. In one or more embodiments, a photograph organization module obtains multiple photographs having images of faces and recognizes the faces in the multiple photographs. The module builds a population by attempting to distinguish individual persons among the faces in the multiple photographs, with each person of the population corresponding to a group of multiple groups. After a first pass through the faces, the population includes a number of duplicative persons. With a second pass, the photograph organization module reduces the number of duplicative persons in the population by merging two or more groups of the multiple groups to produce a reduced number of groups. The merging is performed based on comparisons of the faces corresponding to the two or more groups. The multiple photographs are organized based on the reduced number of groups. Organization can include tagging or displaying grouped photographs.

Photograph organization based on facial recognition

Systems and methods are presented for generating a new digital output image by blending a plurality of digital input images capturing the same scene at different levels of exposure. Each new pixel for the new digital output image is derived from a group of corresponding aligned pixels from the digital input images. In order to determine a weight for each pixel in each group of mutually-aligned source-image pixels, a weight distribution function is applied to values of an image characteristic for the pixels in the group of corresponding aligned pixels, and a net weight is subsequently assigned to each of the pixels in the group. Pixel values of pixels in each group of mutually-aligned source-image pixels are modified based on the net weights assigned to the pixels in order to obtain a new pixel value for a corresponding new pixel in the new digital output image.

Blending of exposure-bracketed images using weight distribution functions

A technique that may be used to configure a computer system to automatically search a video and select a frame within the video clip that best represents the content of that video. A frame contained in a video is identified. Luminance values for each pixel in a plurality of pixels contained in the frame are then generated. The frame is then selected as a poster frame for the video if a luminance value for at least one pixel in the plurality of pixels contained in the identified frame is outside a range of luminance values. The range of luminance values may be determined from a luminance value associated with a reference pixel contained in the frame.

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Noida, India

Alok Kumar Singh