Abstract: Examples are directed to selective editing of digital images using editing tools having persistent tool settings. A source image is opened as a target image in an image editing application. Once opened in the image editing application, the target image includes a plurality of image layers. Visual elements of the target image file are then edited using tools in the image editing application. For each tool used, a tool identifier and the relevant tool settings are saved and associated with the corresponding layer in a target image file. Upon closing and later re-opening the target image file, the saved tool identifier and tool settings are presented for display for further image editing in subsequent user sessions.

Abstract: Various embodiments receive frames as a stream captured during a camera session. During the camera session, faces and facial features of each face are detected from the frames. Then, each face in each frame is assigned a score based on the detected facial features. Using the scores, a candidate frame is selected for each individual face to represent a “best” representation of that face. In addition, an overall score is calculated for each frame based on a combination of assigned scores for the faces in the frame. Then, a reference frame is located from the frames based on the overall score for a respective frame. Faces from the candidate frames are then merged onto the reference frame, and an output image is generated for display.

Abstract: Various embodiments include automatically selecting and modifying objects within an image that are similar to an object previously selected by a user. A user first selects an object in a displayed image. Object feature descriptors are generated describing a shape formed by edges of the object, and image feature descriptors are generated describing shapes formed by edges automatically detected in the image. The object feature descriptors are compared with the image feature descriptors to automatically identify other objects in the image having edges similar to the selected object. The pixels of the user selected object and the automatically identified objects are modified according to a user specified function.

Abstract: Techniques for removal of obstructions in a digital image scene are described, in which target and source digital images that exhibit parallax, one to another, are obtained that were captured together by an image capture device at a similar point in time using two different lenses of the image capture device. A foreground obstruction is identified based on displacement in apparent position of objects in the target and source digital images. The foreground obstruction is removed from the target digital image, such as by generating an obstruction mask that represents the location of the foreground obstruction and copying pixels from the source digital image to the target digital image based on the locations identified in the obstruction mask. The target digital image with the obstruction removed is output to a user interface or service provider system, for example.

Abstract: Dynamic thumbnails are described. Dynamic thumbnails provide a convenient and automated approach for providing thumbnails that are contextually relevant to a user. In at least some implementations, an input image is analyzed to generate tags describing objects or points of interest within the image, and to generate rectangles that describe the locations within the image that correspond to the generated tags. Various combinations of generated tags are analyzed to determine the smallest bounding rectangle that contains every rectangle associated with the tags in the respective combination, and a thumbnail is created. A user input is received and compared to the tags associated with the generated thumbnails, and a thumbnail that is most relevant to the user input is selected and output to the user.

Abstract: Techniques for converting mechanical markings on hardcopy textual content into digital annotations in a digital document file. In accordance with some embodiments, the techniques include identifying at least one block of text in a digital (scanned) image of a hardcopy document, and identifying at least one mechanical marking in the digital image of the hardcopy document. The mechanical marking, such as an underline, strike-through, highlight or circle, covers or lies adjacent to the corresponding block of text. An annotated digital document file is generated from the digital image of the hardcopy document. The annotated digital document file includes computer-executable instructions representing the original text of the hardcopy document and at least one annotation corresponding to the mechanical marking in the hardcopy document.

Abstract: Various embodiments receive frames as a stream captured during a camera session. During the camera session, faces and facial features of each face are detected from the frames. Then, each face in each frame is assigned a score based on the detected facial features. Using the scores, a candidate frame is selected for each individual face to represent a “best” representation of that face. In addition, an overall score is calculated for each frame based on a combination of assigned scores for the faces in the frame. Then, a reference frame is located from the frames based on the overall score for a respective frame. Faces from the candidate frames are then merged onto the reference frame, and an output image is generated for display.

Abstract: A context aware clipping mask is described. Context aware clipping masks may provide an automated approach for creating clipping sets that generate aesthetically pleasing photo texts. In at least some implementations, an input image is analyzed to generate an image saliency map and an image gradient map. A plurality of image rects of varying sizes and aspect ratios are generated on a grid over the input image. A score is assigned to each image rect, incorporating a visual composition, a boundary simplicity based on the image gradient map, and a content preservation based on the image saliency map. An image rect with the highest score is selected and utilized to create a photo text from the input image.

Abstract: Facilitating text identification and editing in images in which in one or more embodiments, a user selection of a location in an area of text in an image is received. Given the location, a region of interest that includes text (including the location of the user selection) in the image is determined. Distortion resulting from a surface in the image on which the text is situated being at some angle other than parallel to the image capture plane is also corrected. One or more fonts and font sizes of the text in the region of interest are also detected. Various actions can be taken on the text in the region of interest (e.g., editing the text and/or identifying the text).

Abstract: Disclosed are various embodiments for automatically selecting and modifying objects within an image that are similar to an object previously selected by a user. A user first selects an object in a displayed image. Object feature descriptors are generated describing a shape formed by edges of the object, and image feature descriptors are generated describing shapes formed by edges automatically detected in the image. The object feature descriptors are compared with the image feature descriptors to automatically identify other objects in the image having edges similar to the selected object. The pixels of the user selected object and the automatically identified objects are modified according to a user specified function.

Abstract: A reflection removal system is capable of automatically removing from digital images reflections related to photographic illumination sources (“flash”), at the time of capture, without user intervention. In an embodiment, the reflection removal system receives a set of digital images taken at substantially the same time from a camera, where one of the image is affected by flash. The images are divided into blocks, and a threshold is determined for each block, indicating the brightest pixel value related to the content of the block. The reflection removal system compares each pixel in the flash-affected image to the corresponding threshold, and generates a digital mask based on the comparison. The reflection removal system creates a corrected image based on the digital mask, such that pixels affected by flash are modified based on corresponding pixels in the other images. In an embodiment, the corrected image is color blended.

Abstract: A content aware pattern stamping tool that preserves the visual texture of an area within an image when filling the area with a pattern is provided. In one embodiment, the pattern stamping tool analyzes an area to be filled with a pattern to determine textural characteristics of the area. These textural characteristics may include reflectance and shading. Once the pattern stamping tool has determined the textural characteristics of the area, the pattern stamping tool fills the area with the pattern and applies the textural characteristics to the filled area. Through application of the textural characteristics, the pattern stamping tool generates an image that combines the pattern with the original textural characteristics of the area.

Abstract: A user identifies an unwanted object in a source image. Related images are identified on the basis of timestamp and/or geolocation metadata. Matching masks are identified in the source image, wherein each of the matching masks is adjacent to the selection mask. Features in the selection and matching masks which also appear in one of the related images are identified. The related image having a maximum of features which are tracked to a source image matching mask, but also a minimum of features which are tracked to the source image selection mask, is identified as a best-match related image. By mapping the source image matching masks onto the best-match related image, a seed region can be located in the best-match related image. This seed region is used for filling in the source image. This allows the unwanted object to be replaced with a visually plausible background having a reasonable appearance.

Abstract: Techniques for image resolution enhancement based on data from related images are described. In one or more implementations, a cropped image and each related image from a set of the related images are divided into blocks that each include a subset of pixels. In at least some implementations, the blocks in the related images have features that match features of respective counterpart blocks in the cropped image. Then, blocks in the related images that have a relatively higher resolution than the respective counterpart blocks in the cropped image are determined. Based on this determination, one or more of the counterpart blocks in the cropped image are replaced with respective blocks from the related images to enhance the image resolution of at least a portion of the cropped image.

Abstract: Image selection control techniques are described. In one or more implementations, a method is described to control image selection in a user interface of a computing device. An input is detected by the computing device involving interaction with a display of an image in a user interface. A pattern is identified by the computing device in a portion of the image that includes the detected input. One or more objects of the content are located by the computing device that include the identified pattern, at least one said object positioned outside the portion of the image used to perform the identifying. A selection of the image is created as having the located one or more objects by the computing device in the user interface.

Abstract: Examples are directed to selective editing of digital images using editing tools having persistent tool settings. A source image is opened as a target image in an image editing application. Once opened in the image editing application, the target image includes a plurality of image layers. Visual elements of the target image file are then edited using tools in the image editing application. For each tool used, a tool identifier and the relevant tool settings are saved and associated with the corresponding layer in a target image file. Upon closing and later re-opening the target image file, the saved tool identifier and tool settings are presented for display for further image editing in subsequent user sessions.

Abstract: The present disclosure is directed towards systems and methods for modifying a digital image. For example, systems and methods described herein involve identifying a target portion of a digital image to remove from the digital image. The systems and methods further involve identifying geometric features intersecting or surrounding the identified target portion. The systems and methods further involve analyzing and identifying a source portion having geometric properties that correspond to the identified geometric features intersecting the target portion. Further, the systems and methods involve removing the target portion and replacing the target portion with the source portion by aligning the geometric properties to blend together with the background surrounding the removed target portion. In this way, the output image includes a replacement portion in place of the removed target portion that blends together with the background of the output image.

Abstract: A user identifies an unwanted object in a source image. Related images are identified on the basis of timestamp and/or geolocation metadata. Matching masks are identified in the source image, wherein each of the matching masks is adjacent to the selection mask. Features in the selection and matching masks which also appear in one of the related images are identified. The related image having a maximum of features which are tracked to a source image matching mask, but also a minimum of features which are tracked to the source image selection mask, is identified as a best-match related image. By mapping the source image matching masks onto the best-match related image, a seed region can be located in the best-match related image. This seed region is used for filling in the source image. This allows the unwanted object to be replaced with a visually plausible background having a reasonable appearance.

Abstract: Examples are directed to selective editing of digital images using editing tools having persistent tool settings. A source image is opened as a target image in an image editing application. Once opened in the image editing application, the target image includes a plurality of image layers. Visual elements of the target image file are then edited using tools in the image editing application. For each tool used, a tool identifier and the relevant tool settings are saved and associated with the corresponding layer in a target image file. Upon closing and later re-opening the target image file, the saved tool identifier and tool settings are presented for display for further image editing in subsequent user sessions.

Abstract: Examples are directed to selective editing of digital images using editing tools having persistent tool settings. A source image is opened as a target image in an image editing application. Once opened in the image editing application, the target image includes a plurality of image layers. Visual elements of the target image file are then edited using tools in the image editing application. For each tool used, a tool identifier and the relevant tool settings are saved and associated with the corresponding layer in a target image file. Upon closing and later re-opening the target image file, the saved tool identifier and tool settings are presented for display for further image editing in subsequent user sessions.