Abstract: The present disclosure describes systems, non-transitory computer-readable media, and methods for detecting and indicating modifications between a digital image and a modified version of a digital image. For example, the disclosed systems generates an ordered collection of change records in response to detecting modifications to the digital image. The disclosed systems generates determine one or more non-contiguous modified regions of pixels in the digital image based on the change records. The disclosed system generate an edited region indicator corresponding to the non-contiguous modified regions. The disclosed systems can further color-code the edited region indicator at an object level based on objects in the modified version of the digital image.

Abstract: Facial expressions depicted in image data are edited based on variations of facial expressions depicted across a plurality of frames in other image data. The facial expression of a target subject, depicted in a first image data set, is edited based on the facial expression of a preview subject depicted in a second image data set. The target subject's facial expression is automatically edited based on variations in the of the preview subject's facial expression. A camera device captures video image data of the preview subject. The camera provides a live data image feed to a face-editing engine. The engine edits the face of the target subject's based on the varying face of the preview subject. In real time, for each frame of the data image feed, a user interface simultaneously displays both the varying face of the preview subject and the edited face of the target subject.

Abstract: Computer-implemented systems and methods herein disclose automatic haze correction in a digital video. In one example, a video dehazing module identifies a scene including a set of video frames. The video dehazing module identifies the dark channel, brightness, and atmospheric light characteristics in the scene. For each video frame in the scene, the video dehazing module determines a unique haze correction amount parameter by taking into account the dark channel, brightness, and atmospheric light characteristics. The video dehazing module applies the unique haze correction amount parameters to each video frame and thereby generates a sequence of dehazed video frames.

Abstract: Depicted skin selection is described. An image processing system selects portions of a digital image that correspond to exposed skin of persons depicted in the digital image without selecting other portions. Initially, the image processing system determines a bounding box for each person depicted in the digital image. Based solely on the portion of the digital image within the bounding box, the image processing system generates an object mask indicative of the pixels of the digital image corresponding to a respective person. Portions of the digital image outside the bounding box are not used for generating this object mask. The image processing system then identifies the pixels of the digital image indicated by the object mask and having a similar color to a range of exposed skin colors determined for the respective person. The processing system generates skin selection data describing the identified pixels and enabling the exposed skin selection.

Abstract: Depicted skin selection is described. An image processing system selects portions of a digital image that correspond to exposed skin of persons depicted in the digital image without selecting other portions. Initially, the image processing system determines a bounding box for each person depicted in the digital image. Based solely on the portion of the digital image within the bounding box, the image processing system generates an object mask indicative of the pixels of the digital image corresponding to a respective person. Portions of the digital image outside the bounding box are not used for generating this object mask. The image processing system then identifies the pixels of the digital image indicated by the object mask and having a similar color to a range of exposed skin colors determined for the respective person. The processing system generates skin selection data describing the identified pixels and enabling the exposed skin selection.

Abstract: Computer-implemented systems and methods herein disclose automatic haze correction in a digital video. In one example, a video dehazing module identifies a scene including a set of video frames. The video dehazing module identifies the dark channel, brightness, and atmospheric light characteristics in the scene. For each video frame in the scene, the video dehazing module determines a unique haze correction amount parameter by taking into account the dark channel, brightness, and atmospheric light characteristics. The video dehazing module applies the unique haze correction amount parameters to each video frame and thereby generates a sequence of dehazed video frames.

Abstract: In various implementations, methods and systems are disclosed for accurately selecting a targeted portion of a digital image. In one embodiment, a selection cursor having a central and a peripheral region is provided. The central region is used to force a selection or a deselection and therefore moving the central region over a portion of the image causes that portion of the image to be selected or deselected, respectively. The peripheral region of the cursor surrounds the central region and defines an area where a hybrid level set algorithm for both boundary detection and region definition, particularly a matting region, is performed. This provides highly accurate boundary detection and matting region selection within a narrowly-focused peripheral region and eliminates the need to subsequently designate a matting region and apply a matting algorithm to complex portions of an object selection.

Abstract: Computer-implemented systems and methods herein disclose automatic haze correction in a digital video. A video dehazing module divides a digital video into multiple scenes, each scene including a set of video frames. For each scene, the video dehazing module identifies the dark channel, brightness, and atmospheric light characteristics in the scene. For each video frame in the scene, the video dehazing module determines a unique haze correction amount parameter by taking into account the dark channel, brightness, and atmospheric light characteristics. For each video frame, the video dehazing module also determines a unique haze correction sensitivity parameter by taking into account transmission map values in the scene. The video dehazing module applies the unique haze correction amount parameters and unique haze correction sensitivity parameters to each video frame generate a sequence of dehazed video frames.

Abstract: Selection refinement tool techniques are described in which a selection within an image is modified using a tool that automatically resizes according to the shape of selection boundaries. In one or more implementations, selection boundaries are displayed in an image that is exposed in a user interface. The selection boundaries may represent an automatic selection via an editing tool, or a selection that is based on manual manipulation of an editing tool by a user. The selection refinement tool is operable to modify the selection boundaries by positioning of a cursor of the refinement tool proximate to the selection boundaries. In response to positioning of the cursor relative to the selection boundaries, the cursor is dynamically resized in dependence upon a positional relationship between the cursor and the selection boundaries, such that boundaries of the cursor are kept from intersecting with the selection boundaries.

Abstract: Facial expressions depicted in image data are edited based on variations of facial expressions depicted across a plurality of frames in other image data. The facial expression of a target subject, depicted in a first image data set, is edited based on the facial expression of a preview subject depicted in a second image data set. The target subject's facial expression is automatically edited based on variations in the of the preview subject's facial expression. A camera device captures video image data of the preview subject. The camera provides a live data image feed to a face-editing engine. The engine edits the face of the target subject's based on the varying face of the preview subject. In real time, for each frame of the data image feed, a user interface simultaneously displays both the varying face of the preview subject and the edited face of the target subject.

Abstract: Computer-implemented systems and methods herein disclose automatic haze correction in a digital video. A video dehazing module divides a digital video into multiple scenes, each scene including a set of video frames. For each scene, the video dehazing module identifies the dark channel, brightness, and atmospheric light characteristics in the scene. For each video frame in the scene, the video dehazing module determines a unique haze correction amount parameter by taking into account the dark channel, brightness, and atmospheric light characteristics. For each video frame, the video dehazing module also determines a unique haze correction sensitivity parameter by taking into account transmission map values in the scene. The video dehazing module applies the unique haze correction amount parameters and unique haze correction sensitivity parameters to each video frame generate a sequence of dehazed video frames.

Abstract: In various implementations, methods and systems are disclosed for accurately selecting a targeted portion of a digital image. In one embodiment, a selection cursor having a central and a peripheral region is provided. The central region is used to force a selection or a deselection and therefore moving the central region over a portion of the image causes that portion of the image to be selected or deselected, respectively. The peripheral region of the cursor surrounds the central region and defines an area where a hybrid level set algorithm for both boundary detection and region definition, particularly a matting region, is performed. This provides highly accurate boundary detection and matting region selection within a narrowly-focused peripheral region and eliminates the need to subsequently designate a matting region and apply a matting algorithm to complex portions of an object selection.

Abstract: In various implementations, methods and systems are disclosed for accurately selecting a targeted portion of a digital image. In one embodiment, a selection cursor having a central and a peripheral region is provided. The central region is used to force a selection or a deselection and therefore moving the central region over a portion of the image causes that portion of the image to be selected or deselected, respectively. The peripheral region of the cursor surrounds the central region and defines an area where a hybrid level set algorithm for both boundary detection and region definition, particularly a matting region, is performed. This provides highly accurate boundary detection and matting region selection within a narrowly-focused peripheral region and eliminates the need to subsequently designate a matting region and apply a matting algorithm to complex portions of an object selection.

Abstract: In various implementations, methods and systems are disclosed for accurately selecting a targeted portion of a digital image. In one embodiment, a selection cursor having a central and a peripheral region is provided. The central region is used to force a selection or a deselection and therefore moving the central region over a portion of the image causes that portion of the image to be selected or deselected, respectively. The peripheral region of the cursor surrounds the central region and defines an area where a hybrid level set algorithm for both boundary detection and region definition, particularly a matting region, is performed. This provides highly accurate boundary detection and matting region selection within a narrowly-focused peripheral region and eliminates the need to subsequently designate a matting region and apply a matting algorithm to complex portions of an object selection.

Abstract: Selection refinement tool techniques are described in which a selection within an image is modified using a tool that automatically resizes according to the shape of selection boundaries. In one or more implementations, selection boundaries are displayed in an image that is exposed in a user interface. The selection boundaries may represent an automatic selection via an editing tool, or a selection that is based on manual manipulation of an editing tool by a user. The selection refinement tool is operable to modify the selection boundaries by positioning of a cursor of the refinement tool proximate to the selection boundaries. In response to positioning of the cursor relative to the selection boundaries, the cursor is dynamically resized in dependence upon a positional relationship between the cursor and the selection boundaries, such that boundaries of the cursor are kept from intersecting with the selection boundaries.