Adobe, Inc.

Prasenjit Mondal

Sachin Soni

Ajay Jain

Sanjeev Tagra

Ryan Rozich

Jonathan Roeder

Anuj Shara

Ankit Bal

Arshiya Aggarwal

Jingwan Lu

Deepanshu Arora

Ajay Bedi

Ram Bhushan Agrawal

Ruppesh Nalwaya

Siddharth Kumar

Anshul Malik

Anugrah Prakash

Embodiments are disclosed for providing customizable, visually aesthetic color diverse template recommendations derived from a source image. A method may include receiving a source image and determining a source image background by separating a foreground of the source image from a background of the source image. The method separates a foreground from the background by identifying portions of the image that belong to the background and stripping out the rest of the image. The method includes identifying a text region of the source image using a machine learning model and identifying font type using the identified text region. The method includes generating an editable template image using the source image background, the text region, and the font type.

Automatic template recommendation

The present disclosure relates to systems, non-transitory computer-readable media, and methods that implement an image filter for enhancing light text and removing document shadows. In particular embodiments, the disclosed systems use a modified adaptive thresholding approach the relies on image gradients to efficiently guide the thresholding process. In addition, the disclosed systems use a machine-learning model to generate a document shadow map. The document shadow map can include text reflections. Accordingly, the disclosed systems remove text reflections from the document shadow map (e.g., by using an interpolated shadow intensity value of neighboring shadow map pixels). In turn, the disclosed systems use the document text mask and the document shadow map cleaned of text reflections to remove shadows from the digital image. Further, the disclosed systems enhance text in the shadow-removed digital image based on contrast stretching.

Enhancing light text in scanned documents while preserving document fidelity

An image differentiation system receives input feature vectors for multiple input images and reference feature vectors for multiple reference images. In some cases, the feature vectors are extracted by an image feature extraction module trained based on training image triplets. A differentiability scoring module determines a differentiability score for each input image based on a distance between the input feature vectors and the reference feature vectors. The distance for each reference feature vector is modified by a weighting factor based on interaction metrics associated with the corresponding reference image. In some cases, an input image is identified as a differentiated image based on the corresponding differentiability score. Additionally or alternatively, an image modification module determines an image modification that increases the differentiability score of the input image. The image modification module generates a recommended image by applying the image modification to the input image.

Machine learning techniques for differentiability scoring of digital images

Methods, systems, and non-transitory computer readable media are disclosed for generating a composite image comprising objects in positions from two or more different digital images. In one or more embodiments, the disclosed system receives a sequence of images and identifies objects within the sequence of images. In one example, the disclosed system determines a target position for a first object based on detecting user selection of the first object in the target position from a first image. The disclosed system can generate a fixed object image comprising the first object in the target position. The disclosed system can generate preview images comprising the fixed object image with the second object sequencing through a plurality of positions as seen in the sequence of images. Based on a second user selection of a desired preview image, the disclosed system can generate the composite image.

Generating composite images with objects from different times

Certain embodiments involve visually augmenting images of three-dimensional containers with virtual elements that fill one or more empty regions of the three-dimensional containers. For instance, a computing system receives a first image that depicts a storage container and identify sub-containers within the storage container. The computing system selects, from a virtual object library, a plurality of virtual objects that are semantically related to the sub-container. The computing system determines an arrangement of the virtual objects within the sub-container based on semantics associated with the sub-container and the plurality of virtual objects. The computing system generates a second image that depicts the arrangement of the plurality of virtual objects within the storage container and sub-containers. The computing system generates, for display, the second image depicting the storage container and the arrangement of the virtual objects.

Visually augmenting images of three-dimensional containers with virtual elements

Methods, apparatus, and systems are provided for obfuscating facial identity in images by synthesizing a new facial image for an input image. A base face is detected from or selected for an input image. Facial images that are similar to the base face are selected and combined to create a new facial image. The new facial image is added to the input image such that the input image includes a combination of the base face and the new facial image. Where no base face is detected in the input image, a base face is selected from reference facial images based at least on pose keypoints identified in the input image. After a new facial image is generated based on the selected base face, a combination of the new facial image and the base facial image are added to the input image by aligning one or more pose keypoints.

Identity obfuscation in images utilizing synthesized faces

In implementations of jointly editing related objects in a digital image, an image editing application segments a first object in a digital image, and an additional object corresponding to the first object, such a shadow cast by the first object, a reflection of the first object, or an object of a same object class as the first object. Respective stick diagrams for the first object and the additional object are generated, and a mapping of the first object to the additional object is generated based on the stick diagrams. Based on a user request to edit the first object, such as to warp the first object, the first object and the additional object are jointly edited based on the mapping. Accordingly, realistic digital images are efficiently generated that maintain correspondence between related objects, without requiring a user to edit each object individually, thus saving time and resources.

Jointly editing related objects in a digital image

The present disclosure relates to a computer-implemented method for generating an enhanced image from an original image, the method including segmenting the original image into a segmented image using an artificial neural network; curve fitting the segmented image to determine boundary artifacts; removing the determined boundary artifacts to generate a smoothed boundary image; and generating the enhanced image from the original image and the smoothed boundary image. The image maybe enhanced further by correcting for glare and adding artificial light.

Method and system to enhance quality of digital images

Techniques for propagating a reflection of an object. In an example, a method includes receiving an input image comprising a first reflection of a first object on a reflective surface. The method further includes generating a second reflection for a second object in the input image. The second reflection is a reflection of the second object on the reflective surface. The method includes adding the second reflection to the input image. The method includes outputting a modified image comprising the first object, first reflection, the second object, and the second reflection.

Generating reflections within images

While a user holds a camera positioned relative to an object, a first image of the object and a second image of the object, as captured by the camera, may be obtained. Intensity variations between a first intensity map of the first image and a combination intensity map obtained from the first intensity map and a second intensity map of the second image may be compared. Then, a shadow may be identified within the first image, based on the intensity variations.

Image shadow detection using multiple images

Techniques are disclosed for document boundary detection (BD) from an input image using a combination of deep learning model and image processing algorithms. Quadrilaterals approximating the document boundaries in the input image are determined and rated separately using both these approaches: deep leaning using convolutional neural network (CNN) and heuristics using image processing algorithms. Thereafter, the best rated quadrilateral is selected from the quadrilaterals obtained from both the approaches.

Document boundary detection using deep learning model and image processing algorithms

Methods, systems, and software are disclosed herein for protecting the privacy of users in images and to prevent or mitigate the occurrence of privacy breaches. In an implementation, a biometric privacy service identifies a biometric marker in an image of a scene, such as a fingerprint, a retina, or an iris. The service then identifies one or more features of the biometric marker to be modified. The biometric service also identifies one or more modifications to make to the one or more features of the biometric marker and generates a modified version of the biometric marker that reflects the modification(s). The image is updated by the service with the modified version of the biometric marker, so that the biometric marker can no longer be used for unwanted purposes, whether they be malicious in intent or benign.

Growing community of inventors

West Bengal, India

Prasenjit Mondal