Adobe, Inc.

Rishu Aggarwal

Ajay Bedi

Sunil Rawat

Rekha Agarwal

Methods, systems, and non-transitory computer readable media are disclosed for accurately and efficiently identifying digital images that portray counterfeit products. The disclosed system can store key values and properties of authentic graphic elements (logos, trademarks, designs, etc.,). For example, the disclosed system can determine authentic graphic features comprising authentic shape features, authentic color features, and authentic text features corresponding with a graphic element. The disclosed system can further extract a graphic element from a digital image for comparison with the authentic graphic element. For instance, the disclosed system can extract graphic features of the graphic element, the graphic features comprising shape features, color features, and text features from the graphic element. The disclosed system can further determine whether the digital image portrays a counterfeit or an authentic product based on comparing the authentic graphic features with the extracted graphic features.

Autonomous aesthetics-aware detection of counterfeit products

Systems and techniques for a design-aware image search are described. The design-aware image search techniques described herein capture a design on an item to determine additional items with similar or matching designs. An image of the item is used to create an edge image of a design, and shape descriptors are generated describing features of the edge image. These shape descriptors are compared to shape descriptors associated with other images to locate images that have similar or matching designs as compared with the input image. The design-aware image search system may use these relationships to generate a search result with images or products having a design similar to the design on the input image.

Systems and methods for product search using generated shape descriptors and edge image of source product images

Techniques are provided herein for more efficiently storing images that have a common subject, such as product images that share the same product in the image. Each image undergoes an adaptive tiling procedure to split the image into a plurality of tiles, with each tile identifying a region of the image having pixels with the same content. The tiles across multiple images can then be clustered together and those tiles having identical content are removed. Once all duplicate tiles have been removed from the set of all tiles across the images, the tiles are once again clustered based on their encoding scheme and certain encoding parameters. Tiles within each cluster are compressed using the best compression technique for the tiles in each corresponding cluster. By removing duplicative tile content between numerous images of the same subject, the total amount of data that needs to be stored is reduced.

Content-adaptive tiling solution via image similarity for efficient image compression

In implementations of search input generation for an image search, a computing device can capture image data of an environment scene that includes multiple objects. The computing device implements a search input module that can detect the multiple objects in the image data, and initiate a display of a selectable indication for each of the multiple objects. The search input module can then determine a subject object from the detected multiple objects, and generate the subject object as the search input for the image search.

Search input generation for image search

Systems and techniques for a design-aware image search are described. The design-aware image search techniques described herein capture a design on an item to determine additional items with similar or matching designs. An image of the item is used to create an edge image of a design, and shape descriptors are generated describing features of the edge image. These shape descriptors are compared to shape descriptors associated with other images to locate images that have similar or matching designs as compared with the input image. The design-aware image search system may uses these relationships to generate a search result with images or products having a design similar to the design on the input image.

Design-aware image search

Structural modifications to a person's face in a reference image are captured and automatically applied to the person's face in another image. The reference image is processed to compute landmark information on the person's face and apply a mesh to the reference image. When structural modifications are made to the person's face in the reference image, the mesh is modified, and the modified mesh is stored in association with the landmark information. Another image is analyzed to compute landmark information on the person's face in that image and apply a mesh to the image. A transformation matrix is computed using the landmark information from the reference image and current image, and the modified mesh from the reference image is transformed using the transformation matrix. The mesh in the current image is modified using the transformed mesh, thereby applying the structural modification to the person's face in the current image.

Structural facial modifications in images

A method of automatically computing tool properties of a virtual paint brush includes obtaining an edge map from a digital image, wherein boundaries of objects in the image are detected, computing contours for the edge map, receiving a brush stroke of the virtual paint brush from a user at a point on a contour, and finding an object boundary that corresponds to the contour that received the brush stroke, computing a tangential slope of the object boundary at the point of the brush stroke, adjusting tool properties of the virtual paint brush based on a change in the tangential slope of the object boundary, and visually displaying the adjusted tool properties dynamically while the user is moving brush strokes around the object boundaries.

Smart painting tools

An eye correction system and related techniques are described herein. The eye correction system can automatically detect and correct one or more misaligned eyes in an image. For example, an image of a person can be analyzed to determine whether one or both eyes of the person are misaligned with respect to each other and/or with respect to the entire face of the person. If an eye is determined to be misaligned, the image can be modified so that the eye is adjusted accordingly. For example, an iris of a misaligned eye can be adjusted to align with the face and/or the other eye.

Correction of misaligned eyes in images

The present disclosure relates to methods, systems, and non-transitory computer-readable media for generating a topic visual element for a portion of a digital video based on audio content and visual content of the digital video. For example, the disclosed systems can generate a map between words of the audio content and their corresponding timestamps from the digital video and then modify the map by associating importance weights with one or more of the words. Further, the disclosed systems can generate an additional map by associating words embedded in one or more video frames of the visual content with their corresponding timestamps. Based on these maps, the disclosed systems can identify a topic for a portion of the digital video (e.g., a portion currently previewed on a computing device), generate a topic visual element that includes the topic, and provide the topic visual element for display on a computing device.

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Delhi, India

Rishu Aggarwal