Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: A rotor assembly for a DC motor is provided which includes a rotor having a rotor shaft, a commutator mounted to the rotor shaft, and a varistor which is connected to the commutator via an electrically-conductive solderless fixing means, such as an electrically-conductive adhesive.

Abstract: Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Abstract: A rotor assembly for a DC motor is provided which includes a rotor having a rotor shaft, a commutator mounted to the rotor shaft, and a varistor which is connected to the commutator via an electrically-conductive solderless fixing means, such as an electrically-conductive adhesive.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Embodiments relate to leveraging automated image understanding to foster user engagement with an image. An image viewer application is provided with image analysis algorithms that determine features of an image by identifying objects in the image, recognizing faces in the image, etc. Hotspots may be added to the display of the image at locations of the features. Contextual metadata such as data about a user, user activity, other images, etc. can be used to determine which features will be provided with hotspots and what information will be associated with the hotspots. In one embodiment, the information associated with a hotspot is another image, and interactive selection of the hotspot causes the image to be both displayed and processed for hotspot augmentation. Tools may be provided to modify or edit the image based on the features found via the image analysis.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Embodiments relate to leveraging automated image understanding to foster user engagement with an image. An image viewer application is provided with image analysis algorithms that determine features of an image by identifying objects in the image, recognizing faces in the image, etc. Hotspots may be added to the display of the image at locations of the features. Contextual metadata such as data about a user, user activity, other images, etc. can be used to determine which features will be provided with hotspots and what information will be associated with the hotspots. In one embodiment, the information associated with a hotspot is another image, and interactive selection of the hotspot causes the image to be both displayed and processed for hotspot augmentation. Tools may be provided to modify or edit the image based on the features found via the image analysis.

Abstract: Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Abstract: Media files may be prioritized based on user behavior data and social data associated with individual media files. A process of prioritizing media files may include accessing user behavior data associated with individual media files in a set of media files that are stored on a client device of a user or in a cloud-based storage location associated with the user, accessing social data associated with one or more media files of the set of media files that are determined to have been shared on a media file sharing service, and ranking the set of media files based at least in part on the user behavior data and the social data.

Abstract: Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Abstract: Media files may be prioritized based on user behavior data and social data associated with individual media files. A process of prioritizing media files may include accessing user behavior data associated with individual media files in a set of media files that are stored on a client device of a user or in a cloud-based storage location associated with the user, accessing social data associated with one or more media files of the set of media files that are determined to have been shared on a media file sharing service, and ranking the set of media files based at least in part on the user behavior data and the social data.

Abstract: Media files may be prioritized based on user behavior data and social data associated with individual media files. A process of prioritizing media files may include accessing user behavior data associated with individual media files in a set of media files that are stored on a client device of a user or in a cloud-based storage location associated with the user, accessing social data associated with one or more media files of the set of media files that are determined to have been shared on a media file sharing service, and ranking the set of media files based at least in part on the user behavior data and the social data.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Technologies for grouping images, and ranking the images and the groupings, based on entities shown in the images. Images may be grouped based on faces shown in the images. Different images with faces that indicate the same entity (e.g., Adam) may be automatically grouped together. Different images with faces that indicate the same multiple entities (e.g., the people in my family) may also be automatically grouped together. Such automatic grouping may be based on facial recognition technologies. Further, images and groups of images may be automatically ranked based on the faces shown and entities represented. Such rankings may also be influenced by adjacent data that indicates family and friends and the like, and that can be used to identify such entities in the images.

Abstract: Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Abstract: Technologies for analyzing various features detected in a face detected in an image. Such features may include at least the eyes, eyebrows, node, and mouth of a face. Such analyzing may include scoring aspects of these features. Such scores may be weighted. The analysis may be used to determine expressions and/or poses of the face, as well as indicate if the face is smiling, frowning, or neutral. An overall quality score for the face may also be determined and provided based on the aforementioned analysis as well as whether or not the face is near an edge of the image or cut-off in the image. Finally, a face signature may be determined that uniquely identifies the face, at least within the scope of the analyzed features. Such face signatures may be used to detect a similar face in other images.

Abstract: Media files may be prioritized based on user behavior data and social data associated with individual media files. A process of prioritizing media files may include accessing user behavior data associated with individual media files in a set of media files that are stored on a client device of a user or in a cloud-based storage location associated with the user, accessing social data associated with one or more media files of the set of media files that are determined to have been shared on a media file sharing service, and ranking the set of media files based at least in part on the user behavior data and the social data.