Abstract: Technologies for modifying a digital image to take on the appearance of an antique image. Such modifying is typically based on generating and rendering various effects that are blended with the input image, such as color transformation, simulating film grain, dust, fibers, tears, and vintage borders. Such effects may be rendered to various layers that are overlaid on a color transformed image resulting in what appears to be an antique image.

Abstract: Technologies for modifying a digital image to take on the appearance of an antique image. Such modifying is typically based on generating and rendering various effects that are blended with the input image, such as color transformation, simulating film grain, dust, fibers, tears, and vintage borders. Such effects may be rendered to various layers that are overlaid on a color transformed image resulting in what appears to be an antique image.

Abstract: Technologies for modifying a digital image to take on the appearance of an antique image. Such modifying is typically based on generating and rendering various effects that are blended with the input image, such as color transformation, simulating film grain, dust, fibers, tears, and vintage borders. Such effects may be rendered to various layers that are overlaid on a color transformed image resulting in what appears to be an antique image.

Abstract: Technologies for selecting a representative subset of images from a set of images, the selecting based at least in part on rating the images in the set based on task, Image, and/or adjacent information. An Indication of the task may be embodied in a query provided by a user. The task may indicate the user's intended use of the subset of images. The set of images may be grouped into one or more clusters that are based on technical attributes of the images in the set, and/or technical attributes indicated by the task. Adjacent information may be obtained from sources that are generally unrelated or indirectly related to the images in the set.

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.