Abstract: Techniques are described for providing functionality to allow a viewer of a television show to watch a “previously on” segment of an episode of the television show and be able to watch the scenes from prior episodes referenced in the “previously on” segment.

Abstract: Object identification through image matching can utilize ratio and other data to accurately identify objects having relatively few feature points otherwise useful for identifying objects. An initial image analysis attempts to locate a “scalar” in the image, such as may include a label, text, icon, or other identifier that can help to narrow a classification of the search, as well as to provide a frame of reference for relative measurements obtained from the image. By comparing the ratios of dimensions of the scalar with other dimensions of the object, it is possible to discriminate between objects containing that scalar in a way that is relatively robust to changes in viewpoint. A ratio signature can be generated for an object for use in matching, while in other embodiments a classification can identify priority ratios that can be used to more accurately identify objects in that classification.

Abstract: An auxiliary device can be used to display a fiducial that contains information useful in determining the physical size of the fiducial as displayed on the auxiliary device. A primary device can capture image data including a representation of the fiducial. The scale and orientation of the fiducial can be determined, such that a graphical overlay can be generated of an item of interest that corresponds to that scale and orientation. The overlay can then be displayed along with the captured image data, in order to provide an augmented reality experience wherein the image displayed on the primary device represents a scale-appropriate view of the item in a location of interest corresponding to the location of the auxiliary device. As the primary device is moved and the viewpoint of the camera changes, changes in relative scale and orientation to the fiducial are determined and the overlay is updated accordingly.

Abstract: Various embodiments identify differences between frame sequences of a video. For example, to determine a difference between two versions of a video, a fingerprint of each frame of the two versions is generated. From the fingerprints, a run-length encoded representation of each version is generated. The fingerprints which appear only once (i.e., unique fingerprints) in the entire video are identified from each version and compared to identify matching unique fingerprints across versions. The matching unique fingerprints are sorted and filtered to determine split points, which are used to align the two versions of the video. Accordingly, each version is segmented into smaller frame sequences using the split points. Once segmented, the individual frames of each segment are aligned across versions using a dynamic programming algorithm. After aligning the segments at a frame level, the segments are reassembled to generate a global alignment output.

Abstract: Techniques are described for providing functionality to allow a viewer of a television show to watch a “previously on” segment of an episode of the television show and be able to watch the scenes from prior episodes referenced in the “previously on” segment.

Abstract: Disclosed are various embodiments for generating recommendations utilizing visual image analysis, for example, by inserting a representative image of an item in an empty region of a digital image. Representative images for an item may be determined by generating a score for each potential image for insertion into the digital image, where a representative image is selected having a highest score from the ranking. The representative image selected may be pre-processed such that the image appears naturally in the setting.

Abstract: The recognition of objects such as clothing items can be improved by capturing image data that represents the material characteristics and true color of the object. A high resolution image can be captured that shows the individual threads and/or fibers, among other possible pattern or texture characteristics. Various approaches are discussed for determining the scale of these features to more accurately determine the way the material is made. Further, approaches are discussed that enable the true color of the material to be determined, through hardware and/or software, to further improve the accuracy of such a determination. The material and color properties can be combined with a conventional object recognition process to provide accurate object identification and/or authentication results.

Abstract: Disclosed are various embodiments for generating recommendations utilizing visual image analysis, for example, by inserting a representative image of an item in an empty region of a digital image. Representative images for an item may be determined by generating a score for each potential image for insertion into the digital image, where a representative image is selected having a highest score from the ranking. The representative image selected may be pre-processed such that the image appears naturally in the setting.

Abstract: The recognition of objects such as clothing items can be improved by capturing image data that represents the material characteristics and true color of the object. A high resolution image can be captured that shows the individual threads and/or fibers, among other possible pattern or texture characteristics. Various approaches are discussed for determining the scale of these features to more accurately determine the way the material is made. Further, approaches are discussed that enable the true color of the material to be determined, through hardware and/or software, to further improve the accuracy of such a determination. The material and color properties can be combined with a conventional object recognition process to provide accurate object identification and/or authentication results.

Abstract: Disclosed are various embodiments for generating recommendations utilizing visual image analysis. A digital image provided by a client device is analyzed to identify an empty region in a setting embodied in the digital image. A recommended item, available for consumption via an electronic commerce system, may be identified based on characteristics of the setting embodied in the digital image and historical data associated with a user. A modified form of the digital image is generated comprising the recommended item in the empty region.

Abstract: Various embodiments enable a device to perform an adaptive sampling method for locating text regions in images of natural scenes. Since only a fraction of pixels in an input image correspond to text regions, it is desirable to eliminate non-text regions early in the text detection process. Therefore, in at least one embodiment, an image is sampled horizontally to identify connected pixel regions indicative of text. In this example, each sampled row of the image is tested for containing Maximally Stable Extremal Regions (MSERs) in order to identify regions containing text. If a respective row contains a connected pixel region indicative of text, the neighboring rows are recursively sampled at finer levels in order to fully contain the text and sampling is terminated for regions which are unlikely to contain text. This sampling process can also be performed for the vertical dimension for regions which are determined to contain text.

Abstract: Object identification through image matching can utilize ratio and other data to accurately identify objects having relatively few feature points otherwise useful for identifying objects. An initial image analysis attempts to locate a “scalar” in the image, such as may include a label, text, icon, or other identifier that can help to narrow a classification of the search, as well as to provide a frame of reference for relative measurements obtained from the image. By comparing the ratios of dimensions of the scalar with other dimensions of the object, it is possible to discriminate between objects containing that scalar in a way that is relatively robust to changes in viewpoint. A ratio signature can be generated for an object for use in matching, while in other embodiments a classification can identify priority ratios that can be used to more accurately identify objects in that classification.

Abstract: Object identification through image matching can utilize ratio and other data to accurately identify objects having relatively few feature points otherwise useful for identifying objects. An initial image analysis attempts to locate a “scalar” in the image, such as may include a label, text, icon, or other identifier that can help to narrow a classification of the search, as well as to provide a frame of reference for relative measurements obtained from the image. By comparing the ratios of dimensions of the scalar with other dimensions of the object, it is possible to discriminate between objects containing that scalar in a way that is relatively robust to changes in viewpoint. A ratio signature can be generated for an object for use in matching, while in other embodiments a classification can identify priority ratios that can be used to more accurately identify objects in that classification.

Abstract: Various embodiments identify differences between frame sequences of a video. For example, to determine a difference between two versions of a video, a fingerprint of each frame of the two versions is generated. From the fingerprints, a run-length encoded representation of each version is generated. The fingerprints which appear only once (i.e., unique fingerprints) in the entire video are identified from each version and compared to identify matching unique fingerprints across versions. The matching unique fingerprints are sorted and filtered to determine split points, which are used to align the two versions of the video. Accordingly, each version is segmented into smaller frame sequences using the split points. Once segmented, the individual frames of each segment are aligned across versions using a dynamic programming algorithm. After aligning the segments at a frame level, the segments are reassembled to generate a global alignment output.

Abstract: Various embodiments identify differences between frame sequences of a video. For example, to determine a difference between two versions of a video, a fingerprint of each frame of the two versions is generated. From the fingerprints, a run-length encoded representation of each version is generated. The fingerprints which appear only once (i.e., unique fingerprints) in the entire video are identified from each version and compared to identify matching unique fingerprints across versions. The matching unique fingerprints are sorted and filtered to determine split points, which are used to align the two versions of the video. Accordingly, each version is segmented into smaller frame sequences using the split points. Once segmented, the individual frames of each segment are aligned across versions using a dynamic programming algorithm. After aligning the segments at a frame level, the segments are reassembled to generate a global alignment output.

Abstract: Disclosed are various embodiments for generating recommendations utilizing visual image analysis. A digital image provided by a client device is analyzed to identify an empty region in a setting embodied in the digital image. A recommended item, available for consumption via an electronic commerce system, may be identified based on characteristics of the setting embodied in the digital image and historical data associated with a user. A modified form of the digital image is generated comprising the recommended item in the empty region.