Abstract: A video visual and audio query system for quickly identifying video within a large known corpus of videos being played on any screen or display. In one embodiment, the system can record via a mobile phone camera and microphone a live video clip from the TV and transcode it into a sequence of frame-signatures. The signatures representative of the clips can then be matched against the signatures of the TV content in a corpus across a network to identify the correct TV show or movie.

Abstract: An automated content recognition system accurately and reliably generates content identification information for multimedia content without accessing the multimedia content or a reliable source of the multimedia content. The system receives content-based queries having fingerprints of multimedia content. The system compares the individual queries to one another to match queries and thereby form query clusters that correspond to the same multimedia content. The system aggregates identification information from the queries in a cluster to generate reliable content identification information from otherwise unreliable identification information.

Abstract: Technology described herein provides techniques for screen detection. This could be a dynamic screen, such as a display screen of an electronic device, or a static screen such as a street sign. In one aspect, a screen is detected in frames of image data. An active image and/or an average color image may be formed based on frames of image data. A set of lines that are candidate for boundaries of a screen are identified in the frames. The active image and/or an average color is integrated on both sides of each line in the set of lines to form integration results. A set of screen candidates are formed based on the integration results. Screens in the screen candidates are scored according to a criterion. One or top few of the screens in the set of screen candidates is selected based on the scoring.

Abstract: Techniques for aligning images are disclosed. The frames might have been captured by a video camera on a hand held device, as one example. Collaboratively aligning related frames of image data is taught. Collaborative alignment determines a correspondence between pixels in pairs of the frames of image data, as well as a confidence in that correspondence. A coordinate system (or transformation) is assigned to each of the frames that is consistent with the correspondences between each of the frame pairs. The confidence in the respective correspondences may be used to provide a weighting to a correspondence when assigning the coordinate systems. Outlying frames may be discarded, and the process repeated for a robust solution.

Abstract: Techniques for aligning images are disclosed. The frames might have been captured by a video camera on a hand held device, as one example. Collaboratively aligning related frames of image data is taught. Collaborative alignment determines a correspondence between pixels in pairs of the frames of image data, as well as a confidence in that correspondence. A coordinate system (or transformation) is assigned to each of the frames that is consistent with the correspondences between each of the frame pairs. The confidence in the respective correspondences may be used to provide a weighting to a correspondence when assigning the coordinate systems. Outlying frames may be discarded, and the process repeated for a robust solution.

Abstract: An automated content recognition system accurately and reliably generates content identification information for multimedia content without accessing the multimedia content or a reliable source of the multimedia content. The system receives content-based queries having fingerprints of multimedia content. The system compares the individual queries to one another to match queries and thereby form query clusters that correspond to the same multimedia content. The system aggregates identification information from the queries in a cluster to generate reliable content identification information from otherwise unreliable identification information.

Abstract: Technology described herein provides techniques for screen detection. This could be a dynamic screen, such as a display screen of an electronic device, or a static screen such as a street sign. In one aspect, a screen is detected in frames of image data. An active image and/or an average color image may be formed based on frames of image data. A set of lines that are candidate for boundaries of a screen are identified in the frames. The active image and/or an average color is integrated on both sides of each line in the set of lines to form integration results. A set of screen candidates are formed based on the integration results. Screens in the screen candidates are scored according to a criterion. One or top few of the screens in the set of screen candidates is selected based on the scoring.

Abstract: Systems and methods for identifying and searching video are disclosed. A video search and identification system includes a catalog representing relationships between video, data and/or objects to enable querying and search based on visual representations of video as well as data or other information associated with the video. In one example, the catalog includes video nodes, metadata nodes and card nodes, although additional or fewer node types may be used. A visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. Confidence measures are generated at the bit level to assist in query signature processing.

Abstract: Systems and methods for identifying and searching video are disclosed. A video search and identification system includes a catalog representing relationships between video, data and/or objects to enable querying and search based on visual representations of video as well as data or other information associated with the video. In one example, the catalog includes video nodes, metadata nodes and card nodes, although additional or fewer node types may be used. A visual-based video identification system is provided to identify content in video sources. An unidentified video source is accessed and visual fingerprints of one or more frames are generated as query signatures for matching against a base set of known signatures. Confidence measures are generated at the bit level to assist in query signature processing.

Abstract: A method and system for inspecting a surface of a semiconductor workpiece comprises providing a surface inspection system and using the surface inspection apparatus to cause laser light to impinge upon a test location on the workpiece surface and thereby cause the laser light to emerge from the surface as returned light comprising at least one of reflected light and scatter light; collecting the returned light and generating a signal from the returned and collected light, the signal comprising a signal value representative of a characteristic of the workpiece surface at the test location; providing a plurality of threshold candidates and causing the surface inspection system to select a threshold from among the plurality of threshold candidates; comparing the threshold to the signal value to obtain a difference value; using the difference value to assess the characteristic of the workpiece surface at the test location; and using the surface inspection system to automatically cause the method to be repeated fo

Abstract: A video visual and audio query system for quickly identifying video within a large known corpus of videos being played on any screen or display. In one embodiment, the system can record via a mobile phone camera and microphone a live video clip from the TV and transcode it into a sequence of frame-signatures. The signatures representative of the clips can then be matched against the signatures of the TV content in a corpus across a network to identify the correct TV show or movie.

Abstract: A method and system for inspecting a surface of a semiconductor workpiece comprises providing a surface inspection system and using the surface inspection apparatus to cause laser light to impinge upon a test location on the workpiece surface and thereby cause the laser light to emerge from the surface as returned light comprising at least one of reflected light and scatter light; collecting the returned light and generating a signal from the returned and collected light, the signal comprising a signal value representative of a characteristic of the workpiece surface at the test location; providing a plurality of threshold candidates and causing the surface inspection system to select a threshold from among the plurality of threshold candidates; comparing the threshold to the signal value to obtain a difference value; using the difference value to assess the characteristic of the workpiece surface at the test location; and using the surface inspection system to automatically cause the method to be repeated fo

Abstract: A system and method for characterizing the jitter of a periodic signal. Samples of the signal are taken with a sampling device. A set of samples representing a particular value of the signal in multiple cycles of the periodic signal is collected. Those values are formed into a histogram. The histogram is matched to a probability distribution function. By identifying parameters that shape the probability distribution function to match the histogram of actual samples, characteristics of the jitter are determined. This technique may be employed as part of the calibration or verification of the jitter injection instrument such as might be used for testing semiconductor devices. Measurements may be made with a sampling device that is calibrated to NIST standards. In this way, the jitter measurements become NIST traceable.