Abstract: An object may be identified, if each measured value of a characteristic of the object is within a corresponding range of a set of characteristics. The object may then be classified as a true alarm or false alarm by a user. Next, the measured values of the object may be added as a data point to a set of data points. Each of data points is along a plurality of dimensions and each of the dimensions corresponds to one of the set of characteristics. Further, each of the data points has been classified as a true alarm or false alarm. The range of the set of characteristics may be updated to reduce a weighted score based on a number of the true alarms that are outside a region along the plurality of dimensions and a number of the false alarms inside the region for the set of data points. The region is defined based on numerical analysis of the set of data points. The weighted score may provide separate weights to the true alarms outside the region and the missed alarms inside the region.

Abstract: A homography is determined between a test image and a plurality of database images based on a location of a character along a main line of text of the test image. A similarity score is determined for each of the plurality of database images based on the determined homography. The similarity score may measure a similarity of a characteristic between the test image and the corresponding database image. The characteristic may be at a location separate from the main line of text of the test image. A type of the test image may be selected based on the similarity scores.

Abstract: An object may be identified, if each measured value of a characteristic of the object is within a corresponding range of a set of characteristics. The object may then be classified as a true alarm or false alarm by a user. Next, the measured values of the object may be added as a data point to a set of data points. Each of data points is along a plurality of dimensions and each of the dimensions corresponds to one of the set of characteristics. Further, each of the data points has been classified as a true alarm or false alarm. The range of the set of characteristics may be updated to reduce a weighted score based on a number of the true alarms that are outside a region along the plurality of dimensions and a number of the false alarms inside the region for the set of data points. The region is defined based on numerical analysis of the set of data points. The weighted score may provide separate weights to the true alarms outside the region and the missed alarms inside the region.

Abstract: Techniques associated with identifying stories in media content are described in various implementations. In one example implementation, a method may include receiving media content, and segmenting the media content into a plurality of media segments based on auditory indicators included in the media content. The method may also include analyzing a first media segment to determine a first set of concepts associated with the first media segment, and analyzing a second media segment to determine a second set of concepts associated with the second media segment. The method may further include comparing the first set of concepts to the second set of concepts to determine a conceptual similarity between the first set of concepts and the second set of concepts, and in response to the conceptual similarity exceeding a similarity threshold, identifying a story that includes the first media segment and the second media segment.

Abstract: A homography is determined between a test image and a plurality of database images based on a location of a character along a main line of text of the test image. A similarity score is determined for each of the plurality of database images based on the determined homography. The similarity score may measure a similarity of a characteristic between the test image and the corresponding database image. The characteristic may be at a location separate from the main line of text of the test image. A type of the test image may be selected based on the similarity scores.

Abstract: Techniques associated with identifying stories in media content are described in various implementations. In one example implementation, a method may include receiving media content, and segmenting the media content into a plurality of media segments based on auditory indicators included in the media content. The method may also include analyzing a first media segment to determine a first set of concepts associated with the first media segment, and analyzing a second media segment to determine a second set of concepts associated with the second media segment. The method may further include comparing the first set of concepts to the second set of concepts to determine a conceptual similarity between the first set of concepts and the second set of concepts, and in response to the conceptual similarity exceeding a similarity threshold, identifying a story that includes the first media segment and the second media segment.