Abstract: Multiple candidate feature components of media content or projection matrices (or other hash functions, e.g., non-linear projections) are identified. Each of the candidate projection matrices (or other hash functions) includes an array of coefficients that relate to the candidate features. A subgroup of the candidate features or the projection matrices (or other hash functions) are selected based at least partially on an optimized combination of at least two characteristics of the candidate features or projection matrices (or other hash functions). Media fingerprints that uniquely identify the media content are derived from the selected optimized subgroup. Optimal projection matrices (or other hash functions) may be designed. Performance or sensitivity (e.g., search time) characteristics of the fingerprints are thus balanced with robustness characteristics thereof.

Abstract: A value is computed for a feature in an instance of query content and compared to a threshold value. Based on the comparison, first and second bits in a hash value, which is derived from the query content feature, are determined. Conditional probability values are computed for the likelihood that quantized values of the first and the second bits equal corresponding quantized bit values of a target or reference feature value. The conditional probabilities are compared and a relative strength determined for the first and second bits, which directly corresponds to the conditional probability. The bit with the lowest bit strength is selected as the weakbit. The value of the weakbit is toggled to generate a variation of the query hash value. The query may be extended using the query hash value variation.

Abstract: A value is computed for a feature in an instance of query content and compared to a threshold value. Based on the comparison, first and second bits in a hash value, which is derived from the query content feature, are determined. Conditional probability values are computed for the likelihood that quantized values of the first and the second bits equal corresponding quantized bit values of a target or reference feature value. The conditional probabilities are compared and a relative strength determined for the first and second bits, which directly corresponds to the conditional probability. The bit with the lowest bit strength is selected as the weakbit. The value of the weakbit is toggled to generate a variation of the query hash value. The query may be extended using the query hash value variation.

Abstract: Multiple candidate feature components of media content or projection matrices (or other hash functions, e.g., non-linear projections) are identified. Each of the candidate projection matrices (or other hash functions) includes an array of coefficients that relate to the candidate features. A subgroup of the candidate features or the projection matrices (or other hash functions) are selected based at least partially on an optimized combination of at least two characteristics of the candidate features or projection matrices (or other hash functions). Media fingerprints that uniquely identify the media content are derived from the selected optimized subgroup. Optimal projection matrices (or other hash functions) may be designed. Performance or sensitivity (e.g., search time) characteristics of the fingerprints are thus balanced with robustness characteristics thereof.

Abstract: A wireless ultrasonic scanning system comprises an ultrasonic sensor, a motor, an ultrasonic transceiver, a high-speed data sampling module, a motor controller and a master control module. The ultrasonic sensor, which moves in accordance with control of the motor controller, is mounted on the motor. The ultrasonic transceiver activates the ultrasonic sensor and amplifies a received ultrasonic signal. The high-speed data sampling module wirelessly transmits radio-frequency ultrasonic data to the master control module. The master control module sets a scanning mode, initiates a scanning process, and wirelessly transmits control signals and control parameters to the high-speed data sampling module.

Abstract: Computer-readable media having computer-executable instructions and apparatuses detect a structured handwritten object is detected in a handwritten document. A primitive structure, which is associated with a set of component objects, is formed. A candidate handwritten object is identified by detecting a corresponding logical structure. A classifier for the candidate handwritten object is determined. Consequently, the candidate handwritten object is validated from the classifier.

Abstract: Computer-readable media having computer-executable instructions and apparatuses detect a table in a handwritten document. Line segments are derived from drawing strokes so that a bounding frame of a candidate table is obtained. An associated table structure is consequently recognized from the bounding frame, lines segments within the bounding frame, and their intersection points. A classifier that reflects at least one table characteristic is determined, and the candidate table is consequently validated or rejected.

Abstract: Systems and methods for detecting doctored JPEG images are described. In one aspect, a JPEG image is evaluated to determine if the JPEG image comprises double quantization effects of double quantized Discrete Cosine Transform coefficients. In response to results of these evaluation operations, the systems and methods determine whether the JPEG image has been doctored and identify any doctored portion.

Abstract: Systems and methods for detecting doctored JPEG images are described. In one aspect, a JPEG image is evaluated to determine if the JPEG image comprises double quantization effects of double quantized Discrete Cosine Transform coefficients. In response to results of these evaluation operations, the systems and methods determine whether the JPEG image has been doctored and identify any doctored portion.

Abstract: Computer-readable media having computer-executable instructions and apparatuses detect a structured handwritten object is detected in a handwritten document. A primitive structure, which is associated with a set of component objects, is formed. A candidate handwritten object is identified by detecting a corresponding logical structure. A classifier for the candidate handwritten object is determined. Consequently, the candidate handwritten object is validated from the classifier.

Abstract: Computer-readable media having computer-executable instructions and apparatuses detect a table in a handwritten document. Line segments are derived from drawing strokes so that a bounding frame of a candidate table is obtained. An associated table structure is consequently recognized from the bounding frame, lines segments within the bounding frame, and their intersection points. A classifier that reflects at least one table characteristic is determined, and the candidate table is consequently validated or rejected.