Abstract: A system accesses content structure that includes a first attribute table including a first list of attributes of a first object, and a first mapping including first attribute values. The first list of attributes of the first object also includes a quality attribute indicating a first quality. After a request to modify quality is received, the system searches a plurality of content structures for a suitable second content structure that comprises a second attribute table including a second list of attributes of a second object. The suitable content structure has another attribute that matches a corresponding attribute of the first list of attributes of the first object and a quality attribute indicating a second quality. The system modifies the first attribute table to include the second list of attributes of the second object. In this way content is generated that is of higher or lower quality than the original content.

Abstract: An intelligent police car loudspeaker includes a loudspeaker mechanism composed of a dust cover, a paper cone, a yoke ring, a centering piece, a magnet, a voice coil, and a soft iron core. The voice coil is disposed around the soft iron core. The magnet is disposed around the voice coil. The paper cone is disposed above the magnet. The centering piece is disposed above the magnet and located inside the paper cone. The yoke ring is disposed above the centering piece and located inside the paper cone. The dust cover is configured to cover an opening of the paper cone. With the present invention, the level of intelligence of the loudspeaker can be improved.

Abstract: Provided is a method for reconstructing content image data. The method includes selecting a first point and a second point in a first image of first content, selecting a third point and a fourth point in a second image of second content (the second image is an image corresponding to the first image and the third point and the fourth point are points in an image corresponding to the first point and the second point, respectively), generating a first reference vector using the first point and the second point, generating a second reference vector using the third point and the fourth point, calculating a rotation, scale, and transformation (RST) value from the first image to the second image using the first reference vector and the second reference vector; and reconstructing the second content using the calculated RST value.

Abstract: A system, a method, and a computer program for surveillance and area encroachment detection, including receiving satellite image data containing an image of a geospatial area, extracting features from the satellite image data of an object in the image, classifying the object in the image based on the extracted features, comparing the extracted features of the object to previously extracted features for the geospatial area, determining delta features between the extracted features of the object and the previously extracted features for the geospatial area, determining existence of an alarm event in the geospatial area based on the delta features, and transmitting an alarm event message to a communicating device.

Abstract: Various approaches are disclosed to temporally and spatially filter noisy image data—generated using one or more ray-tracing effects—in a graphically rendered image. Rather than fully sampling data values using spatial filters, the data values may be sparsely sampled using filter taps within the spatial filters. To account for the sparse sampling, locations of filter taps may be jittered spatially and/or temporally. For filtering efficiency, a size of a spatial filter may be reduced when historical data values are used to temporally filter pixels. Further, data values filtered using a temporal filter may be clamped to avoid ghosting. For further filtering efficiency, a spatial filter may be applied as a separable filter in which the filtering for a filter direction may be performed over multiple iterations using reducing filter widths, decreasing the chance of visual artifacts when the spatial filter does not follow a true Gaussian distribution.

Abstract: An object detection device equipped with: a first object detection unit that uses a predetermined prediction model to acquire, from a known first image for which the coordinates of a detection subject are known, a confidence value with respect to each detection region extracted from a plurality of positions in the first image; a parameter determination unit that, on the basis of the acquired confidence value, determines parameters that include a detection threshold value which is a threshold value with respect to the confidence value; and a second object detection unit that uses the prediction model to acquire a confidence value with respect to each detection region extracted from the narrowed-down detection region candidates, and detects the detection subject on the basis of the acquired confidence value.

Abstract: Methods and systems of breaking pursuit of a target. One example embodiment includes an electronic processor configured to develop a first identifier for the target and determine a breaking junction and an intercept point associated with a cornering route. The processor is configured to determine a geographical area associated with at least one possible target flee route for the target and determine a second identifier for an object within the geographical area. The processor is configured to develop a discrimination factor representing a degree of differentiation of the first identifier from the second identifier. The processor is configured to predict whether the discrimination factor will exceed a threshold when the pursuit asset arrives at the breaking junction. The processor is configured to, in response to the discrimination factor exceeding the threshold, notify the pursuit asset to break pursuit of the target at the breaking junction to execute the cornering route.

Abstract: A control device assumes that observation light observed by an imaging device is composite light of primary reflection light and secondary reflection light. The control device acquires three or more samples of a brightness amplitude value of the observation light, calculates a phase error caused by the secondary reflection light using these samples, calculates a corrected phase value by correcting a phase value of the observation light using the phase error, and calculates a three-dimensional position of the measurement point on the measurement object based on the corrected phase value.

Abstract: An information processing method includes the following performed using a computer: acquiring images created through capturing at the same time, and positions and capturing directions of image capturing devices that created respectively each of the images; acquiring object detection results obtained respectively through object detection processes performed using each of the images; and performing a training data selection process using the images, the positions and the capturing directions, and the object detection results acquired. The training data selection process includes: calculating a common region that is seen in the images in common, based on the positions and the capturing directions; determining a degree of agreement among the object detection results in the common region; and selecting an image to be used as training data from among the images, according to the degree of agreement.