Abstract: The present teaching relates to impression count determination. A forecasting time series (TS) model is established based on measured impression counts (MI-counts). Metrics are calculated from MI-counts from a sub-range of a profile. Different types of data characteristics are detected based on the metrics. Hybrid correction applied to the profile is determined based on the detected data characteristics. Corrected impression counts (I-counts) for the profile are generated via the hybrid correction operation based on the MI-counts and I-counts estimated from the forecasting TS models and provided for determining a level of viewership of the content at the site.

Abstract: The present teaching relates to impression count determination. A forecasting time series (TS) model is established based on measured impression counts (MI-counts). Metrics are calculated from MI-counts from a sub-range of a profile. Different types of data characteristics are detected based on the metrics. Hybrid correction applied to the profile is determined based on the detected data characteristics. Corrected impression counts (I-counts) for the profile are generated via the hybrid correction operation based on the MI-counts and I-counts estimated from the forecasting TS models and provided for determining a level of viewership of the content at the site.

Abstract: A method, non-transitory computer readable medium and apparatus for calculating a by spot occupancy of a parking lot are disclosed. For example, the method includes receiving an indication of a triggering event, sending a query to receive a first image and a second image in response to the triggering event, receiving the first image and the second image, analyzing the first image and the second image to determine a change in an occupancy status of a parking spot within the parking lot and calculating the by spot occupancy of the parking lot based on the change in the occupancy status of the parking spot.

Abstract: A neural network is used to model to model the joint distribution of attributes across multiple health surveys. These multiple health surveys include large scale survey datasets and small scale survey datasets. The neural network model is trained using a combined dataset of the large scale survey datasets and the small scale survey datasets. The large scale survey datasets and the small scale survey datasets may include missing value indicators. The joint distribution of attributes modeled by the neural network model are the used to impute substitute values for the missing values to thereby create an output large scale dataset that does not include missing values.

Abstract: A method, system, and apparatus for license plate relighting comprises collecting an image of a license plate, performing license plate recognition on the image of the license plate; calculating a confidence metric for the license plate recognition; and performing a shadow detection and relighting method if the confidence metric is below a predetermined threshold, comprising identifying a shaded region of said license plate, determining if the shaded region is actually shaded, and relighting the actually shaded region.

Abstract: Methods and systems for localizing numbers and characters in captured images. A side image of a vehicle captured by one or more cameras can be preprocessed to determine a region of interest. A confidence value of series of windows within regions of interest of different sizes and aspect ratios containing a structure of interest can be calculated. Highest confidence candidate regions can then be identified with respect to the regions of interest and at least one region adjacent to the highest confidence candidate regions. An OCR operation can then be performed in the adjacent region. An identifier can then be returned from the adjacent region in order to localize numbers and characters in the side image of the vehicle.

Abstract: A method, system, and apparatus for license plate relighting comprises collecting an image of a license plate, performing license plate recognition on the image of the license plate; calculating a confidence metric for the license plate recognition; and performing a shadow detection and relighting method if the confidence metric is below a predetermined threshold, comprising identifying a shaded region of said license plate, determining if the shaded region is actually shaded, and relighting the actually shaded region.

Abstract: A method, system, and apparatus for parking occupancy detection comprises collecting video of a blockface with at least one video recording module, identifying a number of possible parking spaces along the blockface in the collected video, defining, a region of interest for each of the possible parking spaces, detecting a time dependent occupancy of the defined regions of interest for each of the possible parking spaces, and reporting the time dependent occupancy. Drift correction of the recorded video and ground truth comparisons of occupancy determinations may be provided.

Abstract: A method, system, and apparatus for video frame alignment comprises collecting video data comprising at least two video frames; extracting a line profile along at least one line profile in each of the at least two video frames; selecting one of the at least two video frames as a reference video frame; segmenting each of the at least one line profiles into a plurality of segmented line profile group segments; aligning the plurality of segmented line profiles with the segmented line profiles in the reference video frame; translating each of the at least two video frames for each of the plurality of corresponding segmented line profile alignments; and removing a camera shift from the at least two video frames according to the translation and alignment of the plurality of segmented line profiles with the plurality of segmented line profile in the reference video frame.

Abstract: A method, system, and apparatus for parking occupancy detection comprises collecting video of a blockface with at least one video recording module, identifying a number of possible parking spaces along the blockface in the collected video, defining, a region of interest for each of the possible parking spaces, detecting a time dependent occupancy of the defined regions of interest for each of the possible parking spaces, and reporting the time dependent occupancy. Drift correction of the recorded video and ground truth comparisons of occupancy determinations may be provided.

Abstract: A method, system, and apparatus for video frame alignment comprises collecting video data comprising at least two video frames; extracting a line profile along at least one line profile in each of the at least two video frames; selecting one of the at least two video frames as a reference video frame; segmenting each of the at least one line profiles into a plurality of segmented line profile group segments; aligning the plurality of segmented line profiles with the segmented line profiles in the reference video frame; translating each of the at least two video frames for each of the plurality of corresponding segmented line profile alignments; and removing a camera shift from the at least two video frames according to the translation and alignment of the plurality of segmented line profiles with the plurality of segmented line profile in the reference video frame.

Abstract: A method, non-transitory computer readable medium and apparatus for calculating a by spot occupancy of a parking lot are disclosed. For example, the method includes receiving an indication of a triggering event, sending a query to receive a first image and a second image in response to the triggering event, receiving the first image and the second image, analyzing the first image and the second image to determine a change in an occupancy status of a parking spot within the parking lot and calculating the by spot occupancy of the parking lot based on the change in the occupancy status of the parking spot.

Abstract: Methods and systems for tag recognition in captured images. A candidate region can be localized from regions of interest with respect to a tag and a tag number shown in the regions of interest within a side image of a vehicle. A number of confidence levels can then be calculated with respect to each digit recognized as a result of an optical character recognition operation performed with respect to the tag number. Optimal candidates within the candidate region can be determined for the tag number based on individual character confidence levels among the confidence levels. Optimal candidates from a pool of valid tag numbers can then be validated using prior appearance probabilities and data returned, which is indicative of the most probable tag to be detected to improve image recognition accuracy.

Abstract: Methods and systems for tag recognition in captured images. A candidate region can be localized from regions of interest with respect to a tag and a tag number shown in the regions of interest within a side image of a vehicle. A number of confidence levels can then be calculated with respect to each digit recognized as a result of an optical character recognition operation performed with respect to the tag number. Optimal candidates within the candidate region can be determined for the tag number based on individual character confidence levels among the confidence levels. Optimal candidates from a pool of valid tag numbers can then be validated using prior appearance probabilities and data returned, which is indicative of the most probable tag to be detected to improve image recognition accuracy.

Abstract: Methods and systems for localizing numbers and characters in captured images. A side image of a vehicle captured by one or more cameras can be preprocessed to determine a region of interest. A confidence value of series of windows within regions of interest of different sizes and aspect ratios containing a structure of interest can be calculated. Highest confidence candidate regions can then be identified with respect to the regions of interest and at least one region adjacent to the highest confidence candidate regions. An OCR operation can then be performed in the adjacent region. An identifier can then be returned from the adjacent region in order to localize numbers and characters in the side image of the vehicle.

Abstract: Processes are presented for creating electronic banding compensation profiles for raster output scanner (ROS) devices by printing and scanning a test pattern having a series of strips extending along a process direction and spaced from one another along a cross process (fast scan) direction, analyzing the scanned data to determine facet-specific banding errors corresponding to individual strips, and selectively adjusting banding correction profiles to counteract the banding errors.

Abstract: Raster Output Scanners and printing systems are presented along with methods for mitigating banding in printing systems, in which electronic banding compensation is employed using cross-process direction light source intensity banding correction profiles tailored to corresponding reflective facets of a rotating polygon.

Abstract: Processes are presented for creating electronic banding compensation profiles for raster output scanner (ROS) devices by printing and scanning a test pattern having a series of strips extending along a process direction and spaced from one another along a cross process (fast scan) direction, analyzing the scanned data to determine facet-specific banding errors corresponding to individual strips, and selectively adjusting banding correction profiles to counteract the banding errors.

Abstract: Raster Output Scanners and printing systems are presented along with methods for mitigating banding in printing systems, in which electronic banding compensation is employed using cross-process direction light source intensity banding correction profiles tailored to corresponding reflective facets of a rotating polygon.

Abstract: Systems and methods are provided for calibrating a sensory array to ensure a robust cross-process registration measurement. The calibration is implemented using a calibration step that determines the signature error amount of a given image reading sensor. The signature error amount for the sensor is stored in a signature error look-up table. When the sensors are used to sense print head alignment, the correction may be implemented by accessing the signature error look-up table for the given sensor when calibrating the print heads. The signature error look-up table provides an amount of offset for each sensor that is used in determining the appropriate head position of a given print head to calibrate the print heads for the signature error associated with the given sensor.