Abstract: An off page condition or invalid sensors position data is detected by checking the errors from an initial transformation parameter estimation. If an abnormally large error is encountered, a sensor's reading (position data) may be invalid or the sensor was off page. Then the invalid sensor data will be identified and removed. Finally the transformation parameters will be re-estimated using valid sensor position data only. A weighted least-square minimization is used by considering the sensor lift situation. If a sensor is lifted, the weight for the error related to the sensor will be set to a small weight or zero. Also considered are the geometric properties of sensor locations in weighting the sensor error. A confidence measurement of the sensor data and associated error is performed. The confidence measurement is derived from an error ellipse at 95% confidence level.

Abstract: Two images are stitched together through minimization of a cost function that consists of registration errors from image data of the two images, as well as the estimated errors from a set of sensors. The weight function in the cost function is derived from the confidence value of sensor estimation that considers the sensor errors including lift and off page as well as a measure of accuracy of the sensor readings. Weights are used to adjust image registration accuracy against sensor accuracy to produce a set of registration parameters that would best stitch the two images together. In order to handle large errors for initial registration parameters and to avoid local minima in the minimization process, the image pair may be registered in a lower resolution and then refined in a higher resolution.

Abstract: The present invention is directed towards systems and methods for decoding a bar code. Decoding the bar code includes segmenting the bar code into black tokens, white tokens and gray boundary pixels. Decoding the bar code also includes calculating a boundary position of each token with sub-pixel positional accuracy using intensity values of pixels in a boundary region.

Abstract: A method of estimating and compensating for jitter between two video frames in a digital video sequence comprises comparing a pixel subset in one video frame to candidate matching pixel subsets within a search area in the other video frame to obtain a preliminary matching pixel subset. Based on the location of the preliminary matching pixel subset, an adapted search area is formed and the comparing is continued within the adapted search area to obtain a final matching pixel subset. Motion is calculated between the video frames based on the final match thereby to estimate the jitter. The video frames are then transformed based on the estimated jitter.

Abstract: The present invention is a system and method for decoding an image of a bar code. Decoding the barcode includes tokenizing a plurality of pixels in the image of the barcode based upon a plurality of thresholds to form a first set of tokens. Decoding the barcode also includes re-tokenizing the plurality of pixels in the image of the barcode based upon the intensity of the pixels in the plurality of tokens, and the relativity intensity of neighboring tokens, to form a second set of tokens.

Abstract: The present invention is directed towards systems and methods for decoding a bar code. Decoding the bar code includes segmenting the bar code into black tokens, white tokens and gray boundary pixels. Decoding the bar code also includes calculating a boundary position of each token with sub-pixel positional accuracy using intensity values of pixels in a boundary region.

Abstract: Methods and systems for detecting and compensating for motion depicted in a sequence of frames are disclosed. One example method includes converting video frames to monochrome. Conversion of an image to monochrome includes sampling luminance data of a portion of pixels in the image to identify the image as being of a particular image type, selecting a bit plane of the image based on the identified image type, and converting the image to a monochrome image using the selected bit plane. After conversion of video frames to monochrome, a pixel subset in a monochrome image corresponding to a first frame is compared to candidate matching pixel subsets within a search area of a monochrome image corresponding to a second, consecutive frame. The frames are processed to compensate for perceived motion blur based on the detected inter-frame motion.

Abstract: A method of estimating the distance to a subject using image signals generated by autofocus image sensors of an image capture device comprises processing image data of each image sensor to detect edges therein and for each image sensor generating a corresponding edge image, correlating the edge images to determine the shift of one edge image relative to the other edge image that yields the best match therebetween, and calculating a distance estimation based at least on the determined shift.

Abstract: A method of estimating and compensating for jitter between two video frames in a digital video sequence comprises comparing a pixel subset in one video frame to candidate matching pixel subsets within a search area in the other video frame to obtain a preliminary matching pixel subset. Based on the location of the preliminary matching pixel subset, an adapted search area is formed and the comparing is continued within the adapted search area to obtain a final matching pixel subset. Motion is calculated between the video frames based on the final match thereby to estimate the jitter. The video frames are then transformed based on the estimated jitter.