Abstract: A method and system for determining an orientation of a data-encoding symbol, such as a PDF417 bar-code symbol, and for decoding the encoded data. One example method may include detecting an encoded data sequence at a first location of the symbol and at a second location of the symbol. The symbol orientation may be determined at least partially based on the first and second locations. Strings of data encoded in the symbol may then be decoded by processing the strings to generate indices and accessing a codeword array using the indices to look up codewords corresponding to the strings. A match between a string and a codeword may be verified with data in the codeword array.

Abstract: A method and system for scanning a digital image for detecting the representation of an object, such as a face, and for reducing memory requirements of the computer system performing the image scan. One example method includes identifying an original image and downsamples the original image in an x-dimension and in a y-dimension to obtain a downsampled image that requires less storage space than the original digital image. A first scan is performed of the downsampled image to detect the representation of an object within the downsampled image. Then, the original digital image is divided into at least two image blocks, where each image block contains a portion of the original digital image. A second scan is then performed of each of the image blocks to detect the representation of the object within the image blocks.

Abstract: Converting a digital image from color to gray-scale. In one example embodiment, a method for converting a digital image from color to gray-scale is disclosed. First, an unconverted pixel having red, green, and blue color channels is selected from the color digital image. Next, the red color channel of the pixel is multiplied by ?. Then, the green color channel of the pixel is multiplied by ?. Next, the blue color channel of the pixel is multiplied by ?. Then, the results of the three multiplication operations are added together to arrive at a gray-scale value for the pixel. Finally, these acts are repeated for each remaining unconverted pixel of the color digital image to arrive at a gray-scale digital image. In this example method, ?+?+?1 and ?>?.

Abstract: A method and system for efficiently detecting faces within a digital image. One example method includes identifying a digital image comprised of a plurality of sub-windows and performing a first scan of the digital image using a coarse detection level to eliminate the sub-windows that have a low likelihood of representing a face. The subset of the sub-windows that were not eliminated during the first scan are then scanned a second time using a fine detection level having a higher accuracy level than the coarse detection level used during the first scan to identify sub-windows having a high likelihood of representing a face.

Abstract: A method and system for determining an orientation of a data-encoding symbol, such as a PDF417 bar-code symbol, and for decoding the encoded data. One example method may include detecting an encoded data sequence at a first location of the symbol and at a second location of the symbol. The symbol orientation may be determined at least partially based on the first and second locations. Strings of data encoded in the symbol may then be decoded by processing the strings to generate indices and accessing a codeword array using the indices to look up codewords corresponding to the strings. A match between a string and a codeword may be verified with data in the codeword array.

Abstract: A method of locating and decoding a two-dimensional machine-readable symbol in a digital image, where the symbol has three finder patterns adjacent respective corners of the symbol, takes into account the possibility of skew and pitch when delineating the symbol in the digital image by estimating the unknown fourth corner based on lines passing through known symbol edge points. On the basis of the delineating, a reference grid is mapped to the digital image and a bit stream is extracted based on the mapping.

Abstract: A method and system for scanning a digital image for detecting the representation of an object, such as a face, and for reducing memory requirements of the computer system performing the image scan. One example method includes identifying an original image and downsamples the original image in an x-dimension and in a y-dimension to obtain a downsampled image that requires less storage space than the original digital image. A first scan is performed of the downsampled image to detect the representation of an object within the downsampled image. Then, the original digital image is divided into at least two image blocks, where each image block contains a portion of the original digital image. A second scan is then performed of each of the image blocks to detect the representation of the object within the image blocks.

Abstract: A method and system for efficiently detecting faces within a digital image. One example method includes identifying a digital image comprised of a plurality of sub-windows and performing a first scan of the digital image using a coarse detection level to eliminate the sub-windows that have a low likelihood of representing a face. The subset of the sub-windows that were not eliminated during the first scan are then scanned a second time using a fine detection level having a higher accuracy level than the coarse detection level used during the first scan to identify sub-windows having a high likelihood of representing a face.

Abstract: Converting a digital image from color to gray-scale. In one example embodiment, a method for converting a digital image from color to gray-scale is disclosed. First, an unconverted pixel having red, green, and blue color channels is selected from the color digital image. Next, the red color channel of the pixel is multiplied by ?. Then, the green color channel of the pixel is multiplied by ?. Next, the blue color channel of the pixel is multiplied by ?. Then, the results of the three multiplication operations are added together to arrive at a gray-scale value for the pixel. Finally, these acts are repeated for each remaining unconverted pixel of the color digital image to arrive at a gray-scale digital image. In this example method, ?+?+?1 and ?>?.

Abstract: A method of locating and decoding a two-dimensional machine-readable symbol in a digital image, where the symbol has three finder patterns adjacent respective corners of the symbol, takes into account the possibility of skew and pitch when delineating the symbol in the digital image by estimating the unknown fourth corner based on lines passing through known symbol edge points. On the basis of the delineating, a reference grid is mapped to the digital image and a bit stream is extracted based on the mapping.

Abstract: A method of locating and verifying a finder pattern in a two-dimensional machine-readable symbol, comprises scanning the image along a line to locate a sequence of regions having different optical properties corresponding to that which would be encountered along a line passing through the center of the finder pattern thereby to locate a candidate finder pattern. When a candidate finder pattern is located, performing a multi-stage verification to verify that the candidate finder pattern is an actual finder pattern.

Abstract: A method of determining the vertices of a character in a two-dimensional barcode symbol image includes tracing a contour around a character. The contour is examined and pixels therealong believed to be vertices of the character are determined. The relative positions of the determined pixels are compared to determine if they satisfy a threshold. If the relative positions of the determined pixels satisfy the threshold, the determined pixels are designated as the vertices of the character. If the relative positions of the determined pixels satisfy the threshold, the determined pixels are designated as the vertices of the character. If the relative positions of the determined pixels do not satisfy the threshold, new pixels along the contour are selected using geometric relationships between the determined pixels to replace determined pixels that are not vertices of the character.