Abstract:
A fast image-based barcode detection and recognition technique allows a user of a device to analyze an image containing a barcode, locating the barcode containing region of the image automatically, without requiring a user to frame and align the image. In one embodiment, the technique may locate multiple omni-directional barcode regions simultaneously.

Description:
BACKGROUND 
       [0001]    This disclosure relates generally to the field of barcode detection. More particularly, but not by way of limitation, it relates to a technique for isolating a region of an image containing a barcode. 
         [0002]    Barcodes have become an important aspect of modern life, with barcodes printed onto a wide variety of objects. Barcode capture and decoding capabilities have been built into many types of devices, including mobile devices such as smartphones, often using the built-in camera of the device for capturing an image that may contain a barcode. However, numerous challenges can make quick detection and recognition of barcodes difficult and require increased use of processing resources and time. As illustrated in prior art  FIG. 1 , such challenges include but are not limited to overcoming defocus blur ( 110 ), motion blur ( 120 ), perspective distortion ( 130 ), multiple barcodes ( 140 ), complex background and non-centered barcodes ( 150 ), rotation ( 160 ), poor or uneven illumination ( 170 ), and small scale ( 180 ). 
       SUMMARY 
       [0003]    This disclosure pertains to device, methods, and computer readable media for implementing a more effective barcode detection technique. In some embodiments, the techniques disclosed herein may partition an image into subimages, which are then analyzed to determine whether they contain a portion of a barcode, and a region of the image containing the barcode based on the subimages that contain the barcode is segmented from the image for providing to a barcode decoder. 
         [0004]    Thus, in one embodiment describe herein, a method of detecting a barcode in an image comprises partitioning an image captured by an image capture device into a plurality of subimages; determining the plurality of subimages that contain at least a portion of the barcode; and selecting a region of the image containing the barcode, comprising the plurality of subimages that contain at least a portion of the barcode. 
         [0005]    In another embodiment, an apparatus comprises an image sensor; a programmable control device; a memory coupled to the programmable control device, wherein instructions are stored in the memory, the instructions causing the programmable control device to partition an image captured by the image sensor into a plurality of subimages; select a plurality of barcode subimages from the plurality of subimages, according to a predetermined density threshold, a predetermined parallel line threshold, and a predetermined line count threshold; and select an oriented region of the image containing the barcode, comprising the plurality of barcode subimages. 
         [0006]    In yet another embodiment, a non-transitory program storage device, readable by a programmable control device, comprises instructions stored thereon for causing the programmable control device to partition an image containing a barcode captured by an image sensor into a plurality of subimages; determine the plurality of subimages that contain at least a portion of the barcode; and select a region of the image containing the barcode, comprising the plurality of subimages that contain at least a portion of the barcode. 
         [0007]    The fast image-based barcode detection and recognition techniques described herein allow a user of a device to analyze an image containing a barcode, locating the barcode containing region of the image automatically, without requiring a user to frame and align the image. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0008]      FIG. 1  illustrates examples of challenges causes by various distortions of barcodes according to the prior art. 
           [0009]      FIG. 2  illustrates, in flowchart form, an embodiment of a technique for detecting barcodes. 
           [0010]      FIG. 3  illustrates, in flowchart form, an embodiment of an image preprocessing technique according to one embodiment. 
           [0011]      FIG. 4  illustrates converting a grayscale image to an edge-detected image according to one embodiment. 
           [0012]      FIG. 5  illustrates a localized edge detected image partitioned into subimages according to one embodiment. 
           [0013]      FIG. 6  illustrates, in flowchart form, a technique for recognizing subimages containing barcodes according to one embodiment. 
           [0014]      FIGS. 7 and 8  illustrate a technique for determine orientation of a barcode using singular value decomposition according to one embodiment. 
           [0015]      FIG. 9  illustrates segmenting a bounded region of an image that contains a barcode according to one embodiment. 
           [0016]      FIG. 10  is a block diagram illustrating an electronic device according to one embodiment in which the barcode detection techniques may be implemented. 
       
    
    
     DETAILED DESCRIPTION 
       [0017]    In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the invention. It will be apparent, however, to one skilled in the art that the invention may be practiced without these specific details. In other instances, structure and devices are shown in block diagram form in order to avoid obscuring the invention. References to numbers without subscripts or suffixes are understood to reference all instance of subscripts and suffixes corresponding to the referenced number. Moreover, the language used in this disclosure has been principally selected for readability and instructional purposes, and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter. Reference in the specification to “one embodiment” or to “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiments is included in at least one embodiment of the invention, and multiple references to “one embodiment” or “an embodiment” should not be understood as necessarily all referring to the same embodiment. 
         [0018]    Although the present disclosure is written in terms of handheld personal electronic image capture devices, the techniques described below may be implemented in other types of devices, such as traditional digital cameras or dedicated barcode scanners. 
         [0019]    There are multiple types of barcodes, including what are known as linear or 1D barcodes, 2D barcodes, etc. Unless otherwise stated below, references to barcodes in the following refer to 1D barcodes. Although there are numerous 1D barcode encoding schemes, the discussion below does not depend upon any specific 1D barcode encoding scheme, because the decoding of the barcode is outside of the scope of the present invention. 
         [0020]    Barcodes are composed of a dense sequence of parallel lines on a contrasting background. The information encoded in the barcodes is encoded in the abrupt transitions of pixel intensities, as they transition between the line and the contrasting background. Such a characteristic means that barcodes may be identified by edge detecting an intensity image. In the discussion below, an edge is defined as a discontinuity in an intensity gradient, and an edge pixel is defined as a pixel location of the gradient discontinuity, typically expressed in row, column coordinates. 
         [0021]      FIG. 2  is a flowchart illustrating a technique  200  for detecting barcodes according to one embodiment. In block  210 , an image is captured by an electronic device that may contain a barcode by an image capture logic of the device, typically a camera. Any image capture technique may be used as desired, and image capture is not discussed further herein. In block  220 , the captured image is preprocessed, to generate an edge detected image for further analysis. Once the image has been preprocessed into edge detected image in block  220 , various embodiments perform localization, recognition, orientation, and segmentation on the edge image. In block  230 , a localization technique is used to locate regions of the image that may contain a portion of a barcode. In block  240 , a recognition and orientation technique determines whether the potential barcode regions actually contain a portion of a barcode and the orientation of the barcode. In one embodiment, the recognition and orientation phase may rotate the barcode into a normalized orientation. In block  250 , a segmentation technique extracts and crops only the barcode region from the original image, rejecting the rest of the image. Finally, in block  260 , a decoder is provided the barcode region and decodes the barcode. Any known barcode decoding technique may be used by the decoder and decoding is not discussed further herein. 
         [0022]    Preprocessing 
         [0023]    Prior to beginning the localization, recognition, orientation, and segmentation phases, the captured image may be preprocessed into an edge detected image.  FIG. 3  is a flowchart illustrating a preprocessing technique  300  according to one embodiment. In block  310  the original image may be subsampled to a predetermined number of pixels, such as 100,000 pixels. In block  320 , the subsampled image is converted into a grayscale image. The camera providing the image to be used for barcode detection typically provides a color image, but color is generally irrelevant in barcode detection. The subsampled image in one embodiment may therefore be preprocessed by performing color masking to eliminate the chroma values and leave only the luminance values of the image, converting the luminance values into a grayscale image. Color masking attempts to black out highly colorful parts of the image based on the pixel RGB distributions, to avoid getting irrelevant edges in the edge detection stage. For example, text regions nearby the barcode region that are printed on a colorful background can produce lots of undesired parallel edges because of the shapes of alphabets. In one embodiment, the hardware of the device performing the barcode detection techniques may provide the image as a 10-bit grayscale image. Other embodiments may use software to perform the conversion from a color image to a grayscale image. In one embodiment, adaptive contrast enhancement may be performed in block  330  to enhance the grayscale image. 
         [0024]    Once the grayscale image has been produced, an edge detection technique may be performed in block  340  to locate edge pixels. In one embodiment, illustrated in  FIG. 4 , a Canny edge detection technique may be used to convert grayscale image  410  into edge detected image  420 , using non-maximum suppression and hysteresis thresholding values to produce edge pixels that form continuous lines and to reject scattered noisy edge pixels. Although Canny edge detection is preferred, other edge detection techniques may be used as desired. 
         [0025]    The edge detected grayscale image is then used for the localization, recognition, and orientation phases. 
         [0026]    Localization 
         [0027]    Embodiments of the localization technique break up the subsampled image into a predetermined number of subimages, in one embodiment, 100 subimages, each containing 1% of the subsampled image, such as the image  500  of  FIG. 5 , where the subimages are illustrated with gridlines. 
         [0028]    The size (and thus number) of subimages is a tradeoff between detection and recognition issues. The smaller the subimages, the more samples of the barcode are obtained, which can give localization with greater accuracy, allowing the detection of smaller barcodes and barcodes that are further away from the camera. However, if the subimage is too small, it may contain no lines and thus no longer reliably represent a barcode region. In addition, a camera too close to the object may cause similar problems. 
         [0029]    Larger subimages that are representative of barcode regions, on the other hand, can improve recognition by allowing good estimates of the number of edge pixels, gradient properties, and the number of lines for each subimage, as well as comparing those properties across subimages. However, if the subimage is too large, it may contain part of the background region in addition to the barcode, thus no longer reliably represent a barcode region. In addition, a camera too far from the object may cause similar problems. 
         [0030]    In addition, increasing the number of subimages may affect the speed in which all of the subimages may be processed by the localization technique. In one embodiment, using subimages that contain 1% of the subsampled image may allow reliable localization when the camera is placed up to 1 foot away from the barcode. Distances greater than 1 foot may be used, but may cause a decreased likelihood of success of reading the barcode by the barcode decoder. 
         [0031]    Recognition and Orientation 
         [0032]    Once the image has been divided into subimages, various embodiments then attempt to select regions of dense parallel lines in the edge pixels of each subimage, to find subimages of the image that may have barcodes. Barcode-containing subimages are invariant to scale, rotation, and perspective distortions, when considered along with orientation information produced in the orientation phase described below. 
         [0033]    In one embodiment, the recognition and orientation phase  600  illustrated in  FIG. 6  comprises using three filters: a density filter  610  to find subimages with dense lines, a parallel line filter  620  to find subimages with parallel lines, and a Hough line filter  630  to determine how many lines are in the subimage. These filters are typically implemented using software in the device, but may be implemented in software, firmware, or hardware as desired. The determination may be sequential, as illustrated in  FIG. 6 , first finding subimages with dense lines using the density filter  610 , then refining that group using the parallel filter  620  to eliminate subimages with dense non-parallel lines, then further refining that group using the Hough filter  630  to select only subimages with sufficient numbers of parallel lines. Alternatively, the determination of the density filter  610 , parallel filter  620 , and Hough filter  630  may be performed in parallel, with the recognition phase  600  selecting subimages that are marked by each of the filters as meeting the criteria of that filter. 
         [0034]    The density filter  610  in one embodiment selects subimages according to a number of edge pixels. In one embodiment, the density is calculated using the number of edge pixels in the subimage divided by the number of pixels in the subimage. A subimage having a number of edge pixels that meets or exceeds a predetermined threshold amount of the pixels in the subimage may be a barcode subimage. In one embodiment, the threshold value is that 60% of the subimage pixels are edge pixels, but other threshold values can be used. By restricting the consideration of subimages to only subimages with a sufficient density of lines, the effects of noise in the original image may be reduced. 
         [0035]    The parallel filter  620  in one embodiment selects subimages that contain parallel lines. If a subimage contains more than a predetermined threshold number of edge pixel gradients in any one direction, the subimage may contain a barcode portion. In one embodiment, the gradient direction of edge pixels may be quantized into four bins, each corresponding to one of the four angles 0°-45° (and 180°-225°), 45°-90° (and 225°-270°), 90°-135° (and 270°-315°), and 135°-180° (and 315°-360°). Other angles and number of angles may be used as desired to define different numbers and size bins. If a subimage has more than the threshold value of edge pixels in more than one of the quantized directions, the subimage may have barcode portions at more than one orientation. In one embodiment, a threshold value of 65% may be used for selecting subimages with parallel lines. In one embodiment, as pixels are quantized into the appropriate bins, the subimage may be marked as containing parallel lines as soon as enough pixels have been quantized into a bin to meet the threshold value, and the quantization terminated. In other embodiments, all pixels in the subimage are quantized into one of the several bins, and the subimage is not marked as containing parallel lines until all pixels have been quantized. 
         [0036]    A Hough line filter  630  may be used to find lines in the subimage, and denoise the data. In one embodiment, a singular value decomposition may be performed on the subimage to determine an orientation of lines in the subimage prior to performing the Hough line filtration, using a higher resolution for orientation angle than only four quantized angle values, to enhance the performance of the Hough line filter  630 . 
         [0037]    A singular value decomposition of an m×n matrix M is a factorization of the form M=UΣV T , where U is an m×m unitary matrix, Σ is an m×n rectangular diagonal matrix values on the diagonal, and V T  is an n×n unitary matrix. The diagonal entries σ 1 , σ 2 , . . . , σ n  of Σ are known as the singular values of M, while the m columns of U and the n columns of V are known as the left and right singular vectors of M. Any technique for calculating the singular value decomposition may be used, however, because singular value decomposition can be very time consuming, faster techniques are preferred for real-time processing. 
         [0038]    The principal singular vector indicates the direction of the greatest gradient variance, thus providing an orientation of the lines in the subimage. In one embodiment, the singular values can be used as input to a certainty filter to provide a significance test for the singular value decomposition, where a subimage that is not significantly oriented may be filtered out by the certainty filter, leaving only subimages that are clearly oriented. For example,  FIG. 7  illustrates a collection of lines  700  in which the gradient has a principal orientation along line  710  (corresponding to a principal singular vector), and a secondary orientation along line  720 , but the singular values σ 1  and σ 2  can be used to determine that the lines have a significant orientation by the calculation of 
         [0000]    
       
         
           
             Significance 
             = 
             
               
                 ( 
                 
                   
                     σ 
                     1 
                   
                   - 
                   
                     σ 
                     2 
                   
                 
                 ) 
               
               
                 ( 
                 
                   
                     σ 
                     1 
                   
                   + 
                   
                     σ 
                     2 
                   
                 
                 ) 
               
             
           
         
       
     
         [0039]    If the significance exceeds a predetermined threshold value, the subimage may be considered to be strongly oriented. If the significance is less than the threshold value, the subimage may be considered as not strongly oriented, and thus less likely to contain a barcode. An example of a non-oriented image is illustrated in  FIG. 8 , in which lines  800  run in two distinct directions, resulting in singular values σ 1  and σ 2  that are equal, thus the orientation of the principal singular vector has zero significance. 
         [0040]    Distinguishing subimages that are non-oriented from subimages that are oriented by such a certainty filter, can allow the Hough line filter to be more efficient by filtering out subimages with low orientation significance and directing the Hough filter to operate only in the orientation of the subimage. 
         [0041]    In one embodiment, the Hough line filter  630  results in a number of lines detected in the subimage, and a subimage may be marked as containing a barcode if the number of lines meets or exceeds a threshold value (for example, more than 3 lines). 
         [0042]    The use of a Hough filter is illustrative and exemplary, and other techniques for determining the number of lines in a subimage may be used as desired. 
         [0043]    In one embodiment, the predetermined thresholds used in the density analysis, parallel filter and Hough line filters described above may be based on assumptions about the distance between the camera and objects in the image, as well as the color map of the image. These assumptions and the resulting threshold values may be adaptive to image statistics, such as image intensity or contrast distribution, and may be improved by image enhancement or texture analysis techniques. 
         [0044]    Segmentation 
         [0045]    Having now localized the barcode into subimages, and recognized and oriented the subimages that contain portions of barcodes, the segmentation phase 
         [0046]    Finally, a segmentation phase is employed to provide a cropped and rotated bounding box around a region that contains a barcode. This region typically will contain all of the subimages that have been marked by the recognition phase as containing a portion of a barcode with some extra margins. The bounding box region is then cropped and rotated based on the orientation calculated using singular value decomposition to produce a vertical barcode. In one embodiment, the segmentation phase may create a horizontal-vertical contrast map using the vertical barcode, perform block smoothing on the contrast map, create a colormap of the contrast map, then binarize the colormap to black and white values, and do a morphological closing of the binarized contrast map, to generate a segmented barcode region. Even if the segmented barcode region does not contain the entire barcode, because the information is encoded in one dimension, any full horizontal cross section of the barcode region is sufficient for the barcode decoder. As illustrated in  FIG. 9 , the segmented barcode region  910  may then be extracted from the image as a region with clean edges and provided to a barcode decoder for decoding the information encoded into the barcode. 
         [0047]    By performing localization, recognition and orientation, and segmentation operations on the image, the barcode detection technique described above allows extraction of the region that contains the barcode from the image, rejecting the rest of the image. The detection technique is scale invariant, rotation invariant, and is robust, allowing processing of images with low image quality. 
         [0048]      FIG. 10  is a simplified functional block diagram illustrating a electronic device  FIG. 1000  according to one embodiment that can implement the techniques described above. The electronic device  FIG. 1000  may include a processor  FIG. 1016 , display  FIG. 1020 , microphone  FIG. 1006 , audio/video codecs  FIG. 1002 , speaker  FIG. 1004 , communications circuitry  FIG. 1010 , an image sensor with associated camera hardware  FIG. 1008  for performing image capture, user interface  FIG. 1018 , memory  FIG. 1012 , storage device  FIG. 1014 , and communications bus  FIG. 1022 . Processor  FIG. 1016  may be any suitable programmable control device and may control the operation of many functions, such as the generation and/or processing of image data, as well as other functions performed by electronic device  FIG. 1000 . Processor  FIG. 1016  may drive display  FIG. 1020  and may receive user inputs from the user interface  FIG. 1018 . An embedded processor provides a versatile and robust programmable control device that may be utilized for carrying out the disclosed techniques. 
         [0049]    Storage device  FIG. 1014  may store media (e.g., image and video files), software (e.g., for implementing various functions on device  FIG. 1000 ), preference information, device profile information, and any other suitable data. Storage device  FIG. 1014  may include one more storage mediums for tangibly recording image data and program instructions, including for example, a hard-drive, permanent memory such as ROM, semi-permanent memory such as RAM, or cache. Program instructions may comprise a software implementation encoded in any desired language (e.g., C or C++). 
         [0050]    Memory  FIG. 1012  may include one or more different types of memory which may be used for performing device functions. For example, memory  FIG. 1012  may include cache, ROM, and/or RAM. Communications bus  FIG. 1022  may provide a data transfer path for transferring data to, from, or between at least storage device  FIG. 1014 , memory  FIG. 1012 , and processor  FIG. 1016 . Although referred to as a bus, communications bus  FIG. 1022  is not limited to any specific data transfer technology. User interface  FIG. 1018  may allow a user to interact with the electronic device  FIG. 1000 . For example, the user interface  FIG. 1018  can take a variety of forms, such as a button, keypad, dial, a click wheel, or a touch screen. 
         [0051]    In one embodiment, the electronic device  FIG. 1000  may be an electronic device capable of processing and displaying media, such as image and video files. For example, the electronic device  FIG. 1000  may be a device such as such a mobile phone, personal data assistant (PDA), portable music player, monitor, television, laptop, desktop, and tablet computer, or other suitable personal device. In other embodiments, the electronic device  FIG. 1000  may be dedicated to the barcode scanning functionality. 
         [0052]    It is to be understood that the above description is intended to be illustrative, and not restrictive. For example, the above-described embodiments may be used in combination with each other. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention therefore should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein.”