Abstract:
Methods and apparatuses are disclosed for identifying regions of similar texture in an image. The areas of similar texture include areas conventionally thought of as similar texture regions as well as areas of more varied texture that are treated as regions of similar texture in order to identify them within an image. The method associates frequency characteristics of an image with a spatial position within the image by: applying a frequency analysis on sub-regions of the image, thereby, generating frequency characteristics representative of the sub-regions: and associating the frequency characteristics with the origin of the sub-regions. An embodiment disclosed applies a fast Fourier transform on sub-regions in a given direction to determine a dominant frequency of the sub-region and the power of the dominant frequency, both of which are associated with the respective sub-region by storing the dominant frequency and power in a frequency image and power image, respectively, at the position of the origin. Thereafter, the frequency image and the power image are segmented to generate binary images containing regions having similar frequencies and powers, respectively. The binary images are then logically anded together to further refine the regions possessing similar frequency, and thereby finding regions having similar texture in an image.

Description:
This application claims the benefit of U.S. Provisional Application No. 60/147,646, filed Aug. 6, 1999. 
    
    
     FIELD OF THE INVENTION 
     This invention relates to machine vision, and particularly to methods and apparatuses for processing images. 
     BACKGROUND 
     Many machine-vision applications identify regions of images and process the image data within the regions instead of processing the entire image. Regions are segmented using many different vision tools. For instance, growing a region, applying a connectivity analysis, or applying a boundary-tracking algorithm segments regions from an image. The optimal vision tool for a given application depends upon the region being identified and the imaging environment present. 
     As known in the art, segmenting a region is a difficult machine-vision task. For example, segmenting leads within an image of a leaded device is difficult, where a leaded device is an electronic component that has a device body and metal leads. Leaded devices include surface-mount components and through-hole components, for example. One way to segment the leads is to binarize the image of the leaded device. Binarizing an image is a technique that chooses a threshold value to segment the image into foreground objects and background. Typically, one intensity, such as white, denotes the leads, and the other intensity, such as black, denotes the image background (the background and the device body). One of the short falls of the binarization technique is the inability of a single threshold value to segment the entire lead from the image background and the device body. The leads have specularly reflecting surfaces that frustrate identifying an appropriate threshold value to segment the leads within a front-lit image of the leaded device. The leads also cannot be segmented with one threshold value from within back-lit images, because in back-lit images the leads and the device body have substantially the same grey-scale value. Therefore, no threshold value exists that segments the entire lead relative to the body and background. Thus, the binarization method is not an optimal solution to segment leads. 
     The same shortfalls arise when trying to segment balls of a ball grid array (BGA) device, where a BGA device is a surface mount device consisting of a approximately rectangular body package and a grid of metal balls. 
     Therefore, binarization is typically combined with other techniques, such as morphology, for example, to segment leads or balls. Morphology works best when features in the image that belong together are closest together, because the closest features become one region after applying morphology. When leads are imaged, the specularly reflecting surfaces typically cause opposed ends of the leads to appear as bright features in the image, while other areas of the lead remain unclear in the image. The features closest together are the ends of adjacent leads. Therefore, applying morphology produces a region containing the ends of the adjacent leads, which, unfortunately, is not the region desired to be segmented. Therefore, binarization combined with morphology is also not an optimal solution to segment leads. 
     A region can also be segmented by capitalizing on its inherent properties, such as the texture of the region. Typically, textured regions are segmented using nth-order statistics or textons. Nth-order statistics segment regions that have large enough statistical differences. Therefore, only significantly different regions are segregated by nth order statistics. Further, applications applying nth-ordered statistics segmenting suffer the same problems as other segmentation algorithms: the algorithm has to choose the correct measure (e.g. the correct statistics) to properly identify the right local area. Alternatively, textons, which are local profiles, are used to find textured regions in an image. Textons, however, cannot easily pick up lower frequency texture. 
     SUMMARY 
     Methods and apparatuses are disclosed for identifying regions of similar texture in an image. 
     More particularly, the method acquires image data representing the at least one input image and divides at least a portion of the image data into sub-regions, where each of the sub-regions has an origin. 
     The frequency characteristic(s) for the sub-regions are determined by applying a frequency analysis, such as applying a Fourier transform in one or more orientations. The frequency characteristic(s) of each sub-regions at each orientation is associated with the origin of each of the sub-regions, and thus, with the spatial position of the sub-region within the image. 
     Then, the frequency characteristic(s) of each of the sub-regions is examined to identify similar sub-regions, thereby identifying regions of similar texture in the input image. 
     In one embodiment, the frequency characteristic is examined by first representing the frequency characteristic of each of the sub-regions as a value on a frequency-characteristic image at the respective origin of each of the sub-regions and segmenting the similar sub-regions within the frequency-characteristic image using the values of the sub-regions. The regions within the frequency characteristic image are regions of similar texture, but the regions can have different texture from one another. 
     The invention recognizes, among others, that regions of similar texture display at least one similar frequency characteristic that can be used to segment the regions of similar texture. 
     Further, the invention recognizes, among others, that the frequency characteristic(s) of an image can be associated with the respective spatial position within the image. Specifically, the invention applies a frequency analysis on a sub-region of the image to generate a frequency characteristic(s) and associates that frequency characteristic(s) with the origin of the sub-region. Using the spatial positions of the frequency characteristics, the invention identifies regions of similar texture in an image. 
     In another embodiment, more than one frequency characteristic is used to identify the regions. Specifically, each frequency characteristic, for the sub-regions, is stored as an image, as hereinbefore described. Then, the images are combined. In one embodiment, the images are combined by logical anding, which only maintains portions of a region that are within every one of the frequency characteristic images. Thus, the boundary of the region is refined. 
     In another embodiment, more than one frequency characteristic is also used, but additionally the frequency characteristics images are thresholded to create binary images. Thereafter, at least one of the binary images for one of the frequency characteristics is combined with one of the binary images for another one of the frequency characteristics to again refine the regions of similar texture. In a preferred embodiment, all variations of combining the binary images are performed. Further, in a preferred embodiment, the binary images are combined by logically anding the binary images. 
     In further aspects, the invention also recognizes that leads of a leaded device or balls of a BGA device will produce a homogeneous frequency, and therefore, can be segmented from an image as described herein. 
     The invention is particularly useful for segmenting lead sets from an image of a leaded device or segmenting ball regions from an image of a BGA device. 
     Among other advantages, the invention can segment regions having textures that are typically not classified as similar textures. 
     The invention solves some of the problems of the prior art, including but not limited to, segmenting lead sets and balls from an image, segmenting regions that do not have an easily identifiable threshold value, but that exhibit similar frequency characteristics, and segmenting low frequency elements using a form of texture segmentation. 
     In further aspects, the invention provides apparatuses in accord with the methods described above. The aforementioned and other aspects of the invention are evident in the drawings and in the description that follows. 
    
    
     BRIEF DESCRIPTION OF DRAWINGS 
     The invention will be more fully understood from the following detailed description, in conjunction with the accompanying figures, wherein: 
     FIG. 1 depicts a machine vision system for practice of the invention; 
     FIG. 2 depicts region(s) in images that exhibit similar texture; 
     FIG. 3 is a flowchart summarizing operation of an embodiment of a method according to the invention that identifies regions of similar texture in an image; 
     FIG. 4 depicts various examples of sub-regions into which an image can be divided; 
     FIG. 5 depicts the operation of determining a frequency characteristic of one sub-region for one embodiment of the method of FIG.  3 . Also shown are three aspects of a spatial frequency characteristic, and instances of how to store the three aspects; 
     FIGS. 6A-6D are flowcharts detailing operation of four embodiments of the examining-the-sub-regions phase given the frequency characteristic(s) of FIG. 3; 
     FIG. 7 depicts an instance of the operation of examining-the sub-regions phase of FIG. 3; 
     FIG. 8 illustrates an example of a region that lends itself to being identified using the entire frequency spectrum characteristic; 
     FIG. 9 is a flowchart summarizing the operation of an embodiment of a method according to the invention that identifies regions of similar texture in an image; 
     FIG. 10 depicts examples of sub-regions that may be used when an orientation of the region is unknown; 
     FIG. 11 shows three instances of how to store the frequency characteristic of angle; and 
     FIG. 12 depicts a histogram of the bar code of FIG.  2 . 
    
    
     DETAILED DESCRIPTION 
     The methods and apparatuses identify at least one region having similar texture in an image. The method is particularly useful for locating leads of a leaded device. Though this is the form of a preferred embodiment, this embodiment should be considered illustrative, and not restrictive. 
     FIG. 1 illustrates a machine system  110  of the type in which the invention is practiced. The system  110  includes a capturing device  116 , such as a conventional video camera or scanner that generates an image of an object  112 . Image data (or pixels) generated by the capturing device  116  represent, in the conventional manner, the image intensity (e.g. color or brightness) of each point in the scene at the resolution of the capturing device  116 . 
     The digital image data is transmitted from the capturing device  116  via a communications path  118  to an image analysis system  120 . This can be a conventional digital data processor, or a vision processing system of the type commercially available, for example, from the assignee hereof, Cognex Corporation, programmed in accord with the teachings hereof to identify regions having similar texture from the image data. 
     The image analysis system  120  may have one or more central processing units  122 , main memory  124 , input-output system  126 , and one or more disc drives (or other mass storage device)  128 , all of the conventional type. 
     The system  120  and, more particularly, central processing unit  122 , is configured by programming instructions according to the teaching hereof to identify regions of similar texture in the image, as described in further detail below. Those skilled in the art will appreciate that, in addition to implementation on a programmable digital data processor, the methods and apparatuses taught herein can be implemented in special purpose hardware. 
     As known in the art, many different objects and features have texture, such as a screen, for example. The objects  202  and  204 , depicted in FIG. 2, not drawn to scale, have features (i.e., the concentric outlines) that have similar texture along the radial direction. The blinds  206  also exhibit similar texture. Substantially defect-free areas of a web of material (not shown) also exhibit a similar texture, where the defective areas exhibit a texture different from that of the background (i.e., the majority of the web of material). 
     The invention can identify regions of similar texture from within regions of non-similar texture. For example,. the portions of the print strokes of the logo  228  that share the direction  230  create a region of similar texture. This region is identified although it is among portions of the print strokes that are in a different direction than the texture. The entire object does not have to possess similar texture to be identified using the invention. The invention can identify one portion of an object having a similar texture, where the portion can be segregated in one area of the object or throughout the object. 
     It should also be apparent that multiple objects and not just features of objects can create the texture. 
     Balls  212  of a BGA device  218 , wide leads  208 , and thin leads  210  of a leaded device  216  also create regions of similar texture, that are identified from within the image, even though the images have backgrounds  220  and  222  with a faint texture from the partially occluded circular object  224  and  226 , respectively. Typically, the size of, and/or the spacing between, leads and/or groups of leads or balls is substantially uniform, where the degree of uniformity is known in the art. The uniformity produces regions of similar texture. 
     Objects without uniform size and/or spacing between features or one another also have regions of similar texture, such as the bar code  232 , as described more fully with respect to FIG.  3 . The bar code  232  exhibits a similar texture that is identified and used to segment the entire bar code  232  as a region within an image. The bar code features  234  have different widths, and may not in the traditional sense have similar texture, but as used herein similar texture includes objects and features that can be identified using the methods and apparatuses described herein. 
     Turning to FIG. 3, FIG. 3 is a flowchart summarizing operation of an embodiment of a method according to the invention that identifies regions of similar texture in an image, where steps of the method will be denoted in the detailed description in parentheses. The first step is to acquire an image ( 300 ). Next, at least a portion of the image is divided into sub-regions ( 302 ), which are analyzed for frequency characteristic(s) ( 304 ), as hereinafter described. 
     The analysis associates one or more frequency characteristics with the origin of each sub-region. For example, as shown in FIG. 4, not drawn to scale, the frequency characteristic(s) of the image within each of the sub-regions  402  is associated with the position of the sub-regions origin denoted by an “X” in the image  400 , where like numerals designate like elements. 
     Preferably, the sub-regions, and therefore, the origins thereof, are spaced at regular intervals to facilitate reconstructing a representation of the image using the frequency characteristic(s) measured at each origin. Image  410  depicts an example of the BGA  218  having sub-regions  412  spaced at regular intervals, and image  400  shows an example of the leaded device  216  having sub-regions  422  spaced at regular intervals. In a preferred embodiment, the sub-regions are spaced such that the centers of neighboring sub-regions correspond to neighboring pixels in the image (i.e., every pixel location in the image is an origin for a sub-region). As should be apparent to one skilled in the art, when the sub-regions are not spaced at regular intervals, a map of the spacing used is kept from which the spatial relationship of the sub-regions and the associated frequency characteristic(s) are later reconstructed. 
     All the sub-regions within one given image do not have to be the same shape, as shown in image  430 , which has square sub-regions  432  and rectangular sub-regions  434 . The sub-regions can have various shapes, such as the circular sub-regions  422  of image  420 , the square sub-regions  432  of image  430 , or the rectangular sub-regions  402  and  412  of images  400  and  410 , respectively, for example. 
     The optimum size of the sub-regions is application specific. If the sub-region is too large or too small for the image, the frequency characteristic(s) generated therefrom fail to appropriately represent the sub-region. For instance, if the sub-region is too small, frequency characteristics may be missed due to aliasing affects. 
     An example of the effect of sub-region size on the analysis is illustrated using the bar code  232  of FIG. 2. A frequency spectrum of the bar code  232  produces multiple peaks, including, at least, a peak at the frequency associated with the thickness of the bar features  234 , multiple peaks for the frequency associated with the spacing between the bars features  234 , and a larger peak for the dominant frequency of the background. For smaller sub-regions, the strength of the frequency (i.e. the magnitude of the peak) due to one of the features  234  or a single spacing between features  234  is prominent. For larger sub-regions (approximately the size of the bar code  232 ), it is less likely multiple prominent peaks are present. Instead, a larger number of peaks of similar magnitude are present, which represent the various spacings and various widths of the features  234 . The presence of prominent peaks, or lack thereof, aids identifying and segmenting of the entire bar code  232  and/or features  234  thereon. 
     Typically, the sub-regions overlay various features in the image, therefore, the frequency characteristic(s) calculated as hereinafter described, incorporate, in part, the frequency characteristic(s) of the neighboring region(s). 
     Returning to FIG. 3, optionally, the size of the sub-regions can be changed in an iterative process, denoted by path ( 308 ) to decrease the overlay or to save processing time, for example. For the first iteration, the image is divided into large sub-regions ( 302 ), the frequency characteristics of the large sub-regions are determined ( 304 ), and then intermediate regions of similar texture are hypothesized by examining the frequency characteristics from the large sub-regions ( 306 ). The frequency characteristics generated from the larger sub-regions, however, are coarse, and may not appropriately represent the sub-regions. Therefore, a more optimum size for the sub-regions is chosen using the intermediate regions, and the process is repeated ( 308 ) at the new smaller sub-region size. 
     The optimum size of a sub-region for each part of the image can differ. For instance, in large areas of the image that have substantially homogenous frequencies, analyzing larger sub-regions is better for processing efficiency and data requirements. Choosing an appropriate size to perform local operations is a common problem in image analysis. The solution is often provided by a pyramid scheme, such as the iteration procedure hereinbefore described. Other pyramid schemes, known in the art, can also be used without departing from the scope of the invention. 
     After the sub-regions are created, one or more frequency characteristic(s) are determined for each sub-region ( 304 ), where the frequency characteristics for a sub-region can include the dominant frequency, the power, the frequency spectrum, or the orientation of the analysis that produced the dominant frequency, for example. It should be apparent that any given sub-region can lack a given frequency characteristic. 
     In a preferred embodiment, the frequency analysis is conducted, within each sub-region, along a dominant orientation ( 304 ), such as the direction of arrow  414 , which is aligned with the length of the rectangular sub-region  412 , or the direction of the arrow  424 , which is along the circumference of circular sub-region  422  in FIG.  4 . In a preferred embodiment, the dominant orientation is generated using the methods and apparatuses described in co-pending U.S. Provisional Application, Serial No. 60/147,721 entitled, “Methods and Apparatuses For Determining the Orientation of at Least One Feature in an Image,” which Provisional Application is filed on Aug. 6, 1999 in the names of Venkat Gopalakrishnan, Ivan Bachelder, and Albert Montillo, and which Provisional Application is hereby expressly incorporated by reference in its entirety. 
     The image data within the sub-region is input to a frequency algorithm, such as a Fourier series, Fourier transform, fast Fourier transform, Z transform, Laplace transform, or Taylor series, for example. An example of applying a one-dimensional fast Fourier transform (“1D FFT”) along the length  502  of a sub-region  504  of an input image  500  of the leaded device  216  is shown in FIG. 5, not drawn to scale, where like numerals represent like elements. The input image  500  is represented as I(x, y). The sub-region  504  has an origin  510  positioned at I( 20 , 40 ) in the input image  500 . The 1D FFT of sub-region  504  generates a frequency spectrum  506 , represented mathematically as FS(F n ), shown on graph  508 . The magnitude of the frequency is represented on the X-axis and the power represented on the Y-axis. 
     Next, a frequency characteristic(s) is associated with the origin  510 , where the frequency characteristic is, for example, the maximum power  514  or the dominant frequency  512  of the sub-region  504 , where the dominant frequency as used here means the magnitude of the frequency having the highest power in the frequency spectrum for that sub-region. Consequently, the frequency characteristic(s) for each sub-region is mapped into one point, or vector, that retains the spatial position of the sub-region. 
     The invention recognizes that frequency characteristic(s) of an image can be associated with their spatial position within the image by applying a frequency analysis on a sub-region of the image to generate a frequency characteristic(s) and associating that frequency characteristic(s) with the origin of the sub-region. 
     It should be apparent that any operation on the sub-region that generated a representation of the frequency of the sub-region can be used without departing from the scope of the invention, such as wavelet analysis, for example. 
     One or more frequency characteristic(s) are stored for each sub-region or for selected sub-regions. 
     The frequency characteristic(s) are preferably stored in an image(s), but can be stored in a data structure or other manner known in the art. For example, the dominant frequency  512  of sub-region  504  is stored in a frequency image  516 , represented mathematically as F(x,y), at the same coordinates ( 20 , 40 ) as the origin  510  of the sub-region  504 . It is preferred that the grey values in the frequency image  516  are mapped so that the low grey values represent low frequencies and high grey values represent high frequencies. It should be apparent that other mappings can be used, such as using the period, as opposed to the frequency, or mapping the high frequencies using the low grey values, and the low frequencies using the high grey values, for example. The maximum power  514  of the frequency spectrum  506  is also stored in an image, such as a power image  518 , represented mathematically as P(x,y), at the same coordinates ( 20 , 40 ) as the origin  510  of the sub-region  504 . The frequency image  516  and the power image  518  correspond one-to-one to the input image  500 . It should be apparent that other ratios are possible, such as 5-to-1, for example. 
     In another embodiment, the entire frequency spectrum  506  for the sub-region  504  is stored as a vector  520  associated with the origin  510  ( 20 ,  40 ) of the input image  500  in a three-dimensional array depicted schematically in space  522 . Each coefficient {F 1 , F 2 , . . . F n } of the vector  520  is a term of the 1D FFT. 
     In a preferred embodiment, only the mid-range of the frequency spectrum is important for the analysis of the invention, the DC components, including background, and noise are filtered out of the frequency spectrum prior to generation, analysis, or storage, using techniques known in the art. 
     The next step is to examine neighboring sub-regions for at least one similar frequency characteristic ( 306 ), and, thereby, identify regions of similar texture. It should be apparent that neighboring sub-regions include regions that are not adjacent, such as the next closest sub-region, for example. Several methods can be used to identify the regions of similar texture using one or more of the frequency characteristics previously determined (i.e. dominant frequency, power, frequency spectrum, and statistics of regions in a given direction, for example). 
     In one embodiment, regions of similar texture are defined by segmenting neighboring sub-regions with similar dominant frequencies, where similar dominant frequencies are dominant frequencies within a range of frequencies defined for each application. FIG. 6A is a flowchart detailing operation of a one embodiment of the examining neighboring-sub-regions phase using the dominant frequency, where steps of the method are denoted in the detailed description in parentheses. 
     First, the dominant frequency is stored in a frequency characteristic image ( 610 ), as previously described. 
     Next, the frequency characteristic image is segmented ( 612 ) into connected regions, using boundary tracking techniques, connectivity analysis, or other known techniques in the art, for example. 
     In a preferred embodiment, the segmentation is performed by thresholding the frequency characteristic image to form binary images. The threshold is determined using numerous methods known in the art. An example is illustrated in FIG. 7, not drawn to scale, where like numerals represent like elements. The threshold is found by first forming a histogram  700  of the dominant frequency image  516 , simplified for illustration purposes, where the grey-scale value is graphed on the abscissa and the number of pixels having the grey-scale value is represented on the ordinate. Thus, the histogram is a one-dimensional function that represents the grey-scale values in the dominant frequency image. In this example, the histogram  700  of the dominant frequency image  516  has three thresholds, T 1 , T 2 , and T 3 . The thresholds are used to make binary images. More particularly, the first binary image  702 , denoted F 1 (x,y), is created by setting the grey-scale value to zero for all pixels of the dominant frequency image  516 , F(x,y), that have a grey-scale value below T 1 , and setting the grey-scale values of all other pixels to  255 . Similarly, the second binary image  704 , denoted F 2 (x,y), is created by setting the grey-scale value of all pixels of F(x,y)  516  having a grey-scale value above T 1  and below T 2  to zero, and setting the grey-scale value of all other pixels to  255 . A third binary image  706 , denoted F 3 (x,y), is created by setting the grey-scale value of all pixels of F(x,y)  516  having a grey level above T 2  and below T 3  to zero, and setting all other pixel grey-scale values to  255 . The regions remaining in the binary images  702 ,  704 , and  706  are regions of similar texture. Further, the regions within each of the binary images  702 ,  704 , and  706  have similar texture to each other. 
     Next, optionally the binary images  702 , 704 , and  706  are filtered ( 608 ) to remove unwanted features, thereby, further identifying the leads  208  and  210  from other regions of similar texture. Many different filters can be used without departing from the scope of the invention, where the filters use characteristics of wanted and unwanted features to remove extraneous regions from the binary images. For instance, if the background was previously removed by filtering out the DC component, an area threshold value is used to remove a peak for the background (not shown in the histogram  708 ). For example, noise is removed from the binary images  702 ,  704 , and  706  by discarding any regions having an area below an area-threshold value. In addition, regions formed from non-repetitive features, such as nozzles, for example, are optionally removed from the binary images  702 ,  704 , and  706  because they are below an area-threshold value or are a certain shape, for example. 
     Alternately, in one embodiment, regions of similar texture are defined by finding sub-regions with similar frequency spectrums ( 306 ), as further described with reference to FIG. 6B, where the steps of the method will be denoted in the detailed description in parentheses. “Similar frequency spectrum” is defined for each application, where similarity can be defined by pattern recognition techniques, clustering techniques, such as neural networks, or other techniques known in the art. For instance, the value of a standard deviation between the frequency spectrums of two neighboring sub-regions can define similarity. 
     In this embodiment, no frequency characteristic image is created. 
     Instead, the input image is segmented ( 622 ) by comparing the frequency spectrum data of neighboring sub-regions. Frequency spectrums can also be compared for similarity not only between neighboring sub-regions, but also across a group of neighbors, such as four or five neighboring sub-regions. Either an average change in the standard deviation, or the maximum change in the standard deviation, across the group of neighbors, can define similarity. One skilled in the art should realize that measures other than standard deviation could be used when comparing sub-regions, such as gradients between two frequency characteristics or portions thereof, for example, without departing from the scope of the invention. 
     Comparing frequency spectrums between sub-regions is a more stable comparison than the prior example of comparing dominant frequencies using segmentation. It is more stable because the entire frequency spectrum is compared to define a region as opposed to defining a region from comparing one value of the frequency spectrum, the dominant frequency, of each sub-region. Changes in one value, the dominant frequency, may improperly create a new region, while changes in one value in the midst of many-unchanging values in the frequency spectrum, allows the noise to be ignored. FIG. 8 illustrates an example of a region  800  where comparing the frequency spectrums is a preferred method of identifying the region  800 , which has many inconsequential breaks  802 , exaggerated for illustration purposes. 
     Lastly, extraneous regions are optionally removed using filters, as previously described, or by using other vision techniques known in the art ( 608 ). 
     Another embodiment that uses only the power frequency characteristic is described with reference to FIG. 6C, where steps of the method are denoted in the detailed description in parentheses. This embodiment is suited to process an image having one region of similar texture, other than the background, such as in the bar code application. First, the power characteristic(s) are stored in a power image ( 630 ). The region is segmented from the power image ( 632 ) using the connectivity analysis. previously described or techniques known in the art. Preferably, the region is segmented by thresholding, as previously described, and a binary image is created that includes regions of similar power. Optionally, extraneous regions are removed using filters, as previously described, or by using other vision techniques known in the art ( 608 ). 
     Regardless of how the regions are found, typically, the boundaries of the regions are rough because usually the image within the sub-region contains features that create more than one frequency. As illustrated using the leaded device  216  of FIG. 5, as the sub-region  504  is placed more over the wide leads  208  and less over the narrower leads  210 , the dominant frequency peak  512  of the frequency spectrum  506  will shift from peak  512  to a second peak to the right of peak  512 . The shift is not abrupt, but gradual as the sub-region  504  increasingly begins to overlay the wide leads  208 . Therefore, the boundary of the regions for the smaller-width leads  210  and the wider leads  208  is not sharp. 
     In a preferred embodiment, the regions are further refined by using more than one frequency characteristic to define a region as described with reference to FIG. 6D, where steps of the method are denoted in the detailed description in parentheses. The region is identified by combining more than one frequency characteristic image using logic, such as ANDing ( 604 ), where the region is the area that is common to all the combined frequency characteristic images. 
     An example of a preferred embodiment of combining sub-regions is described with continuing reference to FIG.  6 D and with reference to FIG. 7, not drawn to scale, where like numerals designate like elements. First, the frequency characteristic images are stored ( 600 ) and segmented by thresholding to form binary images ( 602 ). In the example, the dominant frequency image  516  is threshold to form binary image(s),  702 ,  704 , and  706 , as previously described. The power image  518  is also thresholded, and creates one binary image  710 . The regions within the binary image  710  all have similar power. The next step is to optionally combine the power image, designated mathematically as P 1 (x,y), and the dominant frequency images F 1 (x,y), F 2 (x,y), and F 3 (x,y) ( 604 ). The images are combined by logically ANDing the power image, P 1 (x,y), with each of the dominant frequency images, F 1 (x,y), F 2 (x,y), and F 3 (x,y), in turn, to created a combined frequency characteristic image, C 1 (x,y), C 2 (x,y), and C 3 (x,y), respectively. C 1 (x,y) has two regions remaining, shown superimposed over the wide leads  208  of the leaded device  216 . C 2 (x,y) has two regions remaining, shown superimposed over the smaller leads  210  of the leaded device  216 . C 3 (x,y) is a null image. 
     The combined images C 1 , C 2 , and C 3  only contain non-null pixels that are present in both the underlying binary power image, P 1 (x,y) and corresponding binary dominant frequency image F 1 (x,y), F 2 (x,y), and F 3 (x,y), respectively. Thus, unrepresentative frequency characteristic values that were present in the frequency characteristic images optimally are removed from the combined images. Thus, the definitions of the regions are refined to be more exact and, therefore, more accurately represent regions that have a similar texture. 
     The background is removed by the area-threshold value ( 608 ) or the filtering of the signal previously described. 
     Optionally, additional unwanted regions are removed using a connectivity analysis or other filtering techniques previously described or known in the art ( 608 ). 
     When thresholding is employed to segment the images, the choice of the refinement of the threshold value determines what values are similar, whether it is a similar dominant frequency, similar power, or other similar frequency characteristic. For instance, to identify the bar code  232  of FIG. 2, a typical histogram of a dominant frequency image is shown in FIG. 12, not drawn to scale. If the threshold values are chosen to create fine distinctions on the grey-scale axis  1200 , such as at grey values  1202 ,  1204 , and  1206 , the lines features  234  of the bar code  232  will not be interpreted as having the same dominant frequency. With this fine thresholding scheme, the line features  234  of the bar code  232  are distinguished from one another. On the other hand, a coarser thresholding scheme, such as the threshold value chosen at the grey value of  1210 , for example, would identify the bar code features  234  as a region having a similar texture, and the bar code region  1212  is segmented from the background region  1208 . The bar code region could then be input into other image processing algorithms. 
     Turning to FIG. 9, which shows an alternate embodiment of the method of the invention that does not require a given direction in which to perform the frequency analysis, where steps of the method are denoted in the detailed description in parentheses. The first step is to acquire data ( 900 ). The data can be in the form of an image, as described earlier, or other data can be processed by the invention, such as a video stream for example. 
     Although the preferred embodiment analyzed frequency over space (i.e. a spatial frequency analysis), the frequency analysis can be made over time, and, thus used to detect movement, or changes, over a sequence of process steps, for example. For instance, regions of a video stream are identified in time, where the background is a region of similar texture having a low dominant frequency over time, and where moving parts create a region of similar texture having higher dominant frequencies over time. Such an implementation should be apparent to one skilled in the art when using the teachings herein and elements known in the art. 
     Next, the data is divided into sub-regions ( 302 ). 
     Next, unlike the previous embodiment, the frequency analysis does not require a known direction ( 904 ). A spatial frequency analysis is conducted using a non-angle dependent method. For example, on FIG. 10, not drawn to scale, a non-angle dependent analysis sums, in the direction of the circumference, the pixels within two-dimensional annular rings  1002  to generate a one-dimensional signal that is input into the 1D FFT ( 904 ). 
     Alternatively, the angle is determined first and then the frequency characteristics are processed at that angle, as previously described ( 904 ) or the angle information is determined for a plurality of angles and combined with the respective frequency characteristics for each angle. For example, the angle(s) are found by conducting the frequency analysis along the length of each rectangular sub-region  1012  in image  1000  of the leaded device  216  at the angles  1004 ,  1006 ,  1008 , and  1010 , for example. The angle  1004 ,  1006 ,  1008 , and  1010  of the sub-region  1012  that generated the maximum power is stored for the origin  1014  of the sub-region  1012 , given here as position ( 20 ,  40 ), where the angle chosen is designated as β. 
     FIG. 11 depicts several ways of storing β. The β for each origin  1014  of the sub-regions is stored directly in an angle image  1104 , denoted {β(i,j)|0&lt;β≦360°}. Alternately, a dx-image  1100  and dy-image  1102  store a component of β, where {dx(i,j)=cos β|−1≦dx≦1} and {dy(i,j)=sin β|−1≦dy≦1}. Alternatively, the angle information  1108  is appended to a vector  1106  containing other frequency characteristic information for each origin, where the vector  1106  can contain the any combination of the frequency characteristics, such as the frequency spectrum, denoted F 1 . F n , the dx and dy angle components, denoted a 1  and a 2 , and other information denoted  0   1 . The vector  1106  is stored, as previously described, in the three-dimensional array  1110  where each position FS(i,j) of the array contains its own vector  1106 . 
     Preferably, the angle data is stored in an image, which is segmented by thresholding to generate binary angle images, each of the binary angle images having regions therein where the angle that produced the maximum power is similar. 
     Next, the other frequency characteristics are generated at one or more of the angles remaining in the binary angle image(s) ( 904 ). 
     Again, several methods can be used to examine the frequency characteristics and identify the sub-regions ( 906 ). For instance, if the image contained two lead sets at different orientations, such as in a four-sided leaded device, the angle image is used alone, without other frequency characteristics, to find:and distinguish these regions, similarly to the embodiment described with reference to FIG. 6A and 6C. Alternatively, the angle image is combined with the frequency characteristics calculated at that angle by logically ANDing the angle image with other frequency characteristic images, similarly to the embodiment previously described with reference to FIG.  6 D. Lastly, any extraneous regions are optionally removed using an appropriate filter ( 608 ), as previously described. It should be apparent that the filtering can occur earlier in the process. 
     It should also be apparent that although an image was used as a preferred embodiment of the operation of examining the neighboring sub-regions, other data formats can be used, such as a data structure, for example. 
     It should be apparent that the dominant frequency can be redefined according to the application to be a frequency having a power characteristic other than the highest or one of the highest power values in the frequency spectrum. 
     It should be apparent that instead of using the same sub-regions for both the angle search and the frequency analysis, larger sub-regions can be used to determine the appropriate angle at which to analyze the remaining frequency characteristics, and then the frequency analysis can be applied at that angle on smaller sub-regions. 
     Those skilled in the art will appreciate that some, or all, of the steps of, dividing, determining at least one frequency characteristic, thresholding, ANDing, and filtering, described hereinbefore, can be combined and effected as hardware implementations, software implementations, or a combination thereof. 
     Additionally, while two or more aligned frequency characteristic images, (e.g. the power image, the dominant frequency image, and/or the angle image(s)) are described herein as being effectively combined by optionally ANDing one to the other, it should be appreciated that analogs of the power, frequency, and angle images, such as filtered or re-mapped images can be processed and combined to yield the combined image. 
     Furthermore, it should be appreciated that any of the images described herein can be subject to further processing, such as by filtering using a gaussian filter, median filter, smoothing filter, morphological filter, or the like known in the art, in order to improve image quality. 
     Although in the methods and apparatuses described hereinbefore, frequency characteristic images are described as logically ANDed with each other, it will be appreciated that other means of combining the images can be effected, such as fuzzy logic, to arrive at a combined image or an image representative of the logically combined images. 
     Those skilled in the art will also realize that processing time can be decreased by using reduced-resolution images or any combination of full-resolution and reduced-resolution images. However, use of reduced-resolution images typically results in a loss of accuracy. 
     Those skilled in the art will realize that processing time can also be decreased by performing any of the computations described herein using sampled data, such as determining frequency characteristics of sub-regions from sampled data, for example. Sampled data is a subset of the available data points, such as every third data point or every third sub-region, for instance. 
     Those skilled in the art will realize that the features do not have to be part of one object but can be separate objects. 
     Although the invention has been shown and described with respect to exemplary embodiments thereof, various other changes, omissions and additions in the form and detail thereof may be made therein without departing from the spirit and scope of the invention. Accordingly, the above description is not intended to limit the invention except as indicated in the following claims.