Intermec IP Corporation

Intermec, Inc.

Koninklijke Philips Corporation N.v.

Lingnan Liu

Mark Yukio Shimizu

Lars Jonas Olsson

Lisa M Vartanian

An ultrasound imaging system acquires a sequence of partially overlapping image frames, and stores data corresponding to the image frames in a image frame buffer. The relative spatial positions of the image frames are determined by a correlator, and the resulting spatial position data are then stored in an extended image memory and a history buffer along with data corresponding to the image frames. The data stored in the extended image memory and the history buffer can then be used to generate a panoramic image, which is displayed by the imaging system. Significantly, the data corresponding to the image frames are retained in the image frame buffer after the panoramic image has been displayed so that specific image frames can be excluded from the data corresponding to the panoramic image to either edit defective image frames from the panoramic display or to trim the boundaries of the panoramic display.

Editing and trimming apparatus and method for extended field of view ultrasonic imaging systems

Extended field of view (panoramic) ultrasonic images can exhibit dimensional distortion due to the interplay of the direction and speed of scanhead motion and the direction and rate of beam scanning. This distortion can result in measurement inaccuracies when making measurements of anatomy or distances in the panoramic image. This distortional inaccuracy is compensated by adjusting the alignment of elemental images as they are aligned to produce the panoramic images in consideration of the estimated amount of distortion. The technique can be applied to either linear or curved array transducers and to linear or sector scan formats.

Correction of extended field of view images for distortion due to scanhead motion

A method and apparatus for locating geometric shapes or edges thereof in data collection symbols initially samples and stores an image of light reflected from the symbol. Thereafter, two routines are performed. A first routine performs low level vision processing by identifying linked points along edges, lines, curves or within shapes. At least one of three distortion compensating subroutines compensate for blocking, breaking, gaps or other severe distortions affecting shapes in the stored image. Under a first subroutine, an edge of a shape is tracked until a stopping condition occurs, at which point the shape is crossed so as to continue tracking on an opposite edge. Under a second subroutine, previously collected linear curve information is used to identify a jump point to continue tracking following a stopping condition. Under a third subroutine, mathematical morphology techniques are employed to close gaps or move blockage in shapes in the stored image. Coordinates of linked points, which represent lines, curves and other geometric shapes, from the first routine are then employed by a second routine which identifies patterns within the identified lines/curves. Based on these identified patterns, types of symbols from various symbologies can be identified and located within the stored image.

Non-linear image processing and automatic discriminating method and

A method and apparatus for locating geometric shapes or edges thereof in data collection symbols initially samples and stores an image of light reflected from the symbol. Thereafter, two routines are performed. A first routine performs low level vision processing by identifying linked points along edges, lines, curves or within shapes. A rapid pixel linking subroutine identifies edge pixels in symbol images lacking distortion. If the edge or shape suffers from distortions, then one or more distortion compensating subroutines locate sequential linked pixels despite such distortions. The resulting coordinates of linked points, which represent lines, curves and other geometric shapes, are then employed by a second routine which identifies patterns within the identified lines/curves. Based on these identified patterns, types of symbols from various symbologies can be identified and located within the stored image.

Method and apparatus for automatic discriminating and locating patterns

A method and apparatus employs a production system-like rule based routine to process signals, such as reflectance signals produced from machine-readable symbols (e.g., bar code symbols). Relevant features data is extracted from each peak or valley in the signal, such as width, height, and relative size. The routine then uses the extracted data for each peak and valley with a set of rules to refine the reflectance signals. Under one or more iterations, the routine selects and resolves conflicting rules and applies resulting rules to process the signal. The processed signal can then be more readily decoded. The routine preferably processes a reflectance signal in portions, such as on a codeword-by-codeword basis.

Rule based method and apparatus for processing reflectance signals from

A method and apparatus employs a production system-like rule based routine to process signals, such as reflectance signals produced from machine-readable symbols (e.g., bar code symbols). Relevant features data is extracted from each peak or valley in the signal, such as width, height, and relative size. The routine then uses the extracted data for each peak and valley with a set of rules to refine the reflectance signals. Under one or more iterations, the routine selects and resolves conflicting rules and applies resulting rules to process the signal. The processed signal can then be more readily decoded. The routine preferably processes a reflectance signal in portions, such as on a codeword-by-codeword basis. Under an alternative embodiment, a fuzzy inference engine provides sets of values for height, width and relative size. Therefrom rules are applied to determine a size of each peak and valley in the signal.

Rule based and fuzzy logic method and apparatus for processing

A method and apparatus for reading data collection symbols initially samples and preferably stores an image of light reflected from the symbol. An initial portion of the symbol in the stored image is located and multiple reading techniques are performed based on the stored symbol image and the located portion. Several output signals are produced from the multiple reading techniques, and one of the output signals is selected as a decoded signal that represents the data encoded in the symbol. The multiple reading techniques can be multiple reading methods performed substantially simultaneously with each other (on one or more processors), a single method performed multiple times and at multiple locations within the stored symbol image, or each reading method can be broken down into constituent modules, and like modules grouped into sets.

Method and apparatus for reading machine-readable symbols by employing a

A method and apparatus locates a periphery or 'bounding box' of an image of a symbol located within an image stored by a reader. The symbol preferably has a recognition pattern with an longitudinally extending bar and at least one vertically extending reference bar, such as in the Code One symbology. The method locates the positions of landmark points within the stored image. Landmark points include points at the end of the longitudinally extending bar, points at the corner or root formed between the longitudinally extending bar and the vertically extending bar, and points at the ends of the vertically extending bars. Based on the locations of the landmark points, lines are projected between pairs of the landmark points to determine an amount, if any, of optical distortion to which the image of the symbol suffers. Based on the landmark points, lines projected therebetween and the angles of any distortion, one or more bounding boxes are constructed to determine the periphery of the symbol within the stored image.

Method and apparatus for locating data regions in stored images of

A method and apparatus for reading a distorted image of data collection symbol within a reader image generated and stored by a two-dimensional symbology reader begins by first locating a starting point in the distorted symbol image within the stored reader image. The method identifies edge contours of some or all of the bars and spaces in the symbol. The method then identifies one or more points within each of the identified edges. Thereafter, the method defines one or more sampling paths or lines that extend through the symbol, and through the points such that each line is constructed in a 'connect-the-dot' fashion. The bars and spaces can then be sampled based on one or more of the defined lines, and the information in the symbol decoded from the sampled bars and spaces.

Method and apparatus for reading machine-readable symbols having surface

A method and apparatus locates multiple 'bounding boxes' within an image of a symbol located within an image stored by a reader. The symbol preferably has a recognition pattern with an longitudinally extending bar and at least one vertically extending reference bar, such as in the Code One symbology. The method locates the positions of landmark points within the stored image. Landmark points include points at the end of the longitudinally extending bar, points at the corner or root formed between the longitudinally extending bar and the vertically extending bar, points at the ends of the vertically extending bars, and points at the intersection between the bars and blocks that extend transversely therefrom. Based on the locations of the landmark points, horizontal and vertical line functions are defined between collinear landmark points. Based on the intersection of the line functions, multiple bounding boxes are constructed to determine the periphery and all data areas of the symbol within the stored image.

Method and apparatus for accurately locating data regions in stored

A bar code imaging system capable of accurately and efficiently reading a printed bar code that is partially damaged, distorted, or erased. The bar code imaging system comprises an imaging element adapted to receive light reflected from a bar code symbol and provide a two-dimensional image of the bar code symbol. The two-dimensional image is decoded into data representative of the bar code symbol. More particularly, code words in the bar code pattern are decoded along a path beginning at the start of the bar code and moving toward the end of the bar code. As each code word is decoded, the decoded data is stored in a memory. When an invalid code word is encountered, decoding continues along a path beginning at the end of the bar code and moving toward the beginning of the bar code, and the decoded data is stored in the memory with the previously decoded data. When an invalid code word is encountered, the scan path is moved, and decoding continues along the new scan path beginning at the last valid code word decoded while moving toward the end of the bar code.

Problem reduction with low level information integration in bar code

An electro-optical imaging system that utilizes an efficient search algorithm to find a coded symbol within a digitized image is provided. A digitized image of an area of an object having a coded symbol attached or printed thereon is created and stored in an image memory. A search algorithm is then activated to search for the coded symbol within the digitized image stored in the image memory. The search is limited to an area of the digitized image that is defined by boundary parameters. The search begins with the scan of an initial horizontal line of pixels that passes through the center of the search area, and an initial vertical line of pixels that also passes through the center of the search area. Subsequent horizontal and vertical lines of pixels are then scanned, one each subsequent line is located an integer multiple number of step sizes from one of the initial lines. Once the above described search pattern expands beyond the boundaries of the search area, the step sizes are reduced, and the search is continued.

Prioritized searching methods for finding a coded symbol in a digitized

A method and apparatus for locating and decoding machine-readable symbols such as Data Matrix symbols begins by storing an image of the symbol. The invention locates an edge point between the quiet zone and a solid boundary of the finder pattern for the symbol. Therefrom, the present invention locates at least portions of first and second solid boundaries, and preferably a solid corner therebetween. Thereafter, the present invention locates at least portions of dashed boundaries extending from the free ends of the solid boundaries. After locating the solid and dashed boundaries of the finder pattern for the symbol, the present invention determines the periphery and version of the symbol, and then applies the appropriate decode routine to decode the image of the symbol.

Method and apparatus for locating and decoding machine-readable symbols,

A method and apparatus for estimating an X-dimension of a machine-readable symbol having a recognition pattern that includes elements comprising bars and spaces. The method and apparatus proceed from a boundary between two of the elements of the machine-readable symbol. The method and apparatus selects a plurality of sampling paths across a particular one of the elements at the boundary, and determines a median path length across the particular one of the elements out of each of the plurality of sampling paths. The selection of a plurality of sampling paths and the determination of the median path length are repeated for each of the elements of the recognition pattern. The method and apparatus then averages the median path length for all the elements to provide the estimated X-dimension of the machine-readable symbol.

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Mill Creek, WA, United States of America

Lingnan Liu