US 5,867,594 AGranted

Recognizing dataforms in image areas

AssigneeMetanetics Corporation

InventorRobert Stanley Cymbalski

Life of this patent

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Abstract

Dataforms, such as bar codes and matrix codes, are recognized by analysis of histogram type data derived for a plurality of window sections of an image area. Typically, an image area 512 pixels high by 480 pixels wide is divided into 240 window areas, each including 1024 pixels in a 32 by 32 pixel square. A histogram for a window section overlying a portion of the dataform may typically exhibit two peaks representing the two principal reflective values (black and white, for example) of the elements and spaces of a dataform. A background (quiet zone) window section histogram exhibits one principal peak representing the background value (white, for example). The background window sections and the dataform window sections are assigned to two different groups. One or both groups are then used for one or more of the following: recognizing the presence of a dataform; determining whether the dataform is darker or lighter than the background; and locating the dataform within the image area. Also, a single histogram is used to identify the type of dataform, based upon the histogram signature of the dataform.

Claims23

Dependent claims

A method for evaluating an image and identifying characteristics of a dataform therein, the method comprising the steps of:

(a) sampling a portion of an image comprised of a plurality of pixels, to obtain a plurality of reflective values respectively associated with the plurality of pixels;
(b) determining, for each said portion of the image, the number of occurrences of each reflective value of said plurality of reflective values, wherein the number of occurrences are occurrence data;
(c) categorizing the occurrence data in one of the following groups:
(i) first occurrence data indicative of presence of a first principal reflective value and a second principal reflective value, wherein said first and second principal reflective values have a relatively greater number of occurrences in said portion of the image than other reflective values therein, and
(ii) second occurrence data indicative of presence of a third principal reflective value, wherein said third principal reflective value has a relatively greater number of occurrences in said portion of the image than other reflective values therein;
(d) repeating steps (a), (b) and (c) for one or more portions of the image to provide at least one first occurrence data and at least one second occurrence data;
(e) comparing each of the first and second principal reflective values to the third principal reflective value;
and
(f) determining which of the first and second principal reflective values is most distinct from the third principal reflective value.

6 more elements

Dependencies are inferred from the claim text.

Facts

Application: 08/742,368Filed 1 Nov 1996 · division
Priority: 15 Jun 1994
Granted: 2 Feb 1999
Term ends: Not estimatedThe source records don’t support an estimate.

Classification4 codes

G06V 30/413
Analysis of document content: classification of content, e.g. text, photographs or tables
G06V 10/255
Image preprocessing: detecting or recognising potential candidate objects based on visual cues, e.g. shapes
G06V 30/2247
Of printed characters having additional code marks or containing code marks: characters composed of bars, e.g. CMC-7
G06V
Image and video recognition