Image analysis – Pattern recognition – Classification
Reexamination Certificate
1998-11-10
2001-11-13
Johns, Andrew W. (Department: 2621)
Image analysis
Pattern recognition
Classification
C382S318000, C382S319000
Reexamination Certificate
active
06317516
ABSTRACT:
The invention relates to a learning method for an image analysis system for use in the analysis of an object, wherein the object is compared with a reference object, and comprising the steps of:
a. capturing a video image of the reference object which is represented by a plurality of pixel positions, each pixel having its own identification.
b. dividing parts of the video image into a plurality of subareas which represent a plurality of classes.
The invention moreover relates to uses of the method.
Such a method is known e.g. from EP Patent Application No. 574 831. The method of this European publication comprises capturing a video image of an object and defining image areas of the video image on a video screen. Each of these areas is assigned to a specific class. Once the classes have been determined, an iterative process is initiated, comprising mapping the entire video image by means of the selected classes. In other words, the areas of the video image which have not been selected for a specific class, will be assigned to a class which depends on the colour value which the current pixel in the video image has. The method of this document forms histograms of colour occurrences for the classes and weighs the histograms with their probability of occurrence before assigning a pixel value to a cluster according to the intersection of the histograms.
Further, an image analysis system for classification of plants is known from U.S. Pat. No. 5,253,302. In this known system colour classes are formed, in which it is intended that each class contains representatives for variations within the same colour and as few representatives of other colours as possible. The document does not address the problem of overlapping between classes, as each colour class is formed in its own space. This implies eg that pixels to be classified, and which are located in a gap between two predefined classes and maybe are related to one or both classes, will not be assigned to any of these classes, but to the background class. Further, the principle from this US document can not easily be generalized to work simultaneously with more than two classes, because the thresholding only works between two classes (“class” and “not class”=background). Therefore, this prior art will not be very useful where more complicated colour combinations are to be analyzed.
An object of the invention is to provide a method of the type stated in the introductory portion of claim
1
, in which complicated patterns can be analyzed and in which pixel identifications (such as colour) are assigned to previously defined classes for the entire image face.
The object of the invention is achieved by:
c. setting up a user-defined table of classes, where a plurality of identifications is assigned to each class in the table, each class in the able being formed by the user by his selection of a plurality of pixels in the reference image,
d. setting up a special zero class in the table which contains pixel identifications which have not been assigned to any class by the user, and
e. setting up a special conflict class which contains the pixel identifications which have been assigned to more than one class by the user.
f. assigning all pixels belonging to the zero class or the conflict class to the class having a pixel identification which is closest to the identification of the pixel concerned.
It is ensured in this manner that the teaching of the image analysis system can take place with a low process time, while currently monitoring whether the classes of the class table are selected expediently. Further, the entire image face will receive pixel values which belong to one of the selected classes, so that the entire image has now been converted into an image which is composed of the symbolic colour values occurring for the selected classes.
It is noted in this connection that the expression “which is closest to” should be interpreted in view of the selected pixel identification. If, e.g., this identification is expressed as a set of parameters in the form of a vector, the expression “which is closest to” may be interpreted as the nearest vector distance.
When, as stated in claim
2
, the assignment takes place in several stages, each stage comprising processing pixel identifications which adjoin pixel identifications which have already been assigned to a class, it is ensured that the colour composition of the “taught” system has colour change areas which correspond to the original image, but now with more distinct details. Thus, the teaching of the system takes place by adding a growth to the original image, an identification being allocated to all pixels in the image.
For the purpose of using the system for analysis proper, i.e. use of the system after learning, it is an advantage if several reference images are used in the learning, since this allows provision of an image which is representative of an average of objects to be analyzed.
Owing to the flexibility of the image analysis system in the learning it is an advantage, as stated in claim
5
, that a user-selected class may be suppressed, which may be desirable if too many items of colour information have been allocated to the selected class.
Other expedient embodiments of the invention are defined in the remaining dependent claims.
As mentioned, the invention also relates to uses of the method. These uses are defined in claims
9
-
14
.
Several of these uses contain very complicated image compositions, but thanks to the very flexible class table structure according to the invention it is possible to provide extremely useful analyses which have been impossible to perform till now without using much more complicated analysis systems.
REFERENCES:
patent: 3705383 (1972-12-01), Frayer
patent: 3824393 (1974-07-01), Brain
patent: 5253302 (1993-10-01), Massen
patent: 5627908 (1997-05-01), Lee et al.
patent: 574 831 (1993-12-01), None
1993, Gonzales, Rafael C., “Image Segmentation”,Digital Image Processing,pp. 458-462.
Locht Peter
Mikkelsen Peter
Thomsen Knud
Azarian Seyed
Johns Andrew W.
Merchant & Gould P.C.
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