Image analysis – Image transformation or preprocessing – Image storage or retrieval
Reexamination Certificate
2001-03-23
2004-01-20
Mariam, Daniel (Department: 2623)
Image analysis
Image transformation or preprocessing
Image storage or retrieval
C382S190000, C382S218000, C358S403000, C707S793000
Reexamination Certificate
active
06681060
ABSTRACT:
BACKGROUND
This invention relates to image processing and, more particularly, to the characterization of images.
Image processing refers to the analysis and manipulation of images using a computer. The discipline encompasses a wide array of techniques, including pattern recognition and other image analysis, compression and encoding, such as image transmission, and image construction, to name but a few examples. Image processing is used in diverse fields such as astronomy, medicine, military operations, communications, geology, meteorology and so on.
Although visual in nature, images may be characterized so that a computer or other processor-based system may also “see” the image or, for example, distinguish the image from other images. Identifying the moment, entropy, center of mass, orientation, or other histogram-based features is one approach to image characterization. Structural and syntactic features of the image may serve as characteristic parameters. Geometric indicia, such as perimeter, area, eccentricity, or Euler number, may numerically identify the image.
Because images are often perturbed, such as during transmission, the characteristic parameters of the image are ideally invariant following transformations. Image transformations may include translation, rotation, scaling, shearing, compression, inclusion of noise, and so on.
Particularly in the emerging domain of Internet technology, image searching may be an important tool. Different characteristics of the images, used for image searching, may have widely varying results, however. This type of image searching is known as content-based image retrieval.
One technique for retrieving images from a database of images is to associate each image with a certain number of features. Using feature vectors, each image, represented by its feature values, is mapped to a point in an n-dimensional feature space, where there are n features identified. A similarity query is then used to retrieve the images lying in a neighborhood close to the query image.
In executing these similarity queries, the precision of obtaining the image may be offset by the size of the image database and the time to calculate the various features involved in the similarity query.
Thus, there is a continuing need for optimizing image retrieval methods.
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Acharya Tinku
Bhattacharya Bhargab B.
Bishnu Arijit
Kundu Malay K.
Murthy Chivukula A.
Intel Corporation
Mariam Daniel
Trop Pruner & Hu P.C.
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