Image analysis – Image segmentation
Reexamination Certificate
2001-10-11
2004-10-12
Boudreau, Leo (Department: 2621)
Image analysis
Image segmentation
C382S305000, C382S113000
Reexamination Certificate
active
06804394
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates to systems and methods for identifying objects within an image and, more particularly, using experts' knowledge to convert one or more pixels in a hyperspectral image into a 3-D image and for matching the hyperspectral image with predefined images for object recognition purposes.
BACKGROUND OF THE INVENTION
In the field of image processing, it is desirable to improve search and knowledge extraction capabilities in order to facilitate the identification of features therein. One of the factors behind this pressing need is the imminent retirement of data experts who have rich experience and irreplaceable knowledge in information extraction with nuclear test data since the Cold War period. Another factor is the deterioration of the media in which the data is stored to the point that the data in its original form is not comprehensible. The present invention is motivated by the fact that none of the current content based retrieval (CBR) systems can be used to factor data experts' knowledge into the system described in articles in “Storage and Retrieval Image and Video Database I, II, III, IV, and V” (SPIE, 1993 through 1997).
In the physical world, an object is perceived as a two-dimensional and/or a three-dimensional entity that has a certain graytone, color, size and shape. To perceive the existence of such a color/size/shape defined object, the image data set must be processed so that pixels of similar properties can form a uniform field, the size and shape of which are measurable. The means by which these uniform fields are generated is referred to as a segmentation algorithm. Among the current systems, work by Soffer and Samet deals with symbolic-image databases with an emphasis on the concept of an object vs. a full scene or a grid cell. (SPIE Vol. 2679, pp 144-155.) The object by Soffer and Samet is a map feature in which segmentation is not needed.
In order to archive the information in a generic image automatically, the user must take an inventory of the features in the scene. The user must aggregate a set of spatially contiguous pixels into one substantially uniform region first. This pixel grouping process, generally referred to as segmentation, is the process of partitioning a scene into a set of uniform regions. But there are countless ways to segment an image; yet few of these ways are reliable enough for the user to obtain consistent results with imagery of diverse characteristics acquired at varying time intervals. A number of sophisticated segmentation methods have been developed. For example, an edge based segmentation method is disclosed by 20 Farag (1992), a region based method by Hsu (1992), a Markov random field based method by Tenior (1992), and a texture based method by Ma and Manjunath (1996). Ma and Manjunath discuss a comprehensive treatment for segmentation and region merging. Ma and Manjunath's algorithms appear powerful and effective; the feature extraction is totally texture and grid cell based. Nevertheless, the fundamental difficulty in segmentation remains to this day.
The field that deals with enhancing the visual quality of an image and extracting information of an image is called image processing, pattern recognition, remote sensing, or photogrammetry. Objects in a generic image comprising one or more layers are conventionally stored as data representing graytone and/or color pixels mixed with background pixels. Each subject can be identified by matching it against elements of a spectral library, provided with enough bands, such as that in a hyperspectral image cube. The end result of this matched filtering process is a still, pixel classification map, which shows no information other than the fact that each pixel belongs to a specific class or material type.
In the past, a spectral angle mapper of pixel based spectral matching was used to compare an observed waveform (i.e., spectral signature with elements of a spectral library). In the present invention, each pixel is treated as if it is an element of the library; therefore, there is no need to create a spectral library at all.
When a Principal Component (PC) process is used in order to detect objects in images, two major problems exist. First, a PC transform is not based on any objects. Therefore, the selected PC scenes may not contain the desired objects. Second, as documented by Schott, a PC scene is based on variance. Therefore, a low contrast band will not be exhibited, signifying a loss in the depiction of an image.
The relevant papers in “Storage and Retrieval for Image and Video Databases”, published by SPIE in 1993, reveals that few of the current systems are object based. In fact, the vast majority are full scene and/or grid cell based. (NeTra, by Ma and Manjunath of UC Santa Barbara, 1997.) One of the few object based programs deals only with simple objects such as human flesh tone vs. the background. (Tao, et al., SPIE Vol. 3002 p. 340-351, 1997.) The lack of object based systems is closely tied to the inability to segment a scene reliably and meaningfully. The reliability issue is associated with the stability of partitioned regions in the scene, whereas the meaningfulness of the scene is dependent on whether the knowledge of data experts is used in the segmentation algorithm.
There is a lack of lexicon data experts in Image Content Retrieval Systems. A brief review of current image database search systems discussed in SPIE's “Storage and Retrieval of Image and Video Databases (1993-1997)” reveals that most image databases use a subset of tone (color), texture and shape principles. Data experts' knowledge using combinations of key words is simply not used in object extraction algorithms.
The present invention operates in the inventor's IMaG system and uses a pseudo-English programming language, which includes processing and query language. The IMaG system integrates: (1) image processing, (2) multi-source analysis, and (3) GIS (Geographic Information Systems) into one single environment that possesses the tools to assist in solving smart imagery archiving problems. For example, the user can capture the knowledge of data experts and convert it to object based content retrieval algorithms; and then use these expert systems to extract the target objects in the scene, outlining and labeling each of them with appropriate text and color symbols automatically. As a result, the experts' knowledge is used directly to create an enhanced image that contains both textual and logical representations in addition to the original physical representation.
The benefits of textual information for information retrieval is discussed by Kurakake, et al. (SPIE, Vol. 3022, 20 pp. 368-379.) In addition, similar systems, such as DocBrowse, are discussed by Jaisimba, et al. (SPIE, 5 Vol. 2670, pp. 350-361) and IRIS by Hermes, et al. (SPIE Vol. 2420, pp. 394-405) assumes that textual data is already on the images. The above mentioned systems simply assume that text describing the image is on the image without knowing whether the image directly corresponds with the text. In contrast to this invention, the textual information is inserted into the original image after a feature extraction process is completed.
In order to extract an object from an image data set, the user must perform a number of information processing steps. The most crucial step of all is programming the algorithm with a machine-understandable language. Programming with a conventional machine language such as FORTRAN, C, and/or C++, however, is tedious, time-consuming and error prone. Most languages used for retrieval purposes in conventional systems are generally known as query languages. One of the well-known languages is the Manchester Visual Query Language (Oakley, et al., SPIE, Vol. 1908, pp. 104-122.) Another language is the QBIC system, which is used for querying by color, texture and shape. (SPIE, Vol. 1908, pp. 173-187.)
Varying scenarios of image conditions and scene complexity can prevent the algorithm from finding the intended objects
Boudreau Leo
Mark Levy & Assoc.
Sherali Ishrat
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