The present invention features the use of the fundamental concept of color perception and multi-level resolution to perform scene segmentation and object/feature extraction in the context of self-determining and self-calibration modes. The technique uses only a single image, instead of multiple images as the input to generate segmented images. Moreover, a flexible and arbitrary scheme is incorporated, rather than a fixed scheme of segmentation analysis. The process allows users to perform digital analysis using any appropriate means for object extraction after an image is segmented. First, an image is retrieved. The image is then transformed into at least two distinct bands. Each transformed image is then projected into a color domain or a multi-level resolution setting. A segmented image is then created from all of the transformed images. The segmented image is analyzed to identify objects. Object identification is achieved by matching a segmented region against an image library. A featureless library contains full shape, partial shape and real-world images in a dual library system. The depth contours and height-above-ground structural components constitute a dual library. Also provided is a mathematical model called a Parzen window-based statistical/neural network classifier, which forms an integral part of this featureless dual library object identification system. All images are considered three-dimensional. Laser radar based 3-D images represent a special case.
A feature quantity calculation device to calculate a feature quantity for a 3-D object from 3-D object data contained in the 3-D object includes a 3-D object data input module to input the 3-D object data, a 3-D object data analysis module to analyze the input 3-D object data, a volume data conversion module to convert the analyzed 3-D object data to volume data, and a feature quantity calculation module to calculate a feature quantity from the converted volume data.
The invention is a method for augmenting images to highlight regions where objects of interest are likely to appear. The method uses fuzzy set theory to classify locations that may contain objects of interest, uses fog volumes to represent the fuzzy sets in three dimensions, and renders these fog volumes from the vantage point of the image to provide a colored overlay with which to augment the image. Where fog volumes of different colors overlap in an image, the invention renders the overlapping area using cross-hatching, so that the multiple colors appear side by side rather than blending to make a third color.
A system and method for selecting a best match of a received input signal from a set of candidate signals, wherein two or more of the candidate signals are uncorrelated. In a preprocessing phase a unified signal transform (UST) is determined from the candidate signals. The UST converts each candidate signal to a generalized frequency domain. The UST is applied at a generalized frequency to each candidate signal to calculate corresponding generalized frequency component values (GFCVs) for each candidate signal. At runtime, the input signal of interest is received, and the UST is applied at the generalized frequency to the input signal of interest to calculate a corresponding GFCV. The best match is determined between the GFCV of the input signal of interest and the GFCVs of each of the set of candidate signals. Finally, information indicating the best match candidate signal from the set of candidate signals is output.
Three-dimensional position information is used to segment objects in a scene viewed by a three dimensional camera. At one or more instances of an interval, the head location of the user is determined. Object-based compression schemes are applied on the segmented objects and the detected head.
The present invention features a method for identifying objects in an image and automatically generating of object recognition code. First, an image is segmented and coded in a first plane in at least one segmentation domain to create a first set of uniform fields or regions having interior and exterior information. Then the image is segmented and coded in a second plane, again in at least one segmentation domain, to create a second set of uniform fields or regions having interior and exterior information. The relationship of the boundaries of the first and second sets of fields is then determined. Information representative of the uniform fields is analyzed and/or communicated horizontally and vertically for relationships of objects among the planes. The system is then queried, forming object definitions, in order to recognize objects in the image. The objects can also be displayed on a common image domain. The image can also be resegmented, each field being coded by a multi-bit (e.g., 32 bit) representation of an identification code. Boundaries and chains can be generated from the image. The objects in the image can also be arranged by order of importance.
