Previous papers have studied the relationship between a bit map digital image and a given object, called the search
object. In particular, to signal that it is likely, or not likely, that the search object appears, at least partially, in the
image. Edges in the search object and in the digital image are then represented as objects, in the object oriented
programming sense. Each edge or segment of an edge is represented as a normalized Bezier cubic parameterized
curve. The normalization process is intended to remove the effect of size in the edge or edge segment. If the edges
match and their orientation is the same, then the system signals that the object is likely to appear in the image and
the coordinates in the image of the object are returned. The functioning of the algorithm is not dependent on scaling,
rotation, translation, or shading of the image. To begin the data mining process, a collection of search objects is
generated. A database is constructed using a number of images and storing information concerning the combination
of search objects that appear in each image, time and space relationships between the various search objects, along
with identifying information about the image. This database would then be subjected to traditional data mining
techniques in order to generate useful relationships within the data. These relationships could then be used to
advantage in supplying information for defense, corporate, or law enforcement intelligence.
The purpose of this paper is to study the relationship between a bit map digital image and a given object, called the search object. In particular, to signal that it is likely, or not likely, that the search object appears, at least partially, in the image. The edges are detected using known techniques. The edges are then converted to sequences of pixels. Edges in the search object and in the digital image are then represented as objects, in the object oriented programming sense. Each edge or segment of an edge is represented as a normalized Bezier cubic parameterized curve. The conversion from a sequence of pixels to a Bezier polynomial representation is accomplished using least squares approximation techniques. The normalization process is intended to remove the effect of size in the edge or edge segment. The original Bezier representation is also maintained for each edge, as it provides necessary location information. In the event that two edges in the search object are matched with edges in the image, their relative orientation is checked using elementary vector analysis. If the edges match and their orientation is the same, then the system signals that the object is likely to appear in the image and the coordinates in the image of the object are returned. The functioning of the algorithm is not dependent on scaling, rotation, translation, or shading of the image.
The purpose of this paper is to study the relationship between a bit map digital image and a given object, called the search object. In particular, to signal that it is likely, or not likely, that the search object appears, at least partially, in the image. Edge detection capability is assumed. Edges in the search object and in the digital image are represented as objects, in the object oriented programming sense, as Bezier cubic parameterized curves. The conversion from sequence of pixels to a Bezier polynomial representation is accomplished using least squares approximation techniques. In the event that two edges in the search object are matched with edges in the image, their relative orientation is checked using elementary vector analysis. The functioning of the algorithm is not dependent on scaling, rotation, translation, or shading of the image.
The goal of image processing is generally to identify objects and their relationships in a digital image. The first step in this process is normally the identification of the edges in the digital image. There are many different ways to do this and they will be discussed briefly in the background and literature section. This paper assumes that a given object is presented and the software program is asked to determine if that particular object is present in a given image. The final result would consist of informing the user that the object was represented in the image and computing the pixel position of the search object in the given image. Of course if the object is not found, the user should be given that information. The proposed algorithm uses cubic Bezier curves to represent both the search object and the edges identified in the image. The advantages of the Bezier curve approach are discussed.
Image enhancement applications are highly dependent on the efficiency of edge detection techniques. Most of these techniques have a time complexity of O(n2) where the picture has size n X n. The use of more advanced algorithms can substantially reduce this requirement, improving the computational performance of the application. This paper presents a new method, named GALE, which combines the random search mechanisms of Genetic Algorithms with linear time methods. The resulting edge detection process approaches linear time complexity as demonstrated in the experiments also reported here. The Genetic Algorithm is constructed by utilizing a fitness measurement which is proportional to a directional gradient to select picture windows and establishes candidate pairs of points which bracket an edge. Such areas are then investigated by using near-neighbor linear techniques and the Sobel number for edge identification and detection. The linear technique procedures are built in such a way that the use of other fitness functions, such as the Sombrero operator, instead of the Sobel number are easily implemented and activated. The paper begins by discussing related work in this area, following by the description of the basic concepts of Genetic Algorithms required for this solution. A detailed view of the linear search algorithm is then presented, followed by a report on some experiments conducted in a controlled environment. Theoretical results are used to support the evidence of the time complexity and correctness of this new method. In addition, the experimental results show the improved performance of this method.
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