Focusing attention on objects of interest using multiple matched filters

• Published in: IEEE transactions on image processing Vol. 10; no. 3; pp. 419 - 426
• Main Authors: Stough, T.M., Brodley, C.E.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.2001; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Applied sciences; Artificial intelligence; Clustering algorithms; Computational complexity; Computer science; control theory; systems; Exact sciences and technology; Focusing; Image analysis; Image databases; Image processing; Information, signal and communications theory; Matched filters; Object detection; Pattern recognition. Digital image processing. Computational geometry; Planetary probes; Prototypes; Reduction; Signal processing; Studies; Technological innovation; Telecommunications and information theory; Volcanoes; Matched filter; Image processing; Focusing; Image databank; Particular object; Database; Feature extraction; Experimental study; Computational complexity
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/83.908516

In order to be of use to scientists, large image databases need to be analyzed to create a catalog of the objects of interest. One approach is to apply a multiple tiered search algorithm that uses reduction techniques of increasing computational complexity to select the desired objects from the database. The first tier of this type of algorithm, often called a focus of attention (FOA) algorithm, selects candidate regions from the image data and passes them to the next tier of the algorithm. In this paper we present a new approach to FOA that employs multiple matched filters (MMF), one for each object prototype, to detect the regions of interest. The MMFs are formed using k-means clustering on a set of image patches identified by domain experts as positive examples of objects of interest. An innovation of the approach is to radically reduce the dimensionality of the feature space, used by the k-means algorithm, by taking block averages (spoiling) the sample image patches. The process of spoiling is analyzed and its applicability to other domains is discussed. The combination of the output of the MMFs is achieved through the projection of the detections back into an empty image and then thresholding. This research was motivated by the need to detect small volcanos in the Magellan probe data from Venus. An empirical evaluation of the approach illustrates that a combination of the MMF plus the average filter results in a higher likelihood of 100% detection of the objects of interest at a lower false positive rate than a single matched filter alone.