Nalini K. Ratha

A real-time matching system for large fingerprint databases

With the current rapid growth in multimedia technology, there is an imminent need for efficient techniques to search and query large image databases. Because of their unique and peculiar needs, image databases cannot be treated in a similar fashion to other types of digital libraries. The contextual dependencies present in images, and the complex nature of two-dimensional image data make the representation issues more difficult for image databases. An invariant representation of an image is still an open research issue. For these reasons, it is difficult to find a universal content-based retrieval technique. Current approaches based on shape, texture, and color for indexing image databases have met with limited success. Further, these techniques have not been adequately tested in the presence of noise and distortions. A given application domain offers stronger constraints for improving the retrieval performance. Fingerprint databases are characterized by their large size as well as noisy and distorted query images. Distortions are very common in fingerprint images due to elasticity of the skin. In this paper, a method of indexing large fingerprint image databases is presented. The approach integrates a number of domain-specific high-level features such as pattern class and ridge density at higher levels of the search. At the lowest level, it incorporates elastic structural feature-based matching for indexing the database. With a multilevel indexing approach, we have been able to reduce the search space. The search engine has also been implemented on Splash 2-a field programmable gate array (FPGA)-based array processor to obtain near-ASIC level speed of matching. Our approach has been tested on a locally collected test data and on NIST-9, a large fingerprint database available in the public domain.

Adaptive flow orientation-based feature extraction in fingerprint images☆

Abstract A reliable method for extracting structural features from fingerprint images is presented. Viewing fingerprint images as a textured image, an orientation flow field is computed. The rest of the stages in the algorithm use the flow field to design adaptive filters for the input image. To accurately locate ridges, a waveform projection-based ridge segmentation algorithm is used. The ridge skeleton image is obtained and smoothed using morphological operators to detect the features. A large number of spurious features from the detected set of minutiae is deleted by a postprocessing stage. The performance of the proposed algorithm has been evaluated by computing a “goodness index” (GI) which compares the results of automatic extraction with manually extracted ground truth. The significance of the observed GI values is determined by comparing the index for a set of fingerprints against the GI values obtained under a baseline distribution. The detected features are observed to be reliable and accurate.

Object detection using gabor filters

Abstract This paper pertains to the detection of objects located in complex backgrounds. A feature-based segmentation approach to the object detection problem is pursued, where the features are computed over multiple spatial orientations and frequencies. The method proceeds as follows: a given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture energy is computed in a window around each transformed image pixel. The texture energy (“Gabor features”) and their spatial locations are inputted to a squared-error clustering algorithm. This clustering algorithm yields a segmentation of the original image—it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across different spatial orientations and frequencies. The method is applied to a number of visual and infrared images, each one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique signature of “Gabor features” is typically associated with the segment containing the object(s) of interest. Experimental results are provided to illustrate the usefulness of this object detection method in a number of problem domains. These problems arise in IVHS, military reconnaissance, fingerprint analysis, and image database query.

Convolution on Splash 2

Convolution is a fundamental operation in many signal and image processing applications. Since the computation and communication pattern in a convolution operation is regular, a number of special architectures have been designed and implemented for this operator. The Von Neumann architectures cannot meet the real-time requirements of applications that use convolution as an intermediate step. We combine the advantages of systolic algorithms with the low cost of developing application specific designs using field programmable gate arrays (FPGAs) to build a scalable convolver for use in computer vision systems. The performance of the systolic algorithm of (Kung et al., 1981) is compared theoretically and experimentally with many other convolution algorithms reported in the literature. The implementation of a convolution operation on Splash 2, an attached processor based on Xilinx 4010 FPGAs, is reported with impressive performance gains.

Parallel implementation of vision algorithms on workstation clusters

Parallel implementations of two computer vision algorithms on distributed cluster platforms are described. The first algorithm is a square-error data clustering method whose parallel implementation is based on the well-known sequential CLUSTER program. The second algorithm is a motion parameter estimation algorithm used to determine correspondence between two images taken of the same scene. Both algorithms have been implemented and tested on cluster platforms using the PVM package. Performance measurements demonstrate that it is possible to attain good performance in terms of execution time and speedup for large-scale problems, provided that adequate memory; swap space, and I/O capacity are available at each node.

Clustering using a coarse-grained parallel genetic algorithm: a preliminary study

Genetic algorithms (GA) are useful in solving complex optimization problems. By posing pattern clustering as an optimization problem, GAs can be used to obtain optimal minimum squared error partitions. In order to improve the total execution time, a distributed algorithm has been developed using the divide and conquer approach. Using a standard communication library called PVM, the distributed algorithm has been implemented on a workstation cluster: the GA approach gives better quality clusters for many data sets compared to a standard K-means clustering algorithm. We have achieved a near linear speedup for the distributed implementation.

A distributed edge detection and surface reconstruction algorithm

A scalable parallel algorithm for edge detection and surface reconstruction is presented. The algorithm is based on fitting a weak membrane to the pixel gray valves by minimizing the associated energy functional. The edge detection process is modeled as a line process and used as a constraint in minimizing the energy functional of the image. The optimal edge assignment cannot be obtained directly as the energy function is non-convex. Using graduated non-convexity (GNC) approach, the energy is minimized. The proposed parallel algorithm has been implemented on a cluster of workstations using the PVM communication library. The results of parallel implementation on synthetic and natural images are presented. The speedup is observed to be near-linear, thus providing scalability with the problem size. The parallel processing approach presented here can be extended to solve similar problems (e.g., image restoration, and image compression) which use regularization techniques.

FPGA-based high performance page layout segmentation

A page layout segmentation algorithm for locating text, background and halftone areas is presented. The algorithm has been implemented on Splash 2-an FPGA-based array processor. The speed as determined by the Xilinx synthesis tools projects an application speed of 5 MHz. For documents of size 1,024/spl times/1,024 pixels, a significant speedup of two orders of magnitude compared to a SparcStation 20 has been achieved.

Object Detection in the Presence of Clutter Using Gabor Filters

In this paper the problem of detecting objects in the presence of clutter is studied. The images considered are obtained from both visual and infrared sensors. A feature-based segmentation approach to the object detection problem is pursued, where the features used are computed over multiple spatial orientations, and frequencies. The method proceeds as follows: A given image is passed through a bank of even-symmetric Gabor filters. A selection of these filtered images is made and each (selected) filtered image is subjected to a nonlinear (sigmoidal like) transformation. Then, a measure of texture `energy is computed in a window around each transformed image pixel. The texture `energy features, and their spatial locations, are inputted to a least squared error based clustering algorithm. This clustering algorithm yields a segmentation of the original image -- it assigns to each pixel in the image a cluster label that identifies the amount of mean local energy the pixel possesses across the different spatial orientations, and frequencies. This method is applied on a number of visual and infrared images, every one of which contains one or more objects. The region corresponding to the object is usually segmented correctly, and a unique set of texture `energy features is typically associated with the segment containing the object(s) of interest.