P. Ragothaman

An efficient quadratic correlation filter for automatic target recognition

Quadratic Correlation Filters have recently been used for Automatic Target Recognition (ATR). Among these, the Rayleigh Quotient Quadratic Correlation Filter (RQQCF) was found to give excellent performance when tested extensively with Infrared imagery. In the RQQCF method, the filter coefficients are obtained, from a set of training images, such that the response to the filter is large when the input is a target and small when the input is clutter. The method explicitly maximizes a class separation metric to obtain optimal performance. In this paper, a novel transform domain approach is presented for ATR using the RQQCF. The proposed approach, called the Transform Domain RQQCF (TDRQQCF) considerably reduces the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. Since the dimensionality of the data points is reduced, this method also overcomes the common problem of dealing with low rank matrices arising from the lack of large training sets in practice. This is achieved while retaining the high recognition accuracy of the original RQQCF technique. The proposed method is tested using IR imagery, and sample results are presented which confirm its excellent properties.

A performance comparison of the transform domain Rayleigh quotient quadratic correlation filter (TDRQQCF) approach to the regularized RQQCF

The Rayleigh Quotient Quadratic Correlation Filter (RQQCF) has been used to achieve very good performance for Automatic Target Detection/Recognition. The filter coefficients are obtained as the solution that maximizes a class separation metric, thus resulting in optimal performance. Recently, a transform domain approach was presented for ATR using the RQQCF called the Transform Domain RQQCF (TDRQQCF). The TDRQQCF considerably reduced the computational complexity and storage requirements, by compressing the target and clutter data used in designing the QCF. In addition, the TDRQQCF approach was able to produce larger responses when the filter was correlated with target and clutter images. This was achieved while maintaining the excellent recognition accuracy of the original spatial domain RQQCF algorithm. The computation of the RQQCF and the TDRQQCF involve the inverse of the term A1 = Rx + Ry where Rx and Ry are the sample autocorrelation matrices for targets and clutter respectively. It can be conjectured that the TDRQQCF approach is equivalent to regularizing A1. A common regularization approach involves performing the Eigenvalue Decomposition (EVD) of A1, setting some small eigenvalues to zero, and then reconstructing A1, which is now expected to be better conditioned. In this paper, this regularization approach is investigated, and compared to the TDRQQCF.

Automatic target recognition employing signal compression

Quadratic correlation filters (QCFs) have been used successfully to detect and recognize targets embedded in background clutter. Recently, a QCF called the Rayleigh quotient quadratic correlation filter (RQQCF) was formulated for automatic target recognition (ATR) in IR imagery. Using training images from target and clutter classes, the RQQCF explicitly maximized a class separation metric. What we believe to be a novel approach is presented for ATR that synthesizes the RQQCF using compressed images. The proposed approach considerably reduces the computational complexity and storage requirements while retaining the high recognition accuracy of the original RQQCF technique. The advantages of the proposed scheme are illustrated using sample results obtained from experiments on IR imagery.

Adaptive determination of eigenvalues and eigenvectors from perturbed autocorrelation matrices for automatic target recognition

The Modified Eigenvalue problem arises in many applications such as Array Processing, Automatic Target Recognition (ATR), etc. These applications usually involve the Eigenvalue Decomposition (EVD) of matrices that are time varying. It is desirable to have methods that eliminate the need to perform an EVD every time the matrix changes but instead update the EVD adaptively, starting from the initial EVD. In this paper, we propose a novel Optimal Adaptive Algorithm for the Modified EVD problem (OAMEVD). Sample results are presented for an ATR application, which uses Rayleigh Quotient Quadratic Correlation filters (RQQCF). Using a Infrared (IR) dataset, the effectiveness of this new technique as well as its advantages are illustrated.

Nontraditional signal processing techniques employing linear transforms

Nontraditional transform domain signal processing techniques, applied to two important signal processing areas are given. In the first technique, audio and video signals are represented using the superposition of nonorthogonal basis functions. It is shown that this nontraditional representation yields efficient compression algorithms, i.e, for the same bit rate, higher representation accuracy is achieved, or for the same representation accuracy, lower bit rate is required, relative to previously reported methods. In the second technique, signal autocovariance formulations in the transform domains are presented. It is shown that these formulations yield practical solutions to important signal processing classification and recognition problems. This is because these nontraditional autocovariance formulations result in considerable reduction in the storage requirements and the computational complexity, without sacrificing the signal representation accuracy associated with the traditional autocovariance formulation in the time and/or in the spatial domains.

Multiple non-orthogonal bases representations for images

Conventional image compression techniques usually involve use of a single transform in conjunction with other techniques like vector quantization etc. In this paper, the concept of using more than one transform, i.e., using multiple non-orthogonal bases functions representation, for still image compression, is presented. Sample results show that this approach used in conjunction with an efficient vector quantization technique - an adaptive energy based split vector quantization technique, gives improved reconstruction quality of images for the same bit rate compared to existing single transform methods.