We present the design of correlation filters for detection of a target in a noisy input scene when the object of interest is given in a noisy. Correlation Pattern Recognition [B. V. K. Vijaya Kumar, Abhijit Mahalanobis, Richard D. Juday] on *FREE* shipping on qualifying offers. Pattern Recognition in Time-Frequency Domain: Selective Regional Correlation and Its Applications. By Ervin Sejdic and Jin Jiang. Published: November 1st.
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This task naturally lends itself to the application of correlation as a tool to accomplish this goal. Thus correlation pattern recognition field of Correlation Pattern Recognition has developed over the past few decades as an important area of research.
From the signal processing point of view, correlation is nothing but a filtering operation.
PATTERN RECOGNITION BY EXTENDED AUTO- AND CROSS CORRELATION
Thus there has been a great deal of work in using concepts from filter theory to develop Correlation Filters for pattern recognition. While considerable work has been to correlation pattern recognition to develop linear correlation filters over the years, especially in the field of Automatic Target Recognition, a lot of attention has recently been paid to the development of Quadratic Correlation Filters QCF.
QCFs offer the advantages of linear filters correlation pattern recognition optimizing a bank of these simultaneously to offer much improved performance.
This correlation pattern recognition develops efficient QCFs that offer significant savings in storage requirements and computational complexity over existing designs. Firstly, an adaptive algorithm is presented that is able to modify the QCF coefficients as new data is observed.
The goal is to match the live biometric images to those collected during enrollment. The major challenge is that biometric patterns often exhibit significant inter-class variability e.
Well-designed biometric verification systems correlation pattern recognition attempt to decrease both the false accept rate FAR i. Most biometric recognition approaches use features computed from the images.
The choice of features is critical to the efficiency of recognition systems and many types of features e.
Pattern Recognition in Time-Frequency Domain: Selective Regional Correlation and Its Applications
The resulting feature correlation pattern recognition selected as the test biometric is compared to the stored feature vector also called a template to obtain a similarity value between them.
Correlation pattern recognition the similarity is above a chosen threshold, a match is declared and a no match is declared otherwise. While several image domain-based biometric recognition methods have been proposed, frequency-domain methods—such as Fourier transform FT —had until recently received little attention.
In this context, FT is used as an information-preserving operation. There are several advantages to approaching biometric recognition in the spatial frequency domain i. Another important development in this field 3 was the ability to create correlation filters that can represent multiple distortions e.
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Another major difference from the early work is that we now do correlations in digital hardware due to the high speed with which 2D fast Correlation pattern recognition transforms FFT can be implemented.
Figure 1 schematically describes the use of CPR for face verification.
During enrollment, a few face images of the authentic user are acquired. The 2D FTs of these images are then used to generate a 2D filter. This correlation pattern recognition array is stored perhaps on a smart card to identify that authentic user.
When this authentic user presents his face image during verification, the 2D FFT of his live correlation pattern recognition image is multiplied pixel-wise by the stored filter array and the resulting product array is input to a 2D inverse FFT to produce a correlation output array.
As illustrated in Figure 1correlation pattern recognition correlation output exhibits a sharp peak for the authentic user and no such discernible correlation peak for an impostor. Schematic of the use of correlation pattern recognition for biometrics.
CPR offers other advantages as shown in Figure 2. In this example, the filter was designed using three training images representing the face of the subject with illumination-shadow in the left half, in the right half and no shadow of one subject retrieved from the well-known Carnegie Mellon University PIE pose, illumination and expression face database.
Centered and full test image top and resulting correlation output bottom.