Conference Papers
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Item Hidden Markov model-contourlet Hidden Markov tree based texture segmentation(2004) Raghavendra, B.S.; Subbanna Bhat, P.Contourlets have emerged as a new mathematical tool for image processing and provide compact and decorrelated image representations. Hidden Markov modeling (HMM) of contourlet coefficients is a powerful approach for statistical processing of natural images. In this paper, we extended the hidden Markov modeling framework to contourlets and combined hidden Markov trees (HMT) with hidden Markov model to form HMM-Contourlet HMT model. The model is used for block based multiresolution texture segmentation. The performance of the HMM-Contourlet HMT texture segmentation method is compared with that of HMM-Real HMT and HMM-Complex HMT methods. The HMM-Contourlet HMT method provides superior texture segmentation results and excellent visual performance at small block sizes. © 2004 IEEE.Item Shift-invariant image denoising using mixture of laplace distributions in wavelet-domain(2006) Raghavendra, B.S.; Subbanna Bhat, P.In this paper, we propose a new method for denoising of images based on the distribution of the wavelet transform. We model the discrete wavelet coefficients as mixture of Laplace distributions. Redundant, shift invariant wavelet transform is made use of in order to avoid aliasing error that occurs with critically sampled filter bank. A simple Expectation Maximization algorithm is used for estimating parameters of the mixture model of the noisy image data. The noise is considered as zero-mean additive white Gaussian. Using the mixture probability model, the noise-free wavelet coefficients are estimated using a maximum a posteriori estimator. The denoising method is applied for general category of images and results are compared with that of wavelet-domain hidden Markov tree method. The experimental results show that the proposed method gives enhanced image estimation results in the PSNR sense and better visual quality over a wide range of noise variance. © Springer-Verlag Berlin Heidelberg 2006.Item Effectiveness of Phase Correlation Spectral Similarity Measure in Distinguishing Target Signatures for Hyperspectral Data Analysis(Institute of Electrical and Electronics Engineers Inc., 2020) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral imaging is one of the most information-rich sources of remote sensing data that exists. It can capture the entire, continuous spectrum of color and light. Feature extraction techniques that are selected for identifying diagnostic features influence classification accuracy. Spectral matching algorithms like similarity measures are developed to compare spectral features of materials with their reference spec-tral signatures in identifying different earth surfaces. Similarity measures are used as simple feature extraction techniques in target identification using hyperspectral data. Though there are several similarity measures, selecting a robust similarity measure requires further investigation. Influence of similarity measures are not studied much in distinguishing spectrally distinct signatures. In this article, we propose to study the performance of similarity measures in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering signatures of different classes and (iv) endmember extraction. Experimental results show an effective and a robust performance of proposed phase correlation similarity measure among all other similarity measures compared for all the problems under investigation. © 2020 IEEE.Item Similarity measures in generating spectrally distinct targets(Institute of Electrical and Electronics Engineers Inc., 2020) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.In multispectral and hyperspectral remote sensing, classification of pixels is obtained by means of spectral similarity of known field or library spectra to unknown image spectra. Endmember extraction is the most decisive task in hyperspectral image analysis. Endmember initialization algorithms (EIAs) play a key role and support endmember extraction algorithms (EEAs) in extracting near optimal set of endmembers. Though there are few endmember initialization techniques available, similarity measures are not explored in detail in target generation. Hence, in this paper, it is proposed to explore similarity measures in identifying spectrally distinct signatures to use them as initial endmembers. Individual similarity measures are combined to form hybrid similarity measures to confirm their effectiveness in generating spectrally distinct targets. Initial set of endmembers extracted by proposed algorithm are used for initializing classical EEA, the NFINDR, which is sensitive to endmember initialization, and their performance in final endmembers selection is verified. Experimental results on two hyperspectral data sets show the superior performance of the similarity based endmember initialization algorithm (SMEIA). © 2020 IEEE.Item INFLUENCE OF THE DARKEST PIXEL ON ENDMEMBERS INITIALIZATION(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Endmember extraction is one of the necessary steps in hyperspectral data investigation. The eventual objective of hyperspectral data processing and analysis is to improve the accuracy of target identification, and the precise identification of endmembers is a challenging task. The several of the algorithms proposed for endmember extraction require knowledge on the targets for appropriate initialization. The endmember extraction algorithms (EEAs) rely on endmember initialization algorithms (EIAs) and in turn many EIAs also require the knowledge on targets of interest to begin the search process effectively. A comprehensive study on targets of interest to begin the search process is due expected. Therefore, in this paper, the concept of extreme or boundary pixels is explored to identify the targets of interest and the darkest pixel (a pixel with minimum length) is identified and proposed as a new target of interest to begin the endmember search process. The utility/influence of the proposed target of interest, i.e. the darkest pixel, is studied with that's of the brightest pixel (a pixel with maximum length) which has been in use as a popular target of interest for EIAs so far. An automatic target generation process (ATGP) and a similarity measures based endmember initialization algorithm (SMEIA) are adapted to test the proposed initialization strategy. The experimental results have revealed the usefulness of the darkest pixel as an additional target of interest in addition to the brightest pixel in searching for endmembers by EIAs. The strategy of choosing the darkest pixel enhanced the initial target knowledge and also improved the performance of the EIAs considerably. © 2021 IEEE.Item Measurement and Evaluation of Human Vital Sign using 77GHz AWR1642 FMCW Radar Sensor(Institute of Electrical and Electronics Engineers Inc., 2021) Srihari, P.; Vandana, G.S.; Raghavendra, B.S.This paper proposes vital sign measurements of humans like heart rate (HR) and respiration rate (RR). The Texas Instruments AWR1642, frequency modulated continuous wave (FMCW) mmwave radar sensor, operating as 77-81 GHz frequency with 4GHz as the sweep bandwidth of the linear frequency modulated (LFM) waveform, is deployed to measure the HR and RR vital parameters. Here, twenty subjects (10 male; 10 female) experimental data has been collected using TI AWR1642 radar sensor, personal computer (PC), and DCA1000 data acquisition module. The HR and RR algorithm is applied to the process data to measure the HR and RR of these subjects. The male and female subjects' average HR is 78.587 and 77.827, respectively. Further, the average RR of the male and female subjects is 19.959 and 19.23, respectively. Furthermore, a pulse oximeter(POM) obtains HR ground truth information to validate the proposed method. The overall absolute mean error percentage(MEP) compared to pulse oximeter data is 4.7935% for 20 volunteers who have participated in the experiment. © 2021 IEEE.Item Gradient Based Spectral Similarity Measure for Hyperspectral Image Analysis(Institute of Electrical and Electronics Engineers Inc., 2021) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Spectral matching algorithms or similarity measures dis-criminate spectral signatures of unknown materials by comparing them with the reference spectral signatures. Spectral matching algorithms play an important role in identifying different earth surface features using hyperspectral image analysis. Identification of diagnostic features and discriminating spectral signatures, especially of mineral signatures, which exhibit subtle differences among themselves is still a challenging task. Thus, developing or coming up with a new spectral matching algorithms is expected. Therefore, in this paper, we present a gradient based spectral similarity measure (GSSM) that captures the diagnostic (absorption) features to measure the degree of closeness between spectral signatures. Effectiveness of the proposed GSSM in distinguishing spectrally distinct signatures is studied with that of other spectral matching algorithms in (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Experimental results on a spectral library of 5 minerals formed using a benchmark mineral dataset called Cuprite data clearly show that the proposed GSSM is capable of (i) discriminating different spectral signatures in a better manner and also (ii) bringing into notice of the user the locations of diagnostic features by highlighting them to recognize easily. © 2021 IEEE.Item Magnetic resonance image reconstruction by nullspace based finite rate of innovation framework(Association for Computing Machinery, 2021) Sudhakar Reddy, P.S.; Raghavendra, B.S.; Narasimhadhan, A.V.The finite rate of innovation (FRI) framework has proved that it is possible to reconstruct the analog signals which have a finite number of parameters. FRI framework is used to reconstruct the images from undersampled magnetic resonance (MR) data. The reconstruction of the MR image from the MR data is a estimation problem, which can be solved by utilizing Prony's method. However, Prony's method involves solving the polynomial roots of the annihilating filter and this fact leads to an unstable reconstruction in the high noise scenario. In this paper, we introduce a novel reconstruction approach is also based on the annihilating filter. However, it involves the use of solutions of an underdetermined linear system. The simulation results of the proposed reconstruction approach show that the peak signal to noise ratio (PSNR) and the structural similarity index measure (SSIM) are higher magnitude than that of conventional FRI methods in the high noise scenario.1 © 2021 ACM.Item Gradient Correlation Incorporated Similarity Measures in Matching Spectral Signatures(Institute of Electrical and Electronics Engineers Inc., 2022) Yadav, P.P.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral images provide ample information which is needed to be analyzed carefully by the spectral processing algorithms for identifying objects, finding minerals etc. Spectral matching algorithms (SMAs) which make use of similarity measures discriminate and identify earth surface features by comparing spectral signatures with the ground-truth. SMAs that discriminate overall patterns capturing the diagnostic features of spectral signatures is of great use. In view of this, in this paper, we explore spectral gradient as a diagnostic feature to discriminate spectral signatures. Applicability of the proposed spectral gradient which is incorporated with SMAs in distinguishing spectrally distinct signatures is experimented in the following cases: (i) discriminating endmember signatures (ii) mixed pixel identification (iii) clustering spectral signatures of different classes and (iv) endmember extraction. Overall, the experimental results on a benchmark mineral Cuprite dataset library of five minerals have shown significantly improved performance in discriminating various spectral signatures. © 2022 IEEE.Item ATGP based Change Detection in Hyperspectral Images(IEEE Computer Society, 2022) Yadav, P.P.; Bobate, N.; Shetty, A.; Raghavendra, B.S.; Narasimhadhan, A.V.Hyperspectral images (HSIs) due to advancements in spatial-spectral resolutions and availability of multi-temporal information is in demand for many remote sensing (RS) applications including change detection (CD). The high dimensionality of HSIs and limited availability of HSI-CD data sets with ground-truth change maps make CD not so easy but a difficult task. Though there are many classical and deep learning (DL) based algorithms to detect changes, the performance of classical algorithms is not up to the satisfactory level and the final performance of DL models depend on efficiency of pre-detection techniques which provide prior knowledge on changed and unchanged areas that are required to get appropriate training samples to learn to detect changes. Classical and DL approaches consider change information at pixel level i.e. pixel to pixel change either by comparing the corresponding pixels alone or with their local neighborhood pixels. Therefore, identification of features for every pixel that relate the most significant information of the whole HSI-CD data in a simple and an efficient way to detect changes effectively is the need of the hour. In addition, there is not much comprehensive study on developing CD algorithms that not only simple to use but also as efficient as that of DL models is available. Therefore, in this paper, an endmember based feature extraction is proposed to detect changes in HSI. An automatic target generation process (ATGP) algorithm is adapted to extract endmembers present in the HSI-CD data set. Then, various spectral matching algorithms are used to measure endmember relations for all the pixels so that dimensionality of the data is reduced as well as the effective features to detect changes can be extracted. The experimental results on three benchmark HSI-CD data sets show that proposed ATGP based change vector analysis (CVA) algorithm yields remarkable results on comparing both with the classical as well as DL based CD approaches. © 2022 IEEE.