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Joshi, Manjunath V

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Manjunath V Joshi

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079-68261611

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Signal and Image Processing, Digital Communication, Computer Vision, Machine Learning

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Manjunath V. Joshi received the B.E. degree from the University of Mysore, Mysore, India, and the M.Tech. and Ph.D. degrees from the Indian Institute of Technology Bombay (IIT Bombay), Mumbai, India. Currently, he is serving as a Professor with the Dhirubhai Ambani Institute of Information and Communication Technology, Gandhinagar, India. He has been involved in active research in the areas of signal and Image Processing, Computer Vision, and Machine Learning and has several publications in quality journals and conferences. He has co-authored four books entitled Motion-Free Super Resolution (Springer, New York-2005), Digital Heritage Reconstruction using Super-resolution and Inpainting (Morgan and Claypool-2016), Regularization in Hyperspectral Unmixing (SPIE Press-2016) and the book entitled Multi-resolution Image Fusion in Remote Sensing (Cambridge University Press, UK-2019). So far nine PhD students have been graduated under his supervision. Dr. Joshi was a recipient of the Outstanding Researcher Award in Engineering Section by the Research Scholars Forum of IIT Bombay in 2005. He was also a recipient of the Best Ph.D. Thesis Award by Infineon India and the Dr. Vikram Sarabhai Award for the year 2006-2007 in the field of information technology constituted by the Government of Gujarat, India. He served as a Program Co-Chair for the 3rd ACCV Workshop on E-Heritage, 2014 held at Singapore. He was honoured with a plaque of appreciation on achieving excellence in teaching and research at DA-IICT on the occasion of Teacher�s Day 5th September 2015. Dr. Joshi has successfully conducted two Continuing Education Programs (CEPs) for scientists of ISRO, Ahmedabad during August 2012 and March 2016, respectively. He has also served as Visiting Professor at Gandhinar and IIIT Vadodara. He has visited Germany, Italy, France, Hong Kong, USA, Canada, South Korea, Indonesia and contributed to research in his area of expertise. For more information visit

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Now showing 1 - 10 of 29
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    Spectrum Sensing for Cognitive Radio : Fundamentals and Applications
    (CRC Press, Boca Raton, 2021-12-31) Captain, Kamal M; Joshi, Manjunath V
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    Multi-resolution Image Fusion in Remote Sensing
    (Cambridge University Press, Cambridge, 2019-03-01) Joshi, Manjunath V; Upla, Kishor p
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    Digital heritage reconstruction using super-resolution and inpainting
    (Morgan & Claypool, California, 2017-01-01) Padalkar, Milind G; Joshi, Manjunath V; Khatri, Nilay L
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    Automatic segmentation and yield measurement of fruit using shape analysis
    (01-05-2012) Patel, Hetal N; Jain, R K; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar
    In comparison to field crops such as cereals, cotton, hay and grain, specialty crops often require more resources, are usually more sensitive to sudden changes in growth conditions and are known to produce higher value products. Providing quality and quantity assessment of specialty crops during harvesting is crucial for securing higher returns and improving management practices. Technical advancements in computer and machine vision have improved the detection, quality assessment and yield estimation processes for various fruit crops, but similar methods capable of exporting a detailed yield map for vegetable crops have yet to be fully developed. A machine vision-based yield monitor was designed to perform size categorization and continuous counting of shallots in-situ during the harvesting process. Coupled with a software developed in Python, the system is composed of a video logger and a global navigation satellite system. Computer vision analysis is performed within the tractor while an RGB camera collects real-time video data of the crops under natural sunlight conditions. Vegetables are first segmented using Watershed segmentation, detected on the conveyor, and then classified by size. The system detected shallots in a subsample of the dataset with a precision of 76%. The software was also evaluated on its ability to classify the shallots into three size categories. The best performance was achieved in the large class (73%), followed by the small class (59%) and medium class (44%). Based on these results, the occasional occlusion of vegetables and inconsistent lighting conditions were the main factors that hindered performance. Although further enhancements are envisioned for the prototype system, its modular and novel design permits the mapping of a selection of other horticultural crops. Moreover, it has the potential to benefit many producers of small vegetable crops by providing them with useful harvest information in real-time.
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    A model-based approach to multiresolution fusion in remotely sensed images
    (IEEE, 01-09-2006) Joshi, M V; Bruzzonne, Lorenzo; Chaudhuri, Subhasis; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar
    In this paper, a model-based approach to multiresolution fusion of remotely sensed images is presented. Given a high spatial resolution panchromatic (Pan) image and a lowspatial resolution multispectral (MS) image acquired on the same geographical area, the presented method aims to enhance the spatial resolution of the MS image to the resolution of the Pan observation. The proposed fusion technique utilizes the spatial correlation of each of the high-resolution MS channels by using an autoregressive (AR) model, whose parameters are learnt from the analysis of the Pan data. Under the assumption that the parameters of the AR model for the Pan image are the same as those that represent the MS images due to spectral correlation, the proposed technique exploits the learnt parameter values in the context of a proper regularization technique to estimate the high spatial resolution fields for the MS bands. This results in a combination of the spectral characteristics of the low-resolution MS data with the high spatial resolution of the Pan image. The main advantages of the proposed technique are: 1) unlike standard methods proposed in the literature, it requires no registration between the Pan and the MS images; 2) it models effectively the texture of the scene during the fusion process; 3) it shows very small spectral distortion (as it is less affected, compared to standard methods, by the specific digital numbers of pixels in the Pan image, since it exploits the learnt parameters from the Pan image rather than the actual Pan digital numbers for fusion); and 4) it can be used in critical situations in which the Pan and the MS images are acquired (also by different sensors) in slightly different areas. Quantitative experimental results obtained using Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Quickbird images point out the effectiveness of the proposed method
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    Design of Complex Adaptive Multiresolution Directional Filter Bank and Application to Pansharpening
    (Springer, 01-02-2017) Gajbhar, Shrishail S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Gajbhar, Shrishail S (201121016)
    This paper proposes a new 2-D transform design, namely�complex adaptive multiresolution directional filter bank, to represent the spatial orientation features of an input image�adaptively. The proposed design is completely�shift invariant�and represents the input image by one low-pass and multiscale�N�directional band-pass subbands. Here,�N�represents estimated number of dominant directions present in the input image. Our design consists of two main filter bank stages. A fix partitioned complex-valued directional filter bank (CDFB) is at the core of the design followed by a novel partition filter bank stage. Fine partitioning of the CDFB subbands is used to get the adaptive nature of the proposed transform. The partitioning decision is made based on the directional significance of range of CDFB subband angle selectivity in the input image. Partition filter bank stage which�nonuniformly�partitions the CDFB subbands provides total�N�dominant direction selective subbands. Local orientation map of the input image is used to determine the dominant directions and hence�N. For better sparsity properties, we design the multiresolution stage with filters having high�vanishing moments�and better frequency selectivity. Applicability of the proposed adaptive design is shown for pansharpening of multispectral images. Our proposed pansharpening approach is evaluated on images captured using QuickBird and IKONOS-2 satellites. Results obtained using the proposed approach on these datasets show considerable improvements in qualitative as well as quantitative evaluations when compared to state-of-the-art pansharpening approaches including transform-based methods.
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    Abundance estimation using discontinuity preserving and sparsity-induced priors
    (IEEE, 30-05-2019) Patel, Jignesh R; Joshi, Manjunath V; Bhatt, Jignesh S; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Patel, Jignesh R (201521011)
    Abundance estimation is used to infer the proportions of endmembers with the given endmember signatures and reflectance value at each location. In this paper, we propose a two-phase iterative approach to estimate the abundances (fractions) of materials (endmembers) from the pixels of hyperspectral images (HSIs) by using the energy minimization framework. A linear mixture model is used to define the data term. We observe that abundance maps have homogeneous regions with limited discontinuity, and they exhibit spatial redundancy. Hence, we use inhomogeneous Gaussian Markov random field (IGMRF) and sparsity-induced priors as the regularization terms. While the IGMRF prior captures the smoothness and preserves discontinuities among abundance values, the sparsity-induced prior accounts for redundancy. We calculate the IGMRF parameters at every pixel location and learn a dictionary and the sparse representation for abundances using the initial estimate in phase 1, while the final abundance maps are estimated in phase 2. In order to learn the sparsity, we use the approach based on K-singular value decomposition. Both the IGMRF and sparseness parameters are initialized using an initial estimate of abundances and refined using the two-phase iterative approach. The experiments are conducted on synthetic hyperspectral HSIs with different noise levels, as well as on two real HSIs. The results are qualitatively and quantitatively compared with state-of-the-art approaches. Experimental results demonstrate the effectiveness of the proposed approach.
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    SNR wall for generalized energy detector in the presence of noise uncertainty and fading
    (Elsevier, 01-02-2019) Captain, Kamal; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Captain, Kamal (201321003)
    The performance of energy detection (ED) degrades under low�SNR, noise uncertainty (NU) and fading. The generalized energy detector (GED) is obtained by changing the squaring operation in ED by an arbitrary positive number�. In this paper, we investigate the signal to noise ratio (SNR) wall for GED under diversity considering NU as well as fading by considering�-Law combining (pLC) and�law selection (pLS) diversity. First, the�SNR�walls considering�AWGN�channel are derived. It is shown that for pLC diversity, increasing��results in lower�SNR�wall. It is also shown that under no diversity and pLS diversity, the�SNR�wall is independent of�. The analysis is then extended to the channel with Nakagami fading where it is shown that the SNR wall increases significantly. As a byproduct of this work, we also study the effect of NU and fading on the detection performance and show that above certain value, the effect of NU is more severe when compared to the fading. The effect of��on the performance is analyzed and it is shown that the performance is the best for values of��close to 2. The performance of pLC and pLS is also compared.
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    A regularized pan-sharpening approach based on self-similarity and Gabor prior
    (Taylor & Francis, 01-01-2017) Upla, Kishor P; Joshi, Manjunath V; Khatri, Nilay; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Khatri, Nilay
    In this article, we propose a new regularization-based approach for pan-sharpening based on the concepts of self-similarity and Gabor prior. The given low spatial resolution (LR) and high spectral resolution multi-spectral (MS) image is modelled as degraded and noisy version of the unknown high spatial resolution (HR) version. Since this problem is ill-posed, we use regularization to obtain the final solution. In the proposed method, we first obtain an initial HR approximation of the unknown pan-sharpened image using self-similarity and sparse representation (SR) theory. Using self-similarity, we obtain the HR patches from the given LR observation by searching for matching patches in its coarser resolution, thereby obtaining LR�HR pairs. An SR framework is used to obtain the patch pairs for which no matches are available for the patches in LR observation. The entire set of matched HR patches constitutes initial HR approximation (initial estimate) to the final pan-sharpened image which is used to estimate the degradation matrix as used in our model. A regularization framework is then used to obtain the final solution in which we propose to use a new prior which we refer as Gabor prior that extracts the bandpass details from the registered panchromatic (Pan) image. In addition, we also include Markov random field (MRF) smoothness prior that preserves the smoothness in the final pan-sharpened image. MRF parameter is derived using the initial estimate image. The final cost function consists of data fitting term and two prior terms corresponding to Gabor and MRF. Since the derived cost function is convex, simple gradient-based method is used to obtain the final solution. The efficacy of the proposed method is evaluated by conducting the experiments on degraded as well as on un-degraded datasets of three different satellites, i.e., Ikonos-2, Quickbird, and Worldview-2. The results are compared on the basis of traditional measures as well as recently proposed quality with no reference (QNR) measure, which does not require the reference image.
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    Auto-inpainting Heritage Scenes: a Complete Framework for Detecting and Infilling Cracks in Images and Videos with Quantitative Assessment
    (Springer, 01-04-2015) Padalkar, Milind G; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; Joshi, Manjunath V; DA-IICT, Gandhinagar; Padalkar, Milind G (201121015)
    The need for preservation of cultural heritage has necessitated the research on digitally repairing the photographs of damaged monuments. In this paper, we first propose a technique for automatically detecting the cracked regions in photographs of monuments. Unlike the usual practice of manually selecting the mask for inpainting, the detected regions are supplied to an inpainting algorithm. Thus, the process of digitally repairing the cracked regions that physical objects have, using inpainting, is completely automated. The detection of cracked regions is based on comparison of patches, for which we use a measure derived from the edit distance, which is a popular string metric used in the area of text mining. Further, we extend this method to perform inpainting of video frames by making use of the scale-invariant feature transform and homography. We consider the camera to move while capturing video of the heritage site, as such videos are typically captured by novices, hobbyists and tourists. Finally, we also propose a video quality measure to quantify the temporal consistency of the inpainted video. Experiments have been carried out on videos captured from the heritage site at Hampi, India.