Person:
Mandal, Srimanta

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Srimanta Mandal

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Faculty

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079-68261621

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Image Processing, Computer Vision, Machine Learning

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Biography

Dr. Srimanta Mandal received his Ph.D. from IIT Mandi, India, in 2017. He has been a postdoctoral fellow with the Department of Electrical Engineering, IIT Madras, India, from 2017 to 2018. Since October 2018, he has been with DA-IICT, Gandhinagar, where he is currently an associate professor. During his PhD, he received a travel grant from IIT Mandi for presenting work at the International Conference on Image Processing 2014, Paris, France. So far, he supervised 20 master�s students in their dissertation/project work and co-supervised 1 PhD student. He has published several articles in national/international journals and conferences. He received the best paper award (runner-up) at the Indian Conference on Computer Vision, Graphics and Image Processing 2018. He served as a reviewer for various conferences and journals. He is a senior member of IEEE and served as an executive committee member of the IEEE SPS Gujarat chapter from 2019 to 2022. He served as an Advisory Group Member, Department of Technical Education, Gujarat. He is a life member of IUPRAI and ISRS. His research interests include image processing, computer vision, and machine learning.

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2020 - 20224

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Now showing 1 - 4 of 4
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    Local Proximity for Enhanced Visibility in Haze
    (IEEE, 01-01-2020) Mandal, Srimantaand; Rajagopalan, A N; Mandal, Srimanta; Mandal, Srimanta; Mandal, Srimanta; Mandal, Srimanta; Mandal, Srimanta; DA-IICT, Gandhinagar
    Atmospheric medium often constrains the visibility of outdoor scenes due to scattering of light rays. This causes attenuation in the irradiance reaching the imaging device along with an additive component to render a hazy effect in the image. The visibility is further reduced for poorly illuminated scenes. The attenuation becomes wavelength dependent in underwater scenario, causing undesired color cast along with hazy effect. In order to suppress the effect of different atmospheric/underwater conditions such as haze and to enhance the contrast of such images, we reformulate local haziness in a generalized manner. The parameters are estimated by harnessing the similarity of patches within a local neighborhood. Unlike existing methods, our approach is developed based on the assumption that for outdoor scenes the depth of patches changes gradually in a local neighborhood surrounding the patch. This change in depth can be approximated by patch similarity in that neighborhood. As the attenuation in irradiance of an image in presence of atmospheric medium relies on the depth of the scene, the coefficients related to the attenuation are estimated from the weights of patch similarity. The additive haze effect is deduced using non-local mean of the patch. Our experimental results demonstrate the effectiveness of our approach in reducing the haze component as well as in enhancing the image under different conditions of haze (daytime, nighttime, and underwater).
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    Discrimination of multi-crop scenarios with polarimetric SAR data using Wishart mixture model
    (SPIE, 20-08-2021) Chaudhari, Nilam; Mitra, Suman; Chirakkal, Sanid; Mandal, Srimanta; Putrevu, Deepak; Misra, Arundhati; DA-IICT, Gandhinagar; Chaudhari, Nilam (201911063)
    Discrimination of crop varieties spanned over heterogeneous agriculture land is a vital application of polarimetric SAR images for agriculture monitoring and assessment. The covariance matrix of polarimetric SAR images is observed to follow a complex Wishart distribution for major classification tasks. It is true for homogeneous regions, but for heterogeneous regions, the covariance matrix follows a mixture of multiple Wishart distributions. We aim to improve the classification accuracy when the terrain under observation is heterogeneous. For this purpose, Wishart mixture model is employed along with expectation-maximization (EM) algorithm for parameter estimation. Elbow method helps us to devise the number of mixtures. The convergence of the EM algorithm depends on the choice of initial points. So, to improve the robustness of the model, different initialization approaches, such as random,�K-means, and global�K-means, are embedded in the EM algorithm. Further, the degrees of freedom is one of the crucial parameters of Wishart distribution. Therefore, the impact of different degrees of freedom is analyzed on classification accuracy. The method that is equipped with initialization technique along with optimum degrees of freedom is assessed using three full polarimetric SAR data sets of agriculture lands. The first two are benchmark data sets of Flevoland, Netherlands, region acquired by AIRSAR sensor, and third is our study area of Mysore, India, acquired by RADARSAT-2 sensor.
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    Edge-Preserving classification of polarimetric SAR images using Wishart distribution and conditional random field
    (Taylor & Francis, 08-04-2022) Chaudhari, Nilam; Mitra, Suman; Mandal, Srimanta; Chirakkal, Sanid; Putrevu, Deepak; Misra, Arundhati; DA-IICT, Gandhinagar; Chaudhari, Nilam (201911063)
    Classification of polarimetric SAR images into different ground covers has important applications in fields such as land mapping, agriculture monitoring, and assessment. The Wishart supervised classifier is one of the most widely used and general purpose classifier for polarimetric SAR data. However, it is a pixel-based classifier, so the performance is greatly affected by inherent speckle noise. The impact of speckle noise can be reduced by considering the spatial information from neighbouring pixels for classification tasks. In this paper, we aim to improve classification results by incorporating spatial-contextual information along with preservation of significant details such as edges and micro-regions. For this purpose, a conditional random field (CRF) based model is proposed for polarimetric SAR data along with Wishart and Wishart mixture model (WMM) classifiers, namely Wishart-CRF and WMM-CRF, to perform the classification. The model is compared with the Markov random field (MRF) based model as well as neural network-based models. The results are analysed in terms of accuracy and preservation of details such as edges and micro-regions. The model is assessed using three full polarimetric SAR benchmark data sets. The CRF model exhibits better classification results by significantly reducing the noise and preserving the finer details of edges and small regions.
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    Handwritten Digit Recognition Using Bayesian ResNet
    (Springer, 02-08-2021) Mhasakar, Purva; Trivedi, Prapti; Mandal, Srimanta; Mitra, Suman; DA-IICT, Gandhinagar; Mhasakar, Purva (201601082); Trivedi, Prapti (201601020)
    The problem of handwritten digit recognition has seen various developments in the recent times, especially in neural network domain. The methods based on neural network work quite effectively for the seen classes of data by providing deterministic results. However, these methods tend to behave in similar fashion even for unseen class of data. For example, a neural network trained on English language digits will give a deterministic prediction even when tested on digits of other languages. Hence, it is required to predict uncertainty for such methods in this scenario. In this paper, we employ Bayesian inference into the existing ResNet18 framework to bring out uncertainty for handwritten digit recognition when there is a new class of test digit. We term the new architecture as B-ResNet. The novel B-ResNet is first of its kind to be investigated for the handwritten digit recognition. Various experiments on datasets of English, Devanagari, Gujarati, Bengali digits and their all possible combinations demonstrate the efficiency and performance of the B-ResNet for hand written digit recognition.