INSTITUTIONAL DIGITAL REPOSITORY

Browsing by Author "Murala, S."

Browsing by Author "Murala, S."

Sort by: Order: Results:

  • Multi-dimensional multi-directional mask maximum edge pattern for bio-medical image retrieval 
    Galshetwar, G.M.; Waghmare, L.M.; Gonde, A.B.; Murala, S. (2018-12-28)
    Authors have proposed novel multi-dimensional multi-directional mask maximum edge patterns for the bio-medical image retrieval. Standard local binary patterns encode relationship of neighbor pixels with center pixel. Local ...
  • Multi-frame recurrent adversarial network for moving object segmentation 
    Patil, P. W.; Dudhane, A.; Murala, S. (2021-12-06)
    Moving object segmentation (MOS) in different practical scenarios like weather degraded, dynamic background, etc. videos is a challenging and high demanding task for various computer vision applications. Existing ...
  • Multichannel distributed local pattern for content based indexing and retrieval 
    Mathur, S.; Chaudhary, M.; Verma, H.; Mandal, M.; Vipparthi, S.K.; Murala, S. (2019-05-16)
    A novel color feature descriptor, Multichannel Distributed Local Pattern (MDLP) is proposed in this manuscript. The MDLP combines the salient features of both local binary and local mesh patterns in the neighborhood. ...
  • Multi‐frame based adversarial learning approach for video surveillance 
    Patil, P. W.; Dudhane, A.; Chaudhary, S.; Murala, S. (2021-12-19)
    Foreground-background segmentation (FBS) is one of the prime tasks for automated video-based applications like traffic analysis and surveillance. The different practical scenarios like weather degraded videos, irregular ...
  • A novel feature descriptor for image retrieval by combining modifed color histogram and diagonally symmetric co‑occurrence texture pattern 
    Bhunia, A. K.; Bhattacharyya1, A.; Banerjee, P.; Roy, P. P.; Murala, S. (2021-07-03)
    In this paper, we have proposed a novel feature descriptors combining color and texture information collectively. In our proposed color descriptor component, the inter-channel relationship between Hue (H) and Saturation ...
  • NTIRE 2019 image dehazing challenge report 
    Ancuti, C. O.; Ancuti, C.; Timofte, R.; Gool, L. V.; Zhang, L.; Yang, M. H.; Guo, T.; Li, X.; Cherukuri, V.; Monga, V.; Jiang, H.; Yang, S.; Liu, Y.; Qu, X.; Wan, P.; Park, D.; Chun, S. Y.; Hong, M.; Huang, J.; Chen, Y.; Chen, S.; Wang, B.; Michelini, P. N.; Liu, H.; Zhu, D.; Liu, J.; Santra, S.; Mondal, R.; Chanda, B.; Morales, P.; Klinghoffer, T.; Quan, L. M.; Kim, Y. G.; Liang, X.; Li, R.; Pan, J.; Tang, J.; Purohit, K.; Suin, M.; Rajagopalan, A. N.; Schettini, R.; Bianco, S.; Piccoli, F.; Cusano, C.; Celona, L.; Hwang, S.; Ma, Y. S.; Byun, H.; Murala, S.; Dudhane, A.; Aulakh, H.; Zheng, T.; Zhang, T.; Qin, W.; Zhou, R.; Wang, S.; Tarel, J. P.; Wang, C.; Wu, J. (2021-08-21)
    This paper reviews the second NTIRE challenge on image dehazing (restoration of rich details in hazy image) with focus on proposed solutions and results. The training data consists from 55 hazy images (with dense haze ...
  • Pose guided dynamic image network for human action recognition in person centric videos 
    Chaudhary, S.; Dudhane, A.; Patil, P.; Murala, S. (2021-08-20)
    The most emerging concerns in computer vision are size of data to process and privacy preserving of the end user. Camera sensors are all around us these days, recording and analysing our day-to-day activities. In this ...
  • Progressive Subtractive Recurrent Lightweight Network for Video Deraining 
    Kulkarni, A.; Patil, P.W.; Murala, S. (2022-06-28)
    Presence of rainy artifacts severely degrade the overall visual quality of a video and tend to overlap with the useful information present in the video frames. This degraded video affects the effectiveness of many automated ...
  • Pseudo decoder guided light-weight architecture for image inpainting 
    Phutke, S.S.; Murala, S. (2022-12-15)
    Image inpainting is one of the most important and widely used approaches where input image is synthesized at the missing regions. This has various applications like undesired object removal, virtual garment shopping, etc. ...
  • A real-time fine-grained visual monitoring system for retail store auditing 
    Chaudhary, S.; Murala, S. (2018-12-31)
    Automated visual analysis of the object is of prime importance to realize the real-time concept of the internet of things. In this paper, we proposed a real-time fine grained visual analytics system for tracing the ...
  • Regional adaptive affinitive patterns (RADAP) with logical operators for facial expression recognition 
    Mandal, M.; Verma, M.; Mathur, S.; Vipparthi, S. K.; Murala, S.; Kumar, D. K. (2021-08-21)
    Automated facial expression recognition plays a significant role in the study of human behaviour analysis. In this study, the authors propose a robust feature descriptor named regional adaptive affinitive patterns (RADAP) ...
  • RescueNet: an unpaired GAN for brain tumor segmentation 
    Nema, S.; Dudhane, A.; Murala, S.; Naidu, S. (2020-03-13)
    Even with proper acquisition of brain tumor images, the accurate and reliable segmentation of tumors in brain is a complicated job. Automatic segmentation become possible with development of deep learning algorithms that ...
  • RI-GAN: an end-to-end network for single image haze removal 
    Dudhane, A.; Aulakh, H. S.; Murala, S. (2021-08-21)
    The presence of the haze or fog particles in the atmosphere causes visibility degradation in the captured scene. Most of the initial approaches anticipate the transmission map of the hazy scene, airlight component and ...
  • Robust unseen video understanding for various surveillance environments 
    Patil, P.; Singh, J.; Hambarde, P.; Kulkarni, A.; Chaudhary, S.; Murala, S. (2022-12-15)
    Automated video-based applications are a highly demanding technique from a security perspective, where detection of moving objects i.e., moving object segmentation (MOS) is performed. Therefore, we have proposed an effective ...
  • RYF-Net: deep fusion network for single image haze removal 
    Dudhane, A.; Murala, S. (2020-03-13)
    Haze removal from a single image is a challenging task. Estimation of accurate scene transmission map (TrMap) is the key to reconstruct the haze-free scene. In this paper, we propose a convolutional neural network based ...
  • S2DNet: depth estimation from single image and sparse samples 
    Hambarde, P.; Murala, S. (2021-07-04)
    Depth prediction from single image is a challenging task due to the intra scale ambiguity and unavailability of prior information. The prediction of an unambiguous depth from single RGB image is very important aspect for ...
  • Single image depth estimation using deep adversarial training 
    Hambarde, P.; Dudhane, A.; Murala, S. (2021-08-20)
    Scene understanding is an active area of research in computer vision that encompasses several different problems. The LiDARs and stereo depth sensor have their own restrictions such as light sensitiveness, power ...
  • Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval 
    Murala, S.; Wu, Q.M. J. (2016-08-02)
    In this paper, we propose a new algorithm using spherical symmetric three dimensional local ternary patterns (SS-3D-LTP) for natural, texture and biomedical image retrieval applications. The existing local binary patterns ...
  • TSNet: deep network for human action recognition in hazy videos 
    Chaudhar, S.; Murala, S. (2021-08-27)
    The all-weather intelligent surveillance system is the prime challenge for computer vision researchers. The surveillance is mostly done to analyze the human activity in a particular region. Several extreme weather ...
  • An unified recurrent video object segmentation framework for various surveillance environments 
    Patil, P.W.; Dudhane, A.; Kulkarni, A.; Murala, S.; Gonde, A.B.; Gupta, S. (2022-09-03)
    Moving object segmentation (MOS) in videos received considerable attention because of its broad security-based applications like robotics, outdoor video surveillance, self-driving cars, etc. The current prevailing algorithms ...