Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Subject: search.filters.subject.image processing

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    Adaptive Reconfigurable Architecture for Image Denoising
    (Institute of Electrical and Electronics Engineers Inc., 2015) Hegde, K.V.; Kulkarni, V.; Harshavardhan, R.; Sumam David, S.
    In this paper, we propose an adaptive reconfigurable architecture for image denoising. First part of this paper outlines an efficient noise detection hardware for Gaussian & impulse noise detection and suitable filters for denoising. With a robust noise detection method including a novel Gaussian noise detection method, we also explore the dynamic detection of noise in an image giving adaptability to the architecture for a better quality of denoising. Proposed architecture includes a decision making unit to find out the presence of noise as well as type of the noise, based on which a suitable filter is employed during run-time. An onboard microprocessor controls the reconfiguration and dataflow. Proposed architecture is tested on Xilinx Virtex-6 FPGA with localized noise and mixed noise conditions and it gives superior performance compared to the standard filters used. High quality denoising is achieved with simple filters on a reconfigurable region utilizing smaller area and lesser hardware resources. © 2015 IEEE.
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    Automated Evaluation of Attendance and Cumulative Feedback using Face Recognition
    (Institute of Electrical and Electronics Engineers Inc., 2018) Shalini, S.; Navya, R.S.; Neha, M.; Ramteke, P.B.; Koolagudi, S.G.
    Face recognition is an important technological development of this era. It is being widely used in biometric systems, gaming as well as to tag people on social media. It is also being used for attendance because the manual system is tedious and time-consuming. This paper proposes an automated attendance and cumulative feedback system based on facial expression recognition. The proposed automation system recognizes students from a recorded video of the class and captures their attendance. Local Binary Pattern Histograms (LBPH) and Eigen Face recognizers have been used for face recognition with an accuracy of 97% and 95% respectively. This paper addresses another issue of feedback of the professor by deducing genuine and cumulative feedback based on facial expressions of the students. Two methods have been proposed for deducing the feedback. One is the algorithmic method based on face recognition using confidence measure for expressions detection and the other one uses Speeded up robust features (SURF) and Support Vector Machines(SVM). The proposed methodology is observed to be in correlation with the conventional method of feedback evaluation. Copy Right © INDIACom-2018.
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    A Study on Depth Estimation from Single Image Using Neural Networks
    (Institute of Electrical and Electronics Engineers Inc., 2022) Shree, R.; Madagaonkar, S.B.; Singh, M.; Chandra, M.T.A.; Rathnamma, M.V.; Venkataramana, V.; Chandrasekaran, K.
    Depth estimation is fundamental in upcoming technology advancements like scene understanding, robot vision, intelligent driver assistance systems, and many new technologies. Estimating the depth of objects from a viewport can be achieved using various mathematical, geometrical, and stereo concepts, but the process is unaffordable and erroneous. Depth estimation from a single can be accurately done using neural networks. Although this is a challenging task, researchers around the globe have published various works. The works include different neural network standards like CNN, GANs, Encoder-Decoder. The paper analyses and examines famous works in this field of study. Later in the paper, a comparative survey of depth estimation approaches using neural networks is done. © 2022 IEEE.