Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Automatic video object plane segmentation and super resolution: A novel approach(2009) Pais, A.R.; Pasupuleti, N.; Gidnavar, R.; D'Souza, J.Detecting, segmenting and super resolving of moving object is an important subject in computer visual analysis and surveillance. This paper discusses automatic VOP (Video Object Plane) segmentation and super resolving of VOP. We have proposed a new method for automatic generation of VOP by using block matching and quantization techniques. Edge model based approach is used to generate the HR (High Resolution) image with sharper VOP boundaries. Experimental results show that the proper VOP is segmented and super resolution of VOP yields lesser blurring compared to bicubic, bilinear interpolation schemes. Copyright © 2009 by IICAI.Item An Improved and Secure Visual Secret Sharing (VSS) scheme for Medical Images(Institute of Electrical and Electronics Engineers Inc., 2019) Mhala, N.C.; Pais, A.R.Nowadays, medical information is being shared over the communication networks due to ease of technology. The patient's medical information has to be securely communicated over the network for automatic diagnosis. Most of the communication networks are prone to attacks from an intruder thus compromising the security of patients data. Therefore, there is a need to transmit medical images securely over the network. Visual secret sharing scheme can be used to transmit the medical images over the network securely. Visual Secret Sharing (VSS) scheme generates multiple shares to share secret information among n participants. To recover the secret information, all shares should be stacked together. In our previous work [9], we proposed a VSS based technique to recover secret images with the contrast of 70-80% known as Randomized Visual Secret Sharing (RVSS) scheme. However, RVSS scheme suffers from problems like 1) Generation of blocking artifacts in the recovered images. 2) It recovers medical images with a maximum contrast of 30-40%, hence it is not suitable for medical images.In this paper, we propose a modified RVSS scheme to recover the medical images with improved contrast. The proposed scheme introduces the idea of using super-resolution concept to improve the contrast of reconstructed medical images. The reconstruction quality of the medical images is evaluated using Human Visual System (HVS) based parameters. Additionally, the performance of the proposed system is evaluated using the existing Computer Aided Diagnosis (CAD) systems. The experimental results showed that the proposed system is able to reconstruct the secret image with the contrast of almost 85-90% and similarity of almost 77%. AIso, the reconstructed images using the proposed system achieves the similar classification accuracy as that of existing CAD systems. © 2019 IEEE.Item A Novel Fingerprint Image Enhancement based on Super Resolution(Institute of Electrical and Electronics Engineers Inc., 2020) Muhammed, A.; Pais, A.R.Fingerprint is a most common and broadly accepted biometric trait used for personal authentication. In fingerprint-based authentication, the feature extraction module extract features, and these extracted characteristics are used for authentication. In fingerprints, the feature extraction module heavily depends on the status of the image. However, in practice, always getting a good quality fingerprint image is not possible. Moreover, a notable number of fingerprints collected are of poor quality. The accurate extraction of fingerprint characteristics from a lesser quality fingerprint image is a challenging problem. Fingerprint enhancement is introduced to resolve this issue. Hence in this paper, we introduce a fingerprint enhancement technique using a Deep Convolution Neural Network (DCNN), which improves image quality. The proposed method consists of super-resolution, followed by filtering and enhancement. The proposed method provides better results as compared with the conventional fingerprint enhancement methods. The experimental results determine that the proposed strategy improves the visual clarity of low-quality images and reduces the error rates during the fingerprint matching. © 2020 IEEE.