Papers by Venkatachalam Chandrasekaran
International Conference on Information and Knowledge Engineering, 2009
For any non-linear fractional-order differential equation, the numerical simulation is a good way... more For any non-linear fractional-order differential equation, the numerical simulation is a good way to approximate the trajectory of such systems. Fractional-order chaotic attractors is given much attention in the current research because of its wide range of applications. In this paper, we propose a new numerical solution for the fractionalorder Lorenz attractor. As an application of the fractionalorder Lorenz attractor, we propose a novel image encryption scheme using the fractional order Lorenz attractor. The image is viewed as a 3D cube which demonstrates that the confusion and diffusion can be integrated as one process. The fractional-order Lorenz attractor is used directly for image encryption through the generation of confusion matrix. The proposed scheme is demonstrated to be secure and efficient against the commonly known attacks.
Current challenge in the inpainting domain is in regard to large object removal and region-fillin... more Current challenge in the inpainting domain is in regard to large object removal and region-filling thereafter. Problems get even more complex when the inpainting domain is in colour. In the literature, several attempts have been made to achieve perceptually satisfying results which are technically and semantically valid. All these techniques can be grouped under statistical, PDE-based and exemplar-based methods. Among these techniques, the exemplar-based approaches are gaining popularity on account of their computational efficiency and visually pleasing results. Original contribution in this direction is due to a seminal paper by Criminisi et al. This has led to a number of novel contributions in terms of patch filling prioritization and associated metrics to measure colour and structure. In this paper, we propose a fast and simple technique based on a gradient function to evaluate the filling order prioritization. Despite its simplicity, experimental results demonstrate superior pe...
IEEE Winter Conference on Applications of Computer Vision, 2014
Designing a robust image local descriptor for the purpose of pattern recognition and classificati... more Designing a robust image local descriptor for the purpose of pattern recognition and classification has been an active area of research to date. In this paper a robust and computationally efficient Color moments augmented Cumulative Histogram-based Image Local Descriptor called COLOR CHILD has been proposed.The above descriptor has outperformed all the image local descriptors of parametric and non-parametric types quoted as recently as April 2013 in the literature for texture classification. We have demonstrated the efficacy of the proposed descriptor on a variety of benchmark texture databases such as KTH-TIPS2-a, KTH-TIPS2-b, and CuReT under both noiseless and noisy conditions.
In this paper, we propose an Undeniable Blind Signature scheme (UBSS) based on isogenies between ... more In this paper, we propose an Undeniable Blind Signature scheme (UBSS) based on isogenies between supersingular elliptic curves. The proposed UBSS is an extension of the Jao-Soukharev undeniable signature scheme [16]. We formalize the notion of a UBSS by giving the formal definition. We then study its properties along with the pros and cons. Based on this, we provide a couple of its applications. We then state the isogeny problems in a more general form and discuss their computational hardnesses. Finally, we prove that the proposed scheme is secure in the presence of a quantum adversary under certain assumptions.
International Journal of Computational Methods, Sep 1, 2010
In recent years, a variety of chaos-based image encryption schemes have been proposed and demonst... more In recent years, a variety of chaos-based image encryption schemes have been proposed and demonstrated due to the unpredictable and random-look nature of chaotic systems. In particular, use of chaotic maps such as Cat and Baker maps for image encryption has been extensively studied. However, chaotic flows have not been used directly for image encryption except for key generation. In this paper, a generic framework for image encryption using chaotic flows has been proposed. The proposed scheme is extended by a random sequence of chaotic flows selected from a pool for the generation of confusion matrices. It is demonstrated that this scheme results in a more secure, robust and efficient mechanism against commonly known attacks.
A hybrid method for image super-resolution consisting of steering kernel regression (SKR) and exa... more A hybrid method for image super-resolution consisting of steering kernel regression (SKR) and example based super-resolution (EBSR) techniques has been proposed. In this model the output of SKR is given as the input to the EBSR module. It is observed that though the image super-resolution performed by SKR gives a reasonable result, in terms of perceptual quality, the regression techniques have inherent disadvantage of generating artifacts. EBSR on the other hand augments the image with high frequency information to the image, thereby sharpening the edges. In this paper, we demonstrate that the proposed hybrid scheme performs better than the individual methods described above.
The amount of information that can be hidden in an image without any visually noticeable differen... more The amount of information that can be hidden in an image without any visually noticeable difference and retrieval of this embedded information are the difficulties in watermarking techniques. In this paper we present a novel algorithm for embedding and extracting large digital watermarks in images. This method is inspired by Cox's watermarking strategy. Unlike embedding an image or binary watermarks that are smaller than the host image being watermarked, in our algorithm we demonstrate by embedding an audio watermark which is much larger than the host image to be watermarked. The size of the watermarked image increases only by a fraction after watermarking. The positions where the watermarks are to be embedded, intermediate frequency coefficients and the sequence of the transformation orders comprise the key. The experimental results have shown that the proposed algorithm is of good imperceptibility and security. It is also robust against common attacks like rotation, resizing, filtering and even cropping.
Fractional derivative based techniques have been proposed for preprocessing of digital images. Al... more Fractional derivative based techniques have been proposed for preprocessing of digital images. Although these techniques address the texture enhancement and other issues to a certain extent, none of them have proposed a method of determining the fractional order adaptively. In this paper, we propose a Grunwald-Letnikov derivative based fractional derivative mask for image contrast enhancement. The proposed mask is multidirectional thus enhancing the image in several directions in one pass. The regularisation based prediction network learns from the training set of images and determines the fractional order based on the statistics of the image at hand. Also the blur reduction is achieved in a controlled fashion as the fractional order is predicted according to the desired blur improvement. Experimental results with the comparative blur metric show the effectiveness of the proposed novel filter on a wide range of images.
International Conference on Image Processing, Computer Vision, and Pattern Recognition, 2008
In one of the methods of multi-frame superresolution, a high resolution video is reconstructed fr... more In one of the methods of multi-frame superresolution, a high resolution video is reconstructed from a low resolution video of a scene by adding the high frequency components estimated from the sub-pixel shift between the frames using pyramidal tracking and iterative back projection algorithms. In contrast, super resolving a single frame low resolution image is called Example-based superresolution (EBSR). This is achieved by recovering the missing high frequency contents from a large number of example image patches in a database. In this paper we propose a novel hybrid approach to construct a super resolved video sequence which uses the example-based method for the preprocessing of low resolution video frames as input to the multi-frame super resolution. The approach to use EBSR in the preprocessing stage of low resolution frames acts as an image model error correction process. In this paper, we demonstrate that the proposed hybrid technique yields better results than the chosen stand-alone multi-frame technique.
2013 Fourth National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG), 2013
Designing a robust image local descriptor for the purpose of pattern recognition and classificati... more Designing a robust image local descriptor for the purpose of pattern recognition and classification has been an active area of research. Towards this end, a number of local descriptors based on Weber's law have been proposed recently. Notable among them are Weber Local Descriptor (WLD), Weber Local Binary Pattern (WLBP) and Gabor Weber Local Descriptor (GWLD). Experiments reveal their inability to classify patterns under noisy environments. Our analysis indicates that the components of the WLD: differential excitation and orientation are to be redesigned for robustness and computational efficiency.
2008 IEEE 8th International Conference on Computer and Information Technology Workshops, 2008
Most block-cipher image encryption schemes based on Chaos theory have independent modules for con... more Most block-cipher image encryption schemes based on Chaos theory have independent modules for confusion and diffusion processes. None of the schemes thus far use chaos theory in the diffusion modules -thus not utilizing the capabilities of chaos to the fullest extent. We can do better: we integrate these mechanisms into a single step, thus making the encryption process efficient. This paper presents three novelties: (a) we extend 2D images to 3D by using grayscale image intensities in 8-bit binary form (b) we embed the diffusion mechanism into confusion by applying the 3D Baker map based confusion algorithm. Thus, the diffusion process is accomplished by a permutation of binary bits in the third dimension, eliminating the need for a separate diffusion process and (c) we extend the proposed method to color images by using the 24-bit color information. Color image encryption is usually performed by encrypting each channel independently and then combining these to get the encrypted image. We demonstrate that with this simplistic approach, decrypting even a single channel would reveal reasonable information contained in the image. In our approach, this drawback is eliminated because of the inherent dependence between the data contained in all the channels, thus highlighting the inherent superiority of the proposed algorithm for color image security.
… International Conference on …, 2008
Chaos theory has been applied extensively in the past decade to cryptography. In particular, high... more Chaos theory has been applied extensively in the past decade to cryptography. In particular, higher dimensional maps like the 3D Baker and Cat maps have been proposed for chaos based image encryption. In these schemes how-ever, the Baker map traverses the image in a ...
INTERNATIONAL JOURNAL OF …, 2010
AbstractIn recent years, a variety of chaos-based image encryption schemes have been proposed an... more AbstractIn recent years, a variety of chaos-based image encryption schemes have been proposed and demonstrated due to the unpredictable and random-look nature of chaotic systems. In particular, use of chaotic maps such as Cat and Baker maps for image encryption has ...
IAPR conference on …, 2007
for localization of OD. We have tested our method on 100 normal retinal images (achieving 100 % s... more for localization of OD. We have tested our method on 100 normal retinal images (achieving 100 % success) and 232 diseased retinal images (achieving 75% success), thereby demonstrating a significant improvement over two other existing methods.
Current challenge in the inpainting domain is in regard to large object removal and region-fillin... more Current challenge in the inpainting domain is in regard to large object removal and region-filling thereafter. Problems get even more complex when the inpainting domain is in colour. In the literature, several attempts have been made to achieve perceptually satisfying results which are technically and semantically valid. All these techniques can be grouped under statistical, PDE-based and exemplar-based methods. Among these techniques, the exemplar-based approaches are gaining popularity on account of their computational efficiency and visually pleasing results. Original contribution in this direction is due to a seminal paper by Criminisi et al. This has led to a number of novel contributions in terms of patch filling prioritization and associated metrics to measure colour and structure. In this paper, we propose a fast and simple technique based on a gradient function to evaluate the filling order prioritization. Despite its simplicity, experimental results demonstrate superior performance over all the recent advances quoted in the literature.
Image inpainting is considered as a predictive process to compute the missing image data without ... more Image inpainting is considered as a predictive process to compute the missing image data without introducing undesirable artifacts. Most of the existing methods in the literature work very well for small regions but introduce blur for large holes. In this paper, we propose a novel unified framework for affine and flip invariant inpainting of color images. The proposed method combines structural similarity index measure, an improved version of color angular radial transform, frequency domain-based image registration and Dr. Kekre's LUV space based blending. It searches for best candidate regions that are similar to the neighbourhood of the inpainting domain either in the same image or in the large database in terms of its structure, color and texture simultaneously thereby improving the prediction accuracy. Experimental results indicate perceptually satisfactory results.
Image inpainting refers to the process of reconstructing the original image from a damaged one in... more Image inpainting refers to the process of reconstructing the original image from a damaged one in a visually plausible way. We propose a new gradientbased algorithm for exemplar-based inpainting by making use of L ∞ norm. We implement the most time consuming step of the algorithm on the GPU and compare the serial execution timings against the parallel execution timings. The parallel implementation has an average speedup of 14 over the serial implementation. The results obtained from our approach are perceptually on par and in many cases better than the state-of-the-art approaches to date.
Image inpainting is considered as a predictive process to compute the missing image data without ... more Image inpainting is considered as a predictive process to compute the missing image data without introducing any undesirable artifacts. Most of the existing methods in the literature for this purpose work very well for small regions but introduce blur for large holes. In this paper the work done is an extension of a previous work where the novel framework for affine and flip invariant inpainting of color images was proposed which combines structural similarity index measure, an improved version of color angular radial transform, frequency domain based image registration and Kekre's LUV space based blending. We now extend it by including histogram of oriented gradients to preserve the texture and parallelizing the implementation using OpenCV on multicores and GPUs. The parallel implementation of the original algorithm achieved a speed up of 11 on multicores and up to 13 on a GPU and around 18 on the multicores and a GPU hybrid platform. The experimental results are found out to be more computationally efficient, fast, and more visibly plausible.
Detecting and analyzing prominent facial regions forms the fundamental building block in most fac... more Detecting and analyzing prominent facial regions forms the fundamental building block in most face recognition systems. Prominent regions such as left and right eyes, tip of the nose, mouth, etc. are localized to derive an overall representation of the face being recognized. In this paper, we present a method for deriving a set of compact translation-, scale- and rotation-invariant canonical templates which could be used on a large database. In contrast to conventional gray scale templates, these are of the 2D gradient field type. Facial feature detection is based on evidential reasoning from the measures of belief and disbelief estimations. The above method is demonstrated on a facial image database of size 137 using only 9-left, 9-right and 9-nose tip canonical templates
For any non-linear fractional-order differential equation, the numerical simulation is a good way... more For any non-linear fractional-order differential equation, the numerical simulation is a good way to approximate the trajectory of such systems. Fractional-order chaotic attractors is given much attention in the current research because of its wide range of applications. In this paper, we propose a new numerical solution for the fractionalorder Lorenz attractor. As an application of the fractionalorder Lorenz attractor, we propose a novel image encryption scheme using the fractional order Lorenz attractor. The image is viewed as a 3D cube which demonstrates that the confusion and diffusion can be integrated as one process. The fractional-order Lorenz attractor is used directly for image encryption through the generation of confusion matrix. The proposed scheme is demonstrated to be secure and efficient against the commonly known attacks.
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Papers by Venkatachalam Chandrasekaran