Mehul S. Raval

Bio: Mehul S. Raval is an academic researcher from Ahmedabad University. The author has contributed to research in topics: Digital watermarking & Watermark. The author has an hindex of 10, co-authored 65 publications receiving 574 citations. Previous affiliations of Mehul S. Raval include Pandit Deendayal Petroleum University & Sarvajanik College of Engineering and Technology.

Discrete wavelet transform based multiple watermarking scheme

Abstract: The low-frequency embedding of the watermark increases the robustness with respect to image distortions that have low pass characteristics like filtering, lossy compression, geometrical distortions. On the other hand, oblivious schemes with low-frequency watermarks are more sensitive to modifications of the histogram, such as contrast/brightness adjustment, gamma correction, histogram equalization, and cropping. Watermarks inserted into middle and high frequencies are typically less robust to low-pass filtering lossy compression and small geometric deformations of the image, but are extremely robust with respect to noise adding, nonlinear deformations of the gray scale. It is understandable that the advantages and disadvantages of low and middle-to-high frequency watermarks are complementary. It appears that by embedding both watermarks into one image, one could achieve extremely high robustness properties with respect to a large spectrum of image processing operations. The above reasoning leads to proposed technique of embedding multiple watermarks into the low frequency and high frequency bands of discrete wavelet transform.

A robust and secure watermarking scheme based on singular values replacement

Abstract: Digital watermarking is an application associated with copyright protection. Any digital object can be used as a carrier to carry information. If the information is related to object then it is known as a watermark which can be visible or invisible. In the era of digital information, there are multiple danger zones like copyright and integrity violations, of digital object. In case of any dispute during rights violation, content creator can prove ownership by recovering the watermark. Two most important prerequisites for an efficient watermarking scheme are robustness and security. Watermark must be robust and recoverable even if a part of content is altered by one or more attacks like compression, filtering, geometric distortions, resizing, etc. In this work, we propose a blind watermarking scheme based on the discrete wavelet transform (DWT) and singular value decomposition (SVD). Singular values (SV’s) of high frequency (HH) band are used to optimize perceptual transparency and robustness constraints. Although most of the SVD-based schemes prove to be robust, little attention has been paid to their security aspect. Therefore, we introduce a signature-based authentication mechanism at the decoder to improve security. Resulting blind watermarking scheme is secure and robust.

Disease Detection and Severity Estimation in Cotton Plant from Unconstrained Images

Abstract: The primary focus of this paper is to detect disease and estimate its stage for a cotton plant using images. Most disease symptoms are reflected on the cotton leaf. Unlike earlier approaches, the novelty of the proposal lies in processing images captured under uncontrolled conditions in the field using normal or a mobile phone camera by an untrained person. Such field images have a cluttered background making leaf segmentation very challenging. The proposed work use two cascaded classifiers. Using local statistical features, first classifier segments leaf from the background. Then using hue and luminance from HSV colour space another classifier is trained to detect disease and find its stage. The developed algorithm is a generalised as it can be applied for any disease. However as a showcase, we detect Grey Mildew, widely prevalent fungal disease in North Gujarat, India.

Brain Tumor Segmentation and Survival Prediction

Abstract: The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset. Three-layers deep encoder-decoder architecture is used along with dense connection at the encoder part to propagate the information from the coarse layers to deep layers. This architecture is used to train three tumor sub-components separately. Sub-component training weights are initialized with whole tumor weights to get the localization of the tumor within the brain. In the end, three segmentation results were merged to get the entire tumor segmentation. Dice Similarity of training dataset with focal loss implementation for whole tumor, tumor core, and enhancing tumor is 0.92, 0.90, and 0.79, respectively. Radiomic features from the segmentation results predict survival. Along with these features, age and statistical features are used to predict the overall survival of patients using random forest regressors. The overall survival prediction method outperformed the other methods for the validation dataset on the leaderboard with 58.6% accuracy. This finding is consistent with the performance on the test set of BraTS 2019 with 57.9% accuracy.

Deep Learning for Automated Brain Tumor Segmentation in MRI Images

Abstract: Early detection of a brain tumor increases life expectancy and survival chances of a patient. Experts use modalities like magnetic resonance imaging (MRI) and computerized tomography (CT) to locate brain tumors in images. Medical professionals carefully study patterns of the soft tissues, viz. gray matter, white matter, and cerebrospinal fluid within brain images to trace possible abnormality. A manual image analysis task is time consuming, nonreproducible and highly dependent on the individual's skill. On the other hand, computer-assisted analysis of a medical image helps experts in quick decision making, generates reproducible results and electronic patient record, improves diagnosis, and helps in treatment planning. This chapter covers state-of-the-art review for automated brain tumor segmentation and focuses on supervised form of learning. Initially, the chapter covers conventional methods but later shifts focal point to uncover deep neural network for brain tumor segmentation. Deep neural networks have an excellent capability of automatic feature discovery and they also fight against curse of the dimensionality. This chapter covers brain tumor segmentation using MRI images. Any one of the four MRI modalities, namely, T1, T2, T1c, and FLAIR image, is given as an input to a method, which segments out the tumor. The approaches for brain tumor segmentation are analyzed and their comparative study is presented on the publicly available dataset. The chapter also presents various open source tools for brain tumor segmentation and quality metrics to quantify result. Overall purpose of this chapter is to provide comprehensive picture to a reader about the learning approaches in brain tumor segmentation, available tools, and the quality metrics for segmentation.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Inductive Teaching and Learning Methods: Definitions, Comparisons, and Research Bases

Abstract: Traditional engineering instruction is deductive, beginning with theories and progressing to the applications of those theories Alternative teaching approaches are more inductive Topics are introduced by presenting specific observations, case studies or problems, and theories are taught or the students are helped to discover them only after the need to know them has been established This study reviews several of the most commonly used inductive teaching methods, including inquiry learning, problem-based learning, project-based learning, case-based teaching, discovery learning, and just-in-time teaching The paper defines each method, highlights commonalities and specific differences, and reviews research on the effectiveness of the methods While the strength of the evidence varies from one method to another, inductive methods are consistently found to be at least equal to, and in general more effective than, traditional deductive methods for achieving a broad range of learning outcomes

A survey of digital image watermarking techniques

Abstract: Watermarking, which belong to the information hiding field, has seen a lot of research interest. There is a lot of work begin conducted in different branches in this field. Steganography is used for secret communication, whereas watermarking is used for content protection, copyright management, content authentication and tamper detection. In this paper we present a detailed survey of existing and newly proposed steganographic and watermarking techniques. We classify the techniques based on different domains in which data is embedded. We limit the survey to images only.

Robust DWT-SVD domain image watermarking: embedding data in all frequencies

Abstract: Protection of digital multimedia content has become an increasingly important issue for content owners and service providers. As watermarking is identified as a major technology to achieve copyright protection, the relevant literature includes several distinct approaches for embedding data into a multimedia element (primarily images, audio, and video). Because of its growing popularity, the Discrete Wavelet Transform (DWT) is commonly used in recent watermarking schemes. In a DWT-based scheme, the DWT coefficients are modified with the data that represents the watermark. In this paper, we present a hybrid scheme based on DWT and Singular Value Decomposition (SVD). After decomposing the cover image into four bands, we apply the SVD to each band, and embed the same watermark data by modifying the singular values. Modification in all frequencies allows the development of a watermarking scheme that is robust to a wide range of attacks.