Comparative Analysis of Techniques Used to Detect Copy-Move Tampering for Real-World Electronic Images

Evolution of high computational powerful computers, easy availability of several innovative editing software package and high-definition quality-based image capturing tools follows to effortless result in producing image forgery. Though, threats for security and misinterpretation of digital images and scenes have been observed to be happened since a long period and also a lot of research has been established in developing diverse techniques to authenticate the digital images. On the contrary, the research in this region is not limited to checking the validity of digital photos but also to exploring the specific signs of distortion or forgery. This analysis would not require additional prior information of intrinsic content of corresponding digital image or prior embedding of watermarks. In this paper, recent growth in the area of digital image tampering identification have been discussed along with benchmarking study has been shown with qualitative and quantitative results. With variety of methodologies and concepts, different applications of forgery detection have been discussed with corresponding outcomes especially using machine and deep learning methods in order to develop efficient automated forgery detection system. The future applications and development of advanced soft-computing based techniques in digital image forgery tampering has been discussed

Adaptive Methods for Robust Document Image Understanding

A vast amount of digital document material is continuously being produced as part of major digitization efforts around the world. In this context, generic and efficient automatic solutions for document image understanding represent a stringent necessity. We propose a generic framework for document image understanding systems, usable for practically any document types available in digital form. Following the introduced workflow, we shift our attention to each of the following processing stages in turn: quality assurance, image enhancement, color reduction and binarization, skew and orientation detection, page segmentation and logical layout analysis. We review the state of the art in each area, identify current defficiencies, point out promising directions and give specific guidelines for future investigation. We address some of the identified issues by means of novel algorithmic solutions putting special focus on generality, computational efficiency and the exploitation of all available sources of information. More specifically, we introduce the following original methods: a fully automatic detection of color reference targets in digitized material, accurate foreground extraction from color historical documents, font enhancement for hot metal typesetted prints, a theoretically optimal solution for the document binarization problem from both computational complexity- and threshold selection point of view, a layout-independent skew and orientation detection, a robust and versatile page segmentation method, a semi-automatic front page detection algorithm and a complete framework for article segmentation in periodical publications. The proposed methods are experimentally evaluated on large datasets consisting of real-life heterogeneous document scans. The obtained results show that a document understanding system combining these modules is able to robustly process a wide variety of documents with good overall accuracy

Adaptive threshold optimisation for colour-based lip segmentation in automatic lip-reading systems

A thesis submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in ful lment of the requirements for the degree of Doctor of Philosophy. Johannesburg, September 2016Having survived the ordeal of a laryngectomy, the patient must come to terms with the resulting loss of speech. With recent advances in portable computing power, automatic lip-reading (ALR) may become a viable approach to voice restoration. This thesis addresses the image processing aspect of ALR, and focuses three contributions to colour-based lip segmentation. The rst contribution concerns the colour transform to enhance the contrast between the lips and skin. This thesis presents the most comprehensive study to date by measuring the overlap between lip and skin histograms for 33 di erent colour transforms. The hue component of HSV obtains the lowest overlap of 6:15%, and results show that selecting the correct transform can increase the segmentation accuracy by up to three times. The second contribution is the development of a new lip segmentation algorithm that utilises the best colour transforms from the comparative study. The algorithm is tested on 895 images and achieves percentage overlap (OL) of 92:23% and segmentation error (SE) of 7:39 %. The third contribution focuses on the impact of the histogram threshold on the segmentation accuracy, and introduces a novel technique called Adaptive Threshold Optimisation (ATO) to select a better threshold value. The rst stage of ATO incorporates -SVR to train the lip shape model. ATO then uses feedback of shape information to validate and optimise the threshold. After applying ATO, the SE decreases from 7:65% to 6:50%, corresponding to an absolute improvement of 1:15 pp or relative improvement of 15:1%. While this thesis concerns lip segmentation in particular, ATO is a threshold selection technique that can be used in various segmentation applications.MT201

Development and implementation of image fusion algorithms based on wavelets

Image fusion is a process of blending the complementary as well as the common features of a set of images, to generate a resultant image with superior information content in terms of subjective as well as objective analysis point of view. The objective of this research work is to develop some novel image fusion algorithms and their applications in various fields such as crack detection, multi spectra sensor image fusion, medical image fusion and edge detection of multi-focus images etc. The first part of this research work deals with a novel crack detection technique based on Non-Destructive Testing (NDT) for cracks in walls suppressing the diversity and complexity of wall images. It follows different edge tracking algorithms such as Hyperbolic Tangent (HBT) filtering and canny edge detection algorithm. The second part of this research work deals with a novel edge detection approach for multi-focused images by means of complex wavelets based image fusion. An illumination invariant hyperbolic tangent filter (HBT) is applied followed by an adaptive thresholding to get the real edges. The shift invariance and directionally selective diagonal filtering as well as the ease of implementation of Dual-Tree Complex Wavelet Transform (DT-CWT) ensure robust sub band fusion. It helps in avoiding the ringing artefacts that are more pronounced in Discrete Wavelet Transform (DWT). The fusion using DT-CWT also solves the problem of low contrast and blocking effects. In the third part, an improved DT-CWT based image fusion technique has been developed to compose a resultant image with better perceptual as well as quantitative image quality indices. A bilateral sharpness based weighting scheme has been implemented for the high frequency coefficients taking both gradient and its phase coherence in accoun

Colour morphological sieves for scale-space image processing

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