A DCT domain smart vicinity reliant fragile watermarking technique for DIBR 3D-TV

This work presents a vicinity reliant intelligent fragile watermarking scheme for depth image-based rendering technique used for three-dimensional television. Depth map of a centre image is implicitly inserted in the block-based discrete cosine transform (DCT) of the same using an aggregate, which also takes into account the presence of its neighbourhood blocks. Based upon the parity of a Boolean operation on the aggregate, parity is modulated which implicitly embeds the watermark. Genetic algorithm is then utilized to select the appropriate frequency bands in the DCT domain to become eligible for watermark embedding based on imperceptibility requirements. Experimental results demonstrate the usefulness of the proposed scheme in terms of its resistance against a set of fragile watermarking attacks and its ability to detect and localize tempering attempts

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas

Learning compact hashing codes for large-scale similarity search

Retrieval of similar objects is a key component in many applications. As databases grow larger, learning compact representations for efficient storage and fast search becomes increasingly important. Moreover, these representations should preserve similarity, i.e., similar objects should have similar representations. Hashing algorithms, which encode objects into compact binary codes to preserve similarity, have demonstrated promising results in addressing these challenges. This dissertation studies the problem of learning compact hashing codes for large-scale similarity search. Specifically, we investigate two classes of approach: regularized Adaboost and signal-to-noise ratio (SNR) maximization. The regularized Adaboost builds on the classical boosting framework for hashing, while SNR maximization is a novel hashing framework with theoretical guarantee and great flexibility in designing hashing algorithms for various scenarios. The regularized Adaboost algorithm is to learn and extract binary hash codes (fingerprints) of time-varying content by filtering and quantizing perceptually significant features. The proposed algorithm extends the recent symmetric pairwise boosting (SPB) algorithm by taking feature sequence correlation into account. An information-theoretic analysis of the SPB algorithm is given, showing that each iteration of SPB maximizes a lower bound on the mutual information between matching fingerprint pairs. Based on the analysis, two practical regularizers are proposed to penalize those filters generating highly correlated filter responses. A learning-theoretic analysis of the regularized Adaboost algorithm is given. The proposed algorithm demonstrates significant performance gains over SPB for both audio and video content identification (ID) systems. SNR maximization hashing (SRN-MH) uses the SNR metric to select a set of uncorrelated projection directions, and one hash bit is extracted from each projection direction. We first motivate this approach under a Gaussian model for the underlying signals, in which case maximizing SNR is equivalent to minimizing the hashing error probability. This theoretical guarantee differentiates SNR-MH from other hashing algorithms where learning has to be carried out with a continuous relaxation of quantization functions. A globally optimal solution can be obtained by solving a generalized eigenvalue problem. Experiments on both synthetic and real datasets demonstrate the power of SNR-MH to learn compact codes. We extend SNR-MH to two different scenarios in large-scale similarity search. The first extension aims at applications with a larger bit budget. To learn longer hash codes, we propose a multi-bit per projection algorithm, called SNR multi-bit hashing (SNR-MBH), to learn longer hash codes when the number of high-SNR projections is limited. Extensive experiments demonstrate the superior performance of SNR-MBH. The second extension aims at a multi-feature setting, where more than one feature vector is available for each object. We propose two multi-feature hashing methods, SNR joint hashing (SNR-JH) and SNR selection hashing (SNR-SH). SNR-JH jointly considers all feature correlations and learns uncorrelated hash functions that maximize SNR, while SNR-SH separately learns hash functions on each individual feature and selects the final hash functions based on the SNR associated with each hash function. The proposed methods perform favorably compared to other state-of-the-art multi-feature hashing algorithms on several benchmark datasets

Blind image quality assessment: from heuristic-based to learning-based

Image quality assessment (IQA) plays an important role in numerous digital image processing applications, including image compression, image transmission, and image restoration, etc. The goal of objective IQA is to develop computational models that can predict image quality in a way being consistent with human perception. Compared with subjective quality evaluations such as psycho-visual tests, objective IQA metrics have the advantages of predicting image quality automatically and effectively in a timely manner. This thesis focuses on a particular type of objective IQA – blind IQA (BIQA), where the developed methods not only achieve objective IQA, but also are able to assess the perceptual quality of digital images without access to their pristine reference counterparts. Firstly, a novel blind image sharpness evaluator is introduced in Chapter 3, which leverages the discrepancy measures of structural degradation. Secondly, a “completely blind” quality assessment metric for gamut-mapped images is designed in Chapter 4, which does not need subjective quality scores during the model training. Thirdly, a general-purpose BIQA method is presented in Chapter 5, which can evaluate the quality of digital images without prior knowledge on the types of distortions. Finally, in Chapter 6, a deep neural network-based general-purpose BIQA method is proposed, which is fully data driven and trained in an end-to-end manner. In summary, four BIQA methods are introduced in this thesis, where the first three are heuristic-based and the last one is learning-based. Unlike heuristics-based ones, the learning-based method does not involves manually engineered feature designs