Blur Invariants for Image Recognition

Blur is an image degradation that is difficult to remove. Invariants with respect to blur offer an alternative way of a~description and recognition of blurred images without any deblurring. In this paper, we present an original unified theory of blur invariants. Unlike all previous attempts, the new theory does not require any prior knowledge of the blur type. The invariants are constructed in the Fourier domain by means of orthogonal projection operators and moment expansion is used for efficient and stable computation. It is shown that all blur invariants published earlier are just particular cases of this approach. Experimental comparison to concurrent approaches shows the advantages of the proposed theory.Comment: 15 page

Distortion Robust Biometric Recognition

abstract: Information forensics and security have come a long way in just a few years thanks to the recent advances in biometric recognition. The main challenge remains a proper design of a biometric modality that can be resilient to unconstrained conditions, such as quality distortions. This work presents a solution to face and ear recognition under unconstrained visual variations, with a main focus on recognition in the presence of blur, occlusion and additive noise distortions. First, the dissertation addresses the problem of scene variations in the presence of blur, occlusion and additive noise distortions resulting from capture, processing and transmission. Despite their excellent performance, ’deep’ methods are susceptible to visual distortions, which significantly reduce their performance. Sparse representations, on the other hand, have shown huge potential capabilities in handling problems, such as occlusion and corruption. In this work, an augmented SRC (ASRC) framework is presented to improve the performance of the Spare Representation Classifier (SRC) in the presence of blur, additive noise and block occlusion, while preserving its robustness to scene dependent variations. Different feature types are considered in the performance evaluation including image raw pixels, HoG and deep learning VGG-Face. The proposed ASRC framework is shown to outperform the conventional SRC in terms of recognition accuracy, in addition to other existing sparse-based methods and blur invariant methods at medium to high levels of distortion, when particularly used with discriminative features. In order to assess the quality of features in improving both the sparsity of the representation and the classification accuracy, a feature sparse coding and classification index (FSCCI) is proposed and used for feature ranking and selection within both the SRC and ASRC frameworks. The second part of the dissertation presents a method for unconstrained ear recognition using deep learning features. The unconstrained ear recognition is performed using transfer learning with deep neural networks (DNNs) as a feature extractor followed by a shallow classifier. Data augmentation is used to improve the recognition performance by augmenting the training dataset with image transformations. The recognition performance of the feature extraction models is compared with an ensemble of fine-tuned networks. The results show that, in the case where long training time is not desirable or a large amount of data is not available, the features from pre-trained DNNs can be used with a shallow classifier to give a comparable recognition accuracy to the fine-tuned networks.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Extracting geometric information from images with the novel Self Affine Feature Transform

Based on our research, the Self Affine Feature Transform (SAFT) was introduced as it extracts quantities which hold information of the edges in the investigated image region. This paper gives details on algorithms which extract various geometric information from the SAFT matrix. As different image types should be analysed differently, a classification procedure must be performed first. The main contribution of this paper is to describe this classification in details. Information extraction is applied for solving different 2-dimensional image processing tasks, amongst them the detection of con­ver­gent lines, circles, ellipses, parabolae and hiperbolae or localizing corners of calibration grids in a robust and accurate manner

What is hidden in the darkness? Characterization of AlphaFold structural space

The recent public release of the latest version of the AlphaFold database has given us access to over 200 million predicted protein structures. We use a textquotedblleftshape-mertextquotedblright approach, a structural fragmentation method analogous to sequence k-mers, to describe these structures and look for novelties - both in terms of proteins with rare or novel structural composition and possible functional annotation of under-studied proteins. Data and code will be made available at https://github.com/TurtleTools/afdb-shapemer-darknes

An Autoencoder-Based Image Descriptor for Image Matching and Retrieval

Local image features are used in many computer vision applications. Many point detectors and descriptors have been proposed in recent years; however, creation of effective descriptors is still a topic of research. The Scale Invariant Feature Transform (SIFT) developed by David Lowe is widely used in image matching and image retrieval. SIFT detects interest points in an image based on Scale-Space analysis, which is invariant to change in image scale. A SIFT descriptor contains gradient information about an image patch centered at a point of interest. SIFT is found to provide a high matching rate, is robust to image transformations; however, it is found to be slow in image matching/retrieval. Autoencoder is a method for representation learning and is used in this project to construct a low-dimensional representation of a high-dimensional data while preserving the structure and geometry of the data. In many computer vision tasks, the high dimensionality of input data means a high computational cost. The main motivation in this project is to improve the speed and the distinctness of SIFT descriptors. To achieve this, a new descriptor is proposed that is based on Autoencoder. Our newly generated descriptors can reduce the size and complexity of SIFT descriptors, reducing the time required in image matching and image retrieval

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.Comment: Main paper: 14 pages, supplemental material: 12 pages, main paper appeared in IEEE Transaction on Information Forensics and Securit