Subjective and objective quality assessment of ancient degraded documents

Archiving, restoration and analysis of damaged manuscripts have been largely increased in recent decades. Usually, these documents are physically degraded because of aging and improper handing. They also cannot be processed manually because a massive volume of these documents exist in libraries and archives around the world. Therefore, automatic methodologies are needed to preserve and to process their content. These documents are usually processed through their images. Degraded document image processing is a difficult task mainly because of the existing physical degradations. While it can be very difficult to accurately locate and remove such distortions, analyzing the severity and type(s) of these distortions is feasible. This analysis provides useful information on the type and severity of degradations with a number of applications. The main contributions of this thesis are to propose models for objectively assessing the physical condition of document images and to classify their degradations. In this thesis, three datasets of degraded document images along with the subjective ratings for each image are developed. In addition, three no-reference document image quality assessment (NR-DIQA) metrics are proposed for historical and medieval document images. It should be mentioned that degraded medieval document images are a subset of the historical document images and may contain both graphical and textual content. Finally, we propose a degradation classification model in order to identify common distortion types in old document images. Essentially, existing no reference image quality assessment (NR-IQA) metrics are not designed to assess physical document distortions. In the first contribution, we propose the first dataset of degraded document images along with the human opinion scores for each document image. This dataset is introduced to evaluate the quality of historical document images. We also propose an objective NR-DIQA metric based on the statistics of the mean subtracted contrast normalized (MSCN) coefficients computed from segmented layers of each document image. The segmentation into four layers of foreground and background is done based on an analysis of the log-Gabor filters. This segmentation is based on the assumption that the sensitivity of the human visual system (HVS) is different at the locations of text and non-text. Experimental results show that the proposed metric has comparable or better performance than the state-of-the-art metrics, while it has a moderate complexity. Degradation identification and quality assessment can complement each other to provide information on both type and severity of degradations in document images. Therefore, we introduced, in the second contribution, a multi-distortion historical document image database that can be used for the research on quality assessment of degraded documents as well as degradation classification. The developed dataset contains historical document images which are classified into four categories based on their distortion types, namely, paper translucency, stain, readers’ annotations, and worn holes. An efficient NR-DIQA metric is then proposed based on three sets of spatial and frequency image features extracted from two layers of text and non-text. In addition, these features are used to estimate the probability of the four aforementioned physical distortions for the first time in the literature. Both proposed quality assessment and degradation classification models deliver a very promising performance. Finally, we develop in the third contribution a dataset and a quality assessment metric for degraded medieval document (DMD) images. This type of degraded images contains both textual and pictorial information. The introduced DMD dataset is the first dataset in its category that also provides human ratings. Also, we propose a new no-reference metric in order to evaluate the quality of DMD images in the developed dataset. The proposed metric is based on the extraction of several statistical features from three layers of text, non-text, and graphics. The segmentation is based on color saliency with assumption that pictorial parts are colorful. It also follows HVS that gives different weights to each layer. The experimental results validate the effectiveness of the proposed NR-DIQA strategy for DMD images

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE

Referenceless image quality assessment by saliency, color-texture energy, and gradient boosting machines

Abstract In most practical multimedia applications, processes are used to manipulate the image content. These processes include compression, transmission, or restoration techniques, which often create distortions that may be visible to human subjects. The design of algorithms that can estimate the visual similarity between a distorted image and its non-distorted version, as perceived by a human viewer, can lead to significant improvements in these processes. Therefore, over the last decades, researchers have been developing quality metrics (i.e., algorithms) that estimate the quality of images in multimedia applications. These metrics can make use of either the full pristine content (full-reference metrics) or only of the distorted image (referenceless metric). This paper introduces a novel referenceless image quality assessment (RIQA) metric, which provides significant improvements when compared to other state-of-the-art methods. The proposed method combines statistics of the opposite color local variance pattern (OC-LVP) descriptor with statistics of the opposite color local salient pattern (OC-LSP) descriptor. Both OC-LVP and OC-LSP descriptors, which are proposed in this paper, are extensions of the opposite color local binary pattern (OC-LBP) operator. Statistics of these operators generate features that are mapped into subjective quality scores using a machine-learning approach. Specifically, to fit a predictive model, features are used as input to a gradient boosting machine (GBM). Results show that the proposed method is robust and accurate, outperforming other state-of-the-art RIQA methods