Digital encoding of black and white facsimile signals

As the costs of digital signal processing and memory hardware are decreasing each year compared to those of transmission, it is increasingly economical to apply sophisticated source encoding techniques to reduce the transmission time for facsimile documents. With this intent, information lossy encoding schemes have been investigated in which the encoder is divided into two stages. Firstly, preprocessing, which removes redundant information from the original documents, and secondly, actual encoding of the preprocessed documents. [Continues.

Visual Quality Assessment and Blur Detection Based on the Transform of Gradient Magnitudes

abstract: Digital imaging and image processing technologies have revolutionized the way in which we capture, store, receive, view, utilize, and share images. In image-based applications, through different processing stages (e.g., acquisition, compression, and transmission), images are subjected to different types of distortions which degrade their visual quality. Image Quality Assessment (IQA) attempts to use computational models to automatically evaluate and estimate the image quality in accordance with subjective evaluations. Moreover, with the fast development of computer vision techniques, it is important in practice to extract and understand the information contained in blurred images or regions. The work in this dissertation focuses on reduced-reference visual quality assessment of images and textures, as well as perceptual-based spatially-varying blur detection. A training-free low-cost Reduced-Reference IQA (RRIQA) method is proposed. The proposed method requires a very small number of reduced-reference (RR) features. Extensive experiments performed on different benchmark databases demonstrate that the proposed RRIQA method, delivers highly competitive performance as compared with the state-of-the-art RRIQA models for both natural and texture images. In the context of texture, the effect of texture granularity on the quality of synthesized textures is studied. Moreover, two RR objective visual quality assessment methods that quantify the perceived quality of synthesized textures are proposed. Performance evaluations on two synthesized texture databases demonstrate that the proposed RR metrics outperforms full-reference (FR), no-reference (NR), and RR state-of-the-art quality metrics in predicting the perceived visual quality of the synthesized textures. Last but not least, an effective approach to address the spatially-varying blur detection problem from a single image without requiring any knowledge about the blur type, level, or camera settings is proposed. The evaluations of the proposed approach on a diverse sets of blurry images with different blur types, levels, and content demonstrate that the proposed algorithm performs favorably against the state-of-the-art methods qualitatively and quantitatively.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Texture Structure Analysis

abstract: Texture analysis plays an important role in applications like automated pattern inspection, image and video compression, content-based image retrieval, remote-sensing, medical imaging and document processing, to name a few. Texture Structure Analysis is the process of studying the structure present in the textures. This structure can be expressed in terms of perceived regularity. Our human visual system (HVS) uses the perceived regularity as one of the important pre-attentive cues in low-level image understanding. Similar to the HVS, image processing and computer vision systems can make fast and efficient decisions if they can quantify this regularity automatically. In this work, the problem of quantifying the degree of perceived regularity when looking at an arbitrary texture is introduced and addressed. One key contribution of this work is in proposing an objective no-reference perceptual texture regularity metric based on visual saliency. Other key contributions include an adaptive texture synthesis method based on texture regularity, and a low-complexity reduced-reference visual quality metric for assessing the quality of synthesized textures. In order to use the best performing visual attention model on textures, the performance of the most popular visual attention models to predict the visual saliency on textures is evaluated. Since there is no publicly available database with ground-truth saliency maps on images with exclusive texture content, a new eye-tracking database is systematically built. Using the Visual Saliency Map (VSM) generated by the best visual attention model, the proposed texture regularity metric is computed. The proposed metric is based on the observation that VSM characteristics differ between textures of differing regularity. The proposed texture regularity metric is based on two texture regularity scores, namely a textural similarity score and a spatial distribution score. In order to evaluate the performance of the proposed regularity metric, a texture regularity database called RegTEX, is built as a part of this work. It is shown through subjective testing that the proposed metric has a strong correlation with the Mean Opinion Score (MOS) for the perceived regularity of textures. The proposed method is also shown to be robust to geometric and photometric transformations and outperforms some of the popular texture regularity metrics in predicting the perceived regularity. The impact of the proposed metric to improve the performance of many image-processing applications is also presented. The influence of the perceived texture regularity on the perceptual quality of synthesized textures is demonstrated through building a synthesized textures database named SynTEX. It is shown through subjective testing that textures with different degrees of perceived regularities exhibit different degrees of vulnerability to artifacts resulting from different texture synthesis approaches. This work also proposes an algorithm for adaptively selecting the appropriate texture synthesis method based on the perceived regularity of the original texture. A reduced-reference texture quality metric for texture synthesis is also proposed as part of this work. The metric is based on the change in perceived regularity and the change in perceived granularity between the original and the synthesized textures. The perceived granularity is quantified through a new granularity metric that is proposed in this work. It is shown through subjective testing that the proposed quality metric, using just 2 parameters, has a strong correlation with the MOS for the fidelity of synthesized textures and outperforms the state-of-the-art full-reference quality metrics on 3 different texture databases. Finally, the ability of the proposed regularity metric in predicting the perceived degradation of textures due to compression and blur artifacts is also established.Dissertation/ThesisPh.D. Electrical Engineering 201

Continuous Modeling of 3D Building Rooftops From Airborne LIDAR and Imagery

In recent years, a number of mega-cities have provided 3D photorealistic virtual models to support the decisions making process for maintaining the cities' infrastructure and environment more effectively. 3D virtual city models are static snap-shots of the environment and represent the status quo at the time of their data acquisition. However, cities are dynamic system that continuously change over time. Accordingly, their virtual representation need to be regularly updated in a timely manner to allow for accurate analysis and simulated results that decisions are based upon. The concept of "continuous city modeling" is to progressively reconstruct city models by accommodating their changes recognized in spatio-temporal domain, while preserving unchanged structures. However, developing a universal intelligent machine enabling continuous modeling still remains a challenging task. Therefore, this thesis proposes a novel research framework for continuously reconstructing 3D building rooftops using multi-sensor data. For achieving this goal, we first proposes a 3D building rooftop modeling method using airborne LiDAR data. The main focus is on the implementation of an implicit regularization method which impose a data-driven building regularity to noisy boundaries of roof planes for reconstructing 3D building rooftop models. The implicit regularization process is implemented in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). Secondly, we propose a context-based geometric hashing method to align newly acquired image data with existing building models. The novelty is the use of context features to achieve robust and accurate matching results. Thirdly, the existing building models are refined by newly proposed sequential fusion method. The main advantage of the proposed method is its ability to progressively refine modeling errors frequently observed in LiDAR-driven building models. The refinement process is conducted in the framework of MDL combined with HAT. Markov Chain Monte Carlo (MDMC) coupled with Simulated Annealing (SA) is employed to perform a global optimization. The results demonstrates that the proposed continuous rooftop modeling methods show a promising aspects to support various critical decisions by not only reconstructing 3D rooftop models accurately, but also by updating the models using multi-sensor data

Semiautomatic mammographic parenchymal patterns classification using multiple statistical features.

RATIONALE AND OBJECTIVES: Our project was to investigate a complete methodology for the semiautomatic assessment of digital mammograms according to their density, an indicator known to be correlated to breast cancer risk. The BI-RADS four-grade density scale is usually employed by radiologists for reporting breast density, but it allows for a certain degree of subjective input, and an objective qualification of density has therefore often been reported hard to assess. The goal of this study was to design an objective technique for determining breast BI-RADS density. MATERIALS AND METHODS: The proposed semiautomatic method makes use of complementary pattern recognition techniques to describe manually selected regions of interest (ROIs) in the breast with 36 statistical features. Three different classifiers based on a linear discriminant analysis or Bayesian theories were designed and tested on a database consisting of 1408 ROIs from 88 patients, using a leave-one-ROI-out technique. Classifications in optimal feature subspaces with lower dimensionality and reduction to a two-class problem were studied as well. RESULTS: Comparison with a reference established by the classifications of three radiologists shows excellent performance of the classifiers, even though extremely dense breasts continue to remain more difficult to classify accurately. For the two best classifiers, the exact agreement percentages are 76% and above, and weighted kappa values are 0.78 and 0.83. Furthermore, classification in lower dimensional spaces and two-class problems give excellent results. CONCLUSION: The proposed semiautomatic classifiers method provides an objective and reproducible method for characterizing breast density, especially for the two-class case. It represents a simple and valuable tool that could be used in screening programs, training, education, or for optimizing image processing in diagnostic tasks

Automatic verification of road databases using multiple road models

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Automated image-based quality control of molecularly imprinted polymer films

We present results of applying a feature extraction process to images of coatings of molecularly imprinted polymers (MIPs) coatings on glass substrates for defect detec- tion. Geometric features such as MIP side lengths, aspect ratio, internal angles, edge regularity, and edge strength are obtained by using Hough transforms, and Canny edge detection. A Self Organizing Map (SOM) is used for classification of texture of MIP surfaces. The SOM is trained on a data set comprised of images of manufactured MIPs. The raw images are first processed using Hough transforms and Canny edge detection to extract just the MIP-coated portion of the surface, allowing for surface area estimation and reduction of training set size. The training data set is comprised of 20-dimensional feature vectors, each of which is calculated from a single section of a gray scale image of a MIP. Haralick textures are among the quantifiers used as feature vector components. The training data is then processed using principal component analysis to reduce the number of dimensions of the data set. After training, the SOM is capable of classifying texture, including defects