Processing of Byzantine Neume Notation in Ancient Historical Manuscripts

This article presents the principal results of the doctoral thesis “Recognition of neume notation in historical documents” by Lasko Laskov (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), successfully defended before the Specialized Academic Council for Informatics and Mathematical Modelling on 07 June 2010.

Hough Transform Approach to Identification of Flare Stars in Multi-exposure Plate Images

ACM Computing Classification System (1998): I.2.8 , I.2.10, I.5.1, J.2.

Application of Wavelet Decomposition to Document Line Segmentation

ACM Computing Classification System (1998): I.7, I.7.5.

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.

Optical communication based on the periodic nonlinear Fourier transform signal processing

In this work we introduce the periodic nonlinear Fourier transform (PNFT) and propose a proof-of-concept communication system based on it by using a simple waveform with known nonlinear spectrum (NS). We study the performance (addressing the bit-error-rate (BER), as a function of the propagation distance) of the transmission system based on the use of the PNFT processing method and show the benefits of the latter approach. By analysing our simulation results for the system with lumped amplification, we demonstrate the decent potential of the new processing method.

Changing visual value of deciduous species in the climate change

The article deals with the changing visual value of deciduous species. Due to climate change, the climatic patterns found on the plants’ growing area may change. Therefore, foliage of deciduous trees changes itscolor in the fall season witha different timing and intensity. This shift can modify the functional, ornamental and ecological value of these plants in the fall season, which is of special interest in the context of landscape design. However, this effect of climate change hasn’t been examined in terms of landscape architecture yet.In the article we are looking for deciduous species that can be appropriate subjectsforresearch, we are giving suggestions for choosing the right location for a future research and proposing available resources of satellite images, that can provide the basis for evaluation of leaf coloring. We also review already existing methods for calculating the degree of fall leaf coloring.We propose a novel method of satellite image processing to evaluate the coloring of a stand. Leaf Coloring Index (LCI) shows the leaf color’s relation to the color realms. LCI is appropriate for setting up a phenological model based onclimate data in a future research. Based on future climate models, the change of the examined stand’s visual value can be predicted. The results might affect the future use of plant species in landscape architecture.

Extended principal component analysis and its applications to image analysis

The objectives of this research are to analyze and develop a modified Principal Component Analysis (PCA) and to develop a two-dimensional PCA with applications in image processing. PCA is a classical multivariate technique where its mathematical treatment is purely based on the eigensystem of positive-definite symmetric matrices. Its main function is to statistically transform a set of correlated variables to a new set of uncorrelated variables over $\IR\sp{n}$ by retaining most of the variations present in the original variables.^ The variances of the Principal Components (PCs) obtained from the modified PCA form a correlation matrix of the original variables. The decomposition of this correlation matrix into a diagonal matrix produces a set of orthonormal basis that can be used to linearly transform the given PCs. It is this linear transformation that reproduces the original variables. The two-dimensional PCA can be devised as a two successive of one-dimensional PCA. It can be shown that, for an $m\times n$ matrix, the PCs obtained from the two-dimensional PCA are the singular values of that matrix.^ In this research, several applications for image analysis based on PCA are developed, i.e., edge detection, feature extraction, and multi-resolution PCA decomposition and reconstruction. ^

A framework for automatic feature extraction from airborne light detection and ranging data

Recent advances in airborne Light Detection and Ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. Airborne LIDAR systems usually return a 3-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. This technology is becoming a primary method for extracting information of different kinds of geometrical objects, such as high-resolution digital terrain models (DTMs), buildings and trees, etc. In the past decade, LIDAR gets more and more interest from researchers in the field of remote sensing and GIS. Compared to the traditional data sources, such as aerial photography and satellite images, LIDAR measurements are not influenced by sun shadow and relief displacement. However, voluminous data pose a new challenge for automated extraction the geometrical information from LIDAR measurements because many raster image processing techniques cannot be directly applied to irregularly spaced LIDAR points. ^ In this dissertation, a framework is proposed to filter out information about different kinds of geometrical objects, such as terrain and buildings from LIDAR automatically. They are essential to numerous applications such as flood modeling, landslide prediction and hurricane animation. The framework consists of several intuitive algorithms. Firstly, a progressive morphological filter was developed to detect non-ground LIDAR measurements. By gradually increasing the window size and elevation difference threshold of the filter, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Then, building measurements are identified from no-ground measurements using a region growing algorithm based on the plane-fitting technique. Raw footprints for segmented building measurements are derived by connecting boundary points and are further simplified and adjusted by several proposed operations to remove noise, which is caused by irregularly spaced LIDAR measurements. To reconstruct 3D building models, the raw 2D topology of each building is first extracted and then further adjusted. Since the adjusting operations for simple building models do not work well on 2D topology, 2D snake algorithm is proposed to adjust 2D topology. The 2D snake algorithm consists of newly defined energy functions for topology adjusting and a linear algorithm to find the minimal energy value of 2D snake problems. Data sets from urbanized areas including large institutional, commercial, and small residential buildings were employed to test the proposed framework. The results demonstrated that the proposed framework achieves a very good performance. ^

Dynamic image precompensation for improving visual performance of computer users with ocular aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

Análise computacional de imagens de lâminas delgadas na predição dos parâmetros hidráulicos porosidade e condutividade hidráulica do aquífero Barreiras-RN

The understanding of the occurrence and flow of groundwater in the subsurface is of fundamental importance in the exploitation of water, just like knowledge of all associated hydrogeological context. These factors are primarily controlled by geometry of a certain pore system, given the nature of sedimentary aquifers. Thus, the microstructural characterization, as the interconnectivity of the system, it is essential to know the macro properties porosity and permeability of reservoir rock, in which can be done on a statistical characterization by twodimensional analysis. The latter is being held on a computing platform, using image thin sections of reservoir rock, allowing the prediction of the properties effective porosity and hydraulic conductivity. For Barreiras Aquifer to obtain such parameters derived primarily from the interpretation of tests of aquifers, a practice that usually involves a fairly complex logistics in terms of equipment and personnel required in addition to high cost of operation. Thus, the analysis and digital image processing is presented as an alternative tool for the characterization of hydraulic parameters, showing up as a practical and inexpensive method. This methodology is based on a flowchart work involving sampling, preparation of thin sections and their respective images, segmentation and geometric characterization, three-dimensional reconstruction and flow simulation. In this research, computational image analysis of thin sections of rocks has shown that aquifer storage coefficients ranging from 0,035 to 0,12 with an average of 0,076, while its hydrogeological substrate (associated with the top of the carbonate sequence outcropping not region) presents effective porosities of the order of 2%. For the transport regime, it is evidenced that the methodology presents results below of those found in the bibliographic data relating to hydraulic conductivity, mean values of 1,04 x10-6 m/s, with fluctuations between 2,94 x10-6 m/s and 3,61x10-8 m/s, probably due to the larger scale study and the heterogeneity of the medium studied.

Modelagem dos efeitos da irradiação luminosa no cérebro de camundongos e rastreamento de neurônios durante experimentos de microscopia de fluorescência

The fluorescent proteins are an essential tool in many fields of biology, since they allow us to watch the development of structures and dynamic processes of cells in living tissue, with the aid of fluorescence microscopy. Optogenectics is another technique that is currently widely used in Neuroscience. In general, this technique allows to activate/deactivate neurons with the radiation of certain wavelengths on the cells that have ion channels sensitive to light, at the same time that can be used with fluorescent proteins. This dissertation has two main objectives. Initially, we study the interaction of light radiation and mice brain tissue to be applied in optogenetic experiments. In this step, we model absorption and scattering effects using mice brain tissue characteristics and Kubelka-Munk theory, for specific wavelengths, as a function of light penetration depth (distance) within the tissue. Furthermore, we model temperature variations using the finite element method to solve Pennes’ bioheat equation, with the aid of COMSOL Multiphysics Modeling Software 4.4, where we simulate protocols of light stimulation tipically used in optogenetics. Subsequently, we develop some computational algorithms to reduce the exposure of neuron cells to the light radiation necessary for the visualization of their emitted fluorescence. At this stage, we describe the image processing techniques developed to be used in fluorescence microscopy to reduce the exposure of the brain samples to continuous light, which is responsible for fluorochrome excitation. The developed techniques are able to track, in real time, a region of interest (ROI) and replace the fluorescence emitted by the cells by a virtual mask, as a result of the overlay of the tracked ROI and the fluorescence information previously stored, preserving cell location, independently of the time exposure to fluorescent light. In summary, this dissertation intends to investigate and describe the effects of light radiation in brain tissue, within the context of Optogenetics, in addition to providing a computational tool to be used in fluorescence microscopy experiments to reduce image bleaching and photodamage due to the intense exposure of fluorescent cells to light radiation.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Sistema inteligente para o processamento de imagens digitais intrabucais oclusais

Several are the areas in which digital images are used in solving day-to-day problems. In medicine the use of computer systems have improved the diagnosis and medical interpretations. In dentistry it’s not different, increasingly procedures assisted by computers have support dentists in their tasks. Set in this context, an area of dentistry known as public oral health is responsible for diagnosis and oral health treatment of a population. To this end, oral visual inspections are held in order to obtain oral health status information of a given population. From this collection of information, also known as epidemiological survey, the dentist can plan and evaluate taken actions for the different problems identified. This procedure has limiting factors, such as a limited number of qualified professionals to perform these tasks, different diagnoses interpretations among other factors. Given this context came the ideia of using intelligent systems techniques in supporting carrying out these tasks. Thus, it was proposed in this paper the development of an intelligent system able to segment, count and classify teeth from occlusal intraoral digital photographic images. The proposed system makes combined use of machine learning techniques and digital image processing. We first carried out a color-based segmentation on regions of interest, teeth and non teeth, in the images through the use of Support Vector Machine. After identifying these regions were used techniques based on morphological operators such as erosion and transformed watershed for counting and detecting the boundaries of the teeth, respectively. With the border detection of teeth was possible to calculate the Fourier descriptors for their shape and the position descriptors. Then the teeth were classified according to their types through the use of the SVM from the method one-against-all used in multiclass problem. The multiclass classification problem has been approached in two different ways. In the first approach we have considered three class types: molar, premolar and non teeth, while the second approach were considered five class types: molar, premolar, canine, incisor and non teeth. The system presented a satisfactory performance in the segmenting, counting and classification of teeth present in the images.

Image retrieval on the Honeycomb Image Browser

Efficient and effective approaches of dealing with the vast amount of visual information available nowadays are highly sought after. This is particularly the case for image collections, both personal and commercial. Due to the magnitude of these ever expanding image repositories, annotation of all images images is infeasible, and search in such an image collection therefore becomes inherently difficult. Although content-based image retrieval techniques have shown much potential, such approaches also suffer from various problems making it difficult to adopt them in practice. In this paper, we follow a different approach, namely that of browsing image databases for image retrieval. In our Honeycomb Image Browser, large image databases are visualised on a hexagonal lattice with image thumbnails occupying hexagons. Arranged in a space filling manner, visually similar images are located close together enabling large image datasets to be navigated in a hierarchical manner. Various browsing tools are incorporated to allow for interactive exploration of the database. Experimental results confirm that our approach affords efficient image retrieval. © 2010 IEEE.

Improved image analysis based corrosion assessment using preprocessing techniques

The importance of non-destructive techniques (NDT) in structural health monitoring programmes is being critically felt in the recent times. The quality of the measured data, often affected by various environmental conditions can be a guiding factor in terms usefulness and prediction efficiencies of the various detection and monitoring methods used in this regard. Often, a preprocessing of the acquired data in relation to the affecting environmental parameters can improve the information quality and lead towards a significantly more efficient and correct prediction process. The improvement can be directly related to the final decision making policy about a structure or a network of structures and is compatible with general probabilistic frameworks of such assessment and decision making programmes. This paper considers a preprocessing technique employed for an image analysis based structural health monitoring methodology to identify sub-marine pitting corrosion in the presence of variable luminosity, contrast and noise affecting the quality of images. A preprocessing of the gray-level threshold of the various images is observed to bring about a significant improvement in terms of damage detection as compared to an automatically computed gray-level threshold. The case dependent adjustments of the threshold enable to obtain the best possible information from an existing image. The corresponding improvements are observed in a qualitative manner in the present study.