On the way to solve lighting problems in underwater imaging

A major obstacle to processing images of the ocean floor comes from the absorption and scattering effects of the light in the aquatic environment. Due to the absorption of the natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion, and, as the vehicle moves, induce shadows in the scene. For this reason, the first step towards application of standard computer vision techniques to underwater imaging requires dealing first with these lighting problems. This paper analyses and compares existing methodologies to deal with low-contrast, nonuniform illumination in underwater image sequences. The reviewed techniques include: (i) study of the illumination-reflectance model, (ii) local histogram equalization, (iii) homomorphic filtering, and, (iv) subtraction of the illumination field. Several experiments on real data have been conducted to compare the different approaches

Segmentation and grid generation for numerical simulations of vad connections with patient-specific data

Objectives: We are interested in the numerical simulation of the anastomotic region comprised between outflow canula of LVAD and the aorta. Segmenta¬tion, geometry reconstruction and grid generation from patient-specific data remain an issue because of the variable quality of DICOM images, in particular CT-scan (e.g. metallic noise of the device, non-aortic contrast phase). We pro¬pose a general framework to overcome this problem and create suitable grids for numerical simulations.Methods: Preliminary treatment of images is performed by reducing the level window and enhancing the contrast of the greyscale image using contrast-limited adaptive histogram equalization. A gradient anisotropic diffusion filter is applied to reduce the noise. Then, watershed segmentation algorithms and mathematical morphology filters allow reconstructing the patient geometry. This is done using the InsightToolKit library (www.itk.org). Finally the Vascular Model¬ing ToolKit (www.vmtk.org) and gmsh (www.geuz.org/gmsh) are used to create the meshes for the fluid (blood) and structure (arterial wall, outflow canula) and to a priori identify the boundary layers. The method is tested on five different patients with left ventricular assistance and who underwent a CT-scan exam.Results: This method produced good results in four patients. The anastomosis area is recovered and the generated grids are suitable for numerical simulations. In one patient the method failed to produce a good segmentation because of the small dimension of the aortic arch with respect to the image resolution.Conclusions: The described framework allows the use of data that could not be otherwise segmented by standard automatic segmentation tools. In particular the computational grids that have been generated are suitable for simulations that take into account fluid-structure interactions. Finally the presented method features a good reproducibility and fast application.

On the way to solve lighting problems in underwater imaging

A major obstacle to processing images of the ocean floor comes from the absorption and scattering effects of the light in the aquatic environment. Due to the absorption of the natural light, underwater vehicles often require artificial light sources attached to them to provide the adequate illumination. Unfortunately, these flashlights tend to illuminate the scene in a nonuniform fashion, and, as the vehicle moves, induce shadows in the scene. For this reason, the first step towards application of standard computer vision techniques to underwater imaging requires dealing first with these lighting problems. This paper analyses and compares existing methodologies to deal with low-contrast, nonuniform illumination in underwater image sequences. The reviewed techniques include: (i) study of the illumination-reflectance model, (ii) local histogram equalization, (iii) homomorphic filtering, and, (iv) subtraction of the illumination field. Several experiments on real data have been conducted to compare the different approaches

Fingerprint Segmentation

In this thesis, a new algorithm has been proposed to segment the foreground of the fingerprint from the image under consideration. The algorithm uses three features, mean, variance and coherence. Based on these features, a rule system is built to help the algorithm to efficiently segment the image. In addition, the proposed algorithm combine split and merge with modified Otsu. Both enhancements techniques such as Gaussian filter and histogram equalization are applied to enhance and improve the quality of the image. Finally, a post processing technique is implemented to counter the undesirable effect in the segmented image. Fingerprint recognition system is one of the oldest recognition systems in biometrics techniques. Everyone have a unique and unchangeable fingerprint. Based on this uniqueness and distinctness, fingerprint identification has been used in many applications for a long period. A fingerprint image is a pattern which consists of two regions, foreground and background. The foreground contains all important information needed in the automatic fingerprint recognition systems. However, the background is a noisy region that contributes to the extraction of false minutiae in the system. To avoid the extraction of false minutiae, there are many steps which should be followed such as preprocessing and enhancement. One of these steps is the transformation of the fingerprint image from gray-scale image to black and white image. This transformation is called segmentation or binarization. The aim for fingerprint segmentation is to separate the foreground from the background. Due to the nature of fingerprint image, the segmentation becomes an important and challenging task. The proposed algorithm is applied on FVC2000 database. Manual examinations from human experts show that the proposed algorithm provides an efficient segmentation results. These improved results are demonstrating in diverse experiments.

Implementação em FPGA de um sistema para processamento de imagens digitais para aplicações diversificadas

Pós-graduação em Ciência da Computação - IBILCE

Estimativa do número de frutos verdes em laranjeiras com o uso de imagens digitais

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Image enhancement with MatLab algorithms

Nowadays, a lot of applications use digital images. For example in face recognition to detect and tag persons in photograph, for security control, and a lot of applications that can be found in smart cities, as speed control in roads or highways and cameras in traffic lights to detect drivers ignoring red light. Also in medicine digital images are used, such as x-ray, scanners, etc. These applications depend on the quality of the image obtained. A good camera is expensive, and the image obtained depends also on external factor as light. To make these applications work properly, image enhancement is as important as, for example, a good face detection algorithm. Image enhancement also can be used in normal photograph, for pictures done in bad light conditions, or just to improve the contrast of an image. There are some applications for smartphones that allow users apply filters or change the bright, colour or contrast on the pictures. This project compares four different techniques to use in image enhancement. After applying one of these techniques to an image, it will use better the whole available dynamic range. Some of the algorithms are designed for grey scale images and others for colour images. It is used Matlab software to develop and present the final results. These algorithms are Successive Means Quantization Transform (SMQT), Histogram Equalization, using Matlab function and own implemented function, and V transform. Finally, as conclusions, we can prove that Histogram equalization algorithm is the simplest of all, it has a wide variability of grey levels and it is not suitable for colour images. V transform algorithm is a good option for colour images. The algorithm is linear and requires low computational power. SMQT algorithm is non-linear, insensitive to gain and bias and it can extract structure of the data. RESUMEN. Hoy en día incontable número de aplicaciones usan imágenes digitales. Por ejemplo, para el control de la seguridad se usa el reconocimiento de rostros para detectar y etiquetar personas en fotografías o vídeos, para distintos usos de las ciudades inteligentes, como control de velocidad en carreteras o autopistas, cámaras en los semáforos para detectar a conductores haciendo caso omiso de un semáforo en rojo, etc. También en la medicina se utilizan imágenes digitales, como por ejemplo, rayos X, escáneres, etc. Todas estas aplicaciones dependen de la calidad de la imagen obtenida. Una buena cámara es cara, y la imagen obtenida depende también de factores externos como la luz. Para hacer que estas aplicaciones funciones correctamente, el tratamiento de imagen es tan importante como, por ejemplo, un buen algoritmo de detección de rostros. La mejora de la imagen también se puede utilizar en la fotografía no profesional o de consumo, para las fotos realizadas en malas condiciones de luz, o simplemente para mejorar el contraste de una imagen. Existen aplicaciones para teléfonos móviles que permiten a los usuarios aplicar filtros y cambiar el brillo, el color o el contraste en las imágenes. Este proyecto compara cuatro técnicas diferentes para utilizar el tratamiento de imagen. Se utiliza la herramienta de software matemático Matlab para desarrollar y presentar los resultados finales. Estos algoritmos son Successive Means Quantization Transform (SMQT), Ecualización del histograma, usando la propia función de Matlab y una nueva función que se desarrolla en este proyecto y, por último, una función de transformada V. Finalmente, como conclusión, podemos comprobar que el algoritmo de Ecualización del histograma es el más simple de todos, tiene una amplia variabilidad de niveles de gris y no es adecuado para imágenes en color. El algoritmo de transformada V es una buena opción para imágenes en color, es lineal y requiere baja potencia de cálculo. El algoritmo SMQT no es lineal, insensible a la ganancia y polarización y, gracias a él, se puede extraer la estructura de los datos.

Histogram-based DNA analysis for the visualization of chromosome, genome and species information

We describe a novel approach to explore DNA nucleotide sequence data, aiming to produce high-level categorical and structural information about the underlying chromosomes, genomes and species. The article starts by analyzing chromosomal data through histograms using fixed length DNA sequences. After creating the DNA-related histograms, a correlation between pairs of histograms is computed, producing a global correlation matrix. These data are then used as input to several data processing methods for information extraction and tabular/graphical output generation. A set of 18 species is processed and the extensive results reveal that the proposed method is able to generate significant and diversified outputs, in good accordance with current scientific knowledge in domains such as genomics and phylogenetics.

Multi-dimensional scaling applied to histogram-based DNA analysis

This paper aims to study the relationships between chromosomal DNA sequences of twenty species. We propose a methodology combining DNA-based word frequency histograms, correlation methods, and an MDS technique to visualize structural information underlying chromosomes (CRs) and species. Four statistical measures are tested (Minkowski, Cosine, Pearson product-moment, and Kendall τ rank correlations) to analyze the information content of 421 nuclear CRs from twenty species. The proposed methodology is built on mathematical tools and allows the analysis and visualization of very large amounts of stream data, like DNA sequences, with almost no assumptions other than the predefined DNA “word length.” This methodology is able to produce comprehensible three-dimensional visualizations of CR clustering and related spatial and structural patterns. The results of the four test correlation scenarios show that the high-level information clusterings produced by the MDS tool are qualitatively similar, with small variations due to each correlation method characteristics, and that the clusterings are a consequence of the input data and not method’s artifacts.

Avaliação de um sistema CAD na deteção de lesões em imagens mamográficas

Mestrado em Radiações Aplicadas às Tecnologias da Saúde - Área de especialização: Imagem Digital por Radiação X.

Assessment of Autonomic Function by Phase Rectification of RRInterval Histogram Analysis in Chagas Disease

Background:In chronic Chagas disease (ChD), impairment of cardiac autonomic function bears prognostic implications. Phase‑rectification of RR-interval series isolates the sympathetic, acceleration phase (AC) and parasympathetic, deceleration phase (DC) influences on cardiac autonomic modulation.Objective:This study investigated heart rate variability (HRV) as a function of RR-interval to assess autonomic function in healthy and ChD subjects.Methods:Control (n = 20) and ChD (n = 20) groups were studied. All underwent 60-min head-up tilt table test under ECG recording. Histogram of RR-interval series was calculated, with 100 ms class, ranging from 600–1100 ms. In each class, mean RR-intervals (MNN) and root-mean-squared difference (RMSNN) of consecutive normal RR-intervals that suited a particular class were calculated. Average of all RMSNN values in each class was analyzed as function of MNN, in the whole series (RMSNNT), and in AC (RMSNNAC) and DC (RMSNNDC) phases. Slopes of linear regression lines were compared between groups using Student t-test. Correlation coefficients were tested before comparisons. RMSNN was log-transformed. (α < 0.05).Results:Correlation coefficient was significant in all regressions (p < 0.05). In the control group, RMSNNT, RMSNNAC, and RMSNNDCsignificantly increased linearly with MNN (p < 0.05). In ChD, only RMSNNAC showed significant increase as a function of MNN, whereas RMSNNT and RMSNNDC did not.Conclusion:HRV increases in proportion with the RR-interval in healthy subjects. This behavior is lost in ChD, particularly in the DC phase, indicating cardiac vagal incompetence.

Negative Fukui functions: new insights based on electronegativity equalization

The occurrence of negative values for Fukui functions was studied through the electronegativity equalization method. Using algebraic relations between Fukui functions and different other conceptual DFT quantities on the one hand and the hardness matrix on the other hand, expressions were obtained for Fukui functions for several archetypical small molecules. Based on EEM calculations for large molecular sets, no negative Fukui functions were found

A generalization of histogram type estimators

We introduce simple nonparametric density estimators that generalize theclassical histogram and frequency polygon. The new estimators are expressed as linear combination of density functions that are piecewisepolynomials, where the coefficients are optimally chosen in order to minimize the integrated square error of the estimator. We establish the asymptotic behaviour of the proposed estimators, and study theirperformance in a simulation study.

Measuring the stability of histogram appearance when the anchor position is changed

Although the histogram is the most widely used density estimator, itis well--known that the appearance of a constructed histogram for a given binwidth can change markedly for different choices of anchor position. In thispaper we construct a stability index $G$ that assesses the potential changesin the appearance of histograms for a given data set and bin width as theanchor position changes. If a particular bin width choice leads to an unstableappearance, the arbitrary choice of any one anchor position is dangerous, anda different bin width should be considered. The index is based on the statisticalroughness of the histogram estimate. We show via Monte Carlo simulation thatdensities with more structure are more likely to lead to histograms withunstable appearance. In addition, ignoring the precision to which the datavalues are provided when choosing the bin width leads to instability. We provideseveral real data examples to illustrate the properties of $G$. Applicationsto other binned density estimators are also discussed.

Steganalytic methods for the detection of histogram shifting data-hiding schemes

In this paper, some steganalytic techniques designed to detect the existence of hidden messages using histogram shifting methods are presented. Firstly, some techniques to identify specific methods of histogram shifting, based on visible marks on the histogram or abnormal statistical distributions are suggested. Then, we present a general technique capable of detecting all histogram shifting techniques analyzed. This technique is based on the effect of histogram shifting methods on the "volatility" of the histogram of differences and the study of its reduction whenever new data are hidden.