A Multi Views Approach for Remote Sensing Fusion Based on Spectral, Spatial and Temporal Information

The objectives of this chapter are to contribute to the apprehension of image fusion approaches including concepts definition, techniques ethics and results assessment. It is structured in five sections. Following this introduction, a definition of image fusion provides involved fundamental concepts. Respectively, we explain cases in which image fusion might be useful. Most existing techniques and architectures are reviewed and classified in the third section. In fourth section, we focuses heavily on algorithms based on multi-views approach, we compares and analyses the process model and algorithms including advantages, limitations and applicability of each view. The last part of the chapter summarized the benefits and limitations of a multi-view approach image fusion; it gives some recommendations on the effectiveness and the performance of these methods. These recommendations, based on a comprehensive study and meaningful quantitative metrics, evaluate various proposed views by applying them to various environmental applications with different remotely sensed images coming from different sensors. In the concluding section, we fence the chapter with a summary and recommendations for future researches

GENETIC FUZZY FILTER BASED ON MAD AND ROAD TO REMOVE MIXED IMPULSE NOISE

In this thesis, a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD) is proposed. The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. From the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations. It can also remove salt-and-pepper and uniform impulse noise well

Doctor of Philosophy

dissertationCigarette smoking is the leading cause of preventable death in the United States. While there are more than 4,000 chemicals found in tobacco smoke, the polycyclic aromatic hydrocarbons (PAHs) have been clearly demonstrated to contribute to smoking-related cancers. Of this group of compounds, benzo(a)pyrene (B(a)P) is concidered to be the most carcinogenic and its ability to cause lung tumors is well documented. Many conventional biomarker assays conducted today use the measurement of nicotine (and its metabolite cotinine) in blood, urine, or oral fluids for assessment of tobacco smoke exposure. However, these conventional assays do not measure exposure to carcinogenic compounds and are sensitive only to recent smoke exposures. Due to the ease of hair sampling and its extended detection window of substances incorporated into its matrix, this dissertational research proposes a promising new tool for the assessment of toxic tobacco smoke exposure. We investigated the disposition of B(a)P and its electrophilic reactive metabolite, trans-7,8-dihydroxy-anti-9,10-epoxy-7,8,9,10-tetrahydro-benzo(a)pyrene (BPDE), in rat and human hair. BPDE is one of the most potent mutagens and carcinogens known, and forms protein and DNA adducts in iv multiple tissues. Our overarching hypothesis was that B(a)P and BPDE-protein adducts in hair can be used as biomarkers of toxic B(a)P exposure. The data presented in this dissertation demonstrate that B(a)P and BPDE-protein adducts are incorporated into rat hair in a dose-dependent manner. While B(a)P incorporation into rat hair is not dependent upon pigment content, BPDE-protein adducts concentrations are significantly greater in pigmented vs. nonpigmented hair. Gross histopathological changes in rat lung tissue, such as alveolar wall thickening, decreased air space, and macrophage hyperplasia were visually evident in rats 14 days after B(a)P administration. Immunohistochemistry staining for myeloperoxide content (a marker for neutrophils) in the lung tissue of B(a)P-dosed rats was also significantly greater than vehicle control rats. B(a)P can be detected in human hair, but BPDE-protein adducts could not be detected, despite evidence of active smoking status via plasma cotinine concentrations. The results of this dissertational research demonstrate that hair may serve as an easily accessible surrogate tissue for the detection of a biomarker of toxic tobacco smoke exposure

Fuzzy approach for Arabic character recognition

Pattern recognition/classification is increasingly drawing the attention of scientific research because of its important roll in automation and human-machine communication. Even though many models have been introduced to deal with classification, because of the inherited imprecision and ambiguity, these models did not tackle the problem in an efficient way. Traditional models deal only with statistical uncertainty (randomness) but not with the non-statistical uncertainty (vagueness). Fuzzy set theory allows us to better understand imprecision in both of its categories: vagueness and randomness. The incorporation of fuzzy set theory in existing algorithms helped in many cases to improve the performance and increase the efficiency of those algorithms. This thesis will explore fuzzy logic as it pertains to pattern recognition. In order to demonstrate fuzzy logic, the problem of recognizing the Arabic alphabet is discussed. In this problem moments and central moments were used as discriminating features. A fuzzy classifier was designed in a way that incorporated some statistical knowledge of the problem in hand. Performance of this classifier was compared to a Bayesian classifier and a neural network classifier. Performance, evaluation, and advantages and disadvantages of each classifier is reported and discussed

Image similarity in medical images

Recent experiments have indicated a strong influence of the substrate grain orientation on the self-ordering in anodic porous alumina. Anodic porous alumina with straight pore channels grown in a stable, self-ordered manner is formed on (001) oriented Al grain, while disordered porous pattern is formed on (101) oriented Al grain with tilted pore channels growing in an unstable manner. In this work, numerical simulation of the pore growth process is carried out to understand this phenomenon. The rate-determining step of the oxide growth is assumed to be the Cabrera-Mott barrier at the oxide/electrolyte (o/e) interface, while the substrate is assumed to determine the ratio β between the ionization and oxidation reactions at the metal/oxide (m/o) interface. By numerically solving the electric field inside a growing porous alumina during anodization, the migration rates of the ions and hence the evolution of the o/e and m/o interfaces are computed. The simulated results show that pore growth is more stable when β is higher. A higher β corresponds to more Al ionized and migrating away from the m/o interface rather than being oxidized, and hence a higher retained O:Al ratio in the oxide. Experimentally measured oxygen content in the self-ordered porous alumina on (001) Al is indeed found to be about 3% higher than that in the disordered alumina on (101) Al, in agreement with the theoretical prediction. The results, therefore, suggest that ionization on (001) Al substrate is relatively easier than on (101) Al, and this leads to the more stable growth of the pore channels on (001) Al