A monocular color vision system for road intersection detection

Urban driving has become the focus of autonomous robotics in recent years. Many groups seek to benefit from the research in this field including the military, who hopes to deploy autonomous rescue forces to battle-torn cities, and consumers, who will benefit from the safety and convenience resulting from new technologies finding purpose in consumer automobiles. One key aspect of autonomous urban driving is localization, or the ability of the robot to determine its position on a road network. Any information that can be obtained for the surrounding area including stop signs, road lines, and intersecting roads can aid this localization. The work here attempts to combine some previously established computer vision methods to identify roads and develop a new method that can identify both the road and any possible intersecting roads present in front of a vehicle using a single color camera. Computer vision systems rely on a few basic methods to understand and identify what they are looking at. Two valuable methods are the detection of edges that are present in the image and analysis of the colors that compose the image. The method described here attempts to utilize edge information to find road lines and color information to find the road area and any similarly colored intersecting roads. This work demonstrates that combining edge detection and color analysis methods utilizes their strengths and accommodates for their weaknesses and allows for a method that can successfully detect road lanes and intersecting roads at speeds fast enough for use with autonomous urban driving

Extraction of textual information from image for information retrieval

Ph.DDOCTOR OF PHILOSOPH

Off-line Arabic Handwriting Recognition System Using Fast Wavelet Transform

In this research, off-line handwriting recognition system for Arabic alphabet is introduced. The system contains three main stages: preprocessing, segmentation and recognition stage. In the preprocessing stage, Radon transform was used in the design of algorithms for page, line and word skew correction as well as for word slant correction. In the segmentation stage, Hough transform approach was used for line extraction. For line to words and word to characters segmentation, a statistical method using mathematic representation of the lines and words binary image was used. Unlike most of current handwriting recognition system, our system simulates the human mechanism for image recognition, where images are encoded and saved in memory as groups according to their similarity to each other. Characters are decomposed into a coefficient vectors, using fast wavelet transform, then, vectors, that represent a character in different possible shapes, are saved as groups with one representative for each group. The recognition is achieved by comparing a vector of the character to be recognized with group representatives. Experiments showed that the proposed system is able to achieve the recognition task with 90.26% of accuracy. The system needs only 3.41 seconds a most to recognize a single character in a text of 15 lines where each line has 10 words on average

Anomaly Detection In Blockchain

Anomaly detection has been a well-studied area for a long time. Its applications in the financial sector have aided in identifying suspicious activities of hackers. However, with the advancements in the financial domain such as blockchain and artificial intelligence, it is more challenging to deceive financial systems. Despite these technological advancements many fraudulent cases have still emerged. Many artificial intelligence techniques have been proposed to deal with the anomaly detection problem; some results appear to be considerably assuring, but there is no explicit superior solution. This thesis leaps to bridge the gap between artificial intelligence and blockchain by pursuing various anomaly detection techniques on transactional network data of a public financial blockchain named 'Bitcoin'. This thesis also presents an overview of the blockchain technology and its application in the financial sector in light of anomaly detection. Furthermore, it extracts the transactional data of bitcoin blockchain and analyses for malicious transactions using unsupervised machine learning techniques. A range of algorithms such as isolation forest, histogram based outlier detection (HBOS), cluster based local outlier factor (CBLOF), principal component analysis (PCA), K-means, deep autoencoder networks and ensemble method are evaluated and compared

AN INVESTIGATION OF MOTOR VEHICLE DRIVER INATTENTION AND ITS EFFECTS AT HIGHWAY-RAIL GRADE CROSSINGS

The relationship between accident injury severity and drivers’ inattentive behavior requires an in-depth investigation – this is especially needed in the case of motor vehicle drivers at highway-rail grade crossings (HRGCs). The relationship between drivers’ personality/ socioeconomic characteristics and inattentive behavior at HRGCs is another topic requiring research. Past educational programs about safe driving at HRGCs have often not been designed to target people who may be in urgent need of such information, which may limit the effectiveness of those programs. This dissertation thus focuses on the following four objectives: to investigate the association between motor vehicle inattentive driving and the severity of drivers’ injuries sustained in crashes reported at or near HRGCs; to investigate the association between drivers’ self-reported inattentive driving experience and a series of factors such as drivers’ knowledge of safe driving, attitudes towards safe driving, etc.; to identify driver groups that have lower or higher levels of knowledge of correct rail crossing negotiation; and to investigate the direct and indirect effects between drivers’ characteristics and their knowledge level as well as their involvement with inattentive driving behavior at HRGCs. The research obtained 12 years of police-reported crash data from the Nebraska Department of Roads and collected data in a statewide random-sample mail questionnaire survey. Statistical analysis methods, including random parameters binary logit model, confirmatory factor analysis, robust linear regression, multinomial logit model, and structural equation models were utilized in this research. Conclusions are that inattentive driving plays a significant role in contributing to more severe injuries in accidents reported in proximity of HRGCs in Nebraska; Nebraska motor vehicle drivers’ personality traits, knowledge levels of negotiating HRGCs and driving experience are associated with inattentive driving; drivers with lower levels of knowledge of correct HRGC negotiation are: drivers who drive vehicles other than passenger cars, have received less safety information, have a shorter driving history, are older, have lower household income, and have higher intent to violate rules at rail crossings; inattentive driving behavior at HRGCs is directly and indirectly affected by their personality traits while drivers’ knowledge of correct HRGC negotiation appears to only have an indirect effect. Advisor: Aemal J.Khatta

The acquisition and quantitative analysis of digitally generated Fourier transforms resulting from the physiognomy of various tree and shrub species

An optical Fourier transform represents the interference of wavefronts produced as light passes through multiple slits. Theoretically, any image containing periodic structure causes diffraction. The cellular arrangement of a plant leaf and the regularity in venation are ideal conditions for diffracting light. As light passes through columns of cells, its path is altered according to the distance and orientation present. A photograph or slide of a leaf or the leaf itself may be used to produce a diffraction pattern by either optical or digital means. Optically, a laser beam directed through a slide is refracted by a converging lens and focused onto a piece of film at the transform plane. Digitally, software such as ER Mapper processes a scanned image using the Fast Fourier Transform (FFT) algorithm that closely approximates the mathematical analysis by summing the sine and cosine functions, referred to as the real and imaginary bands, respectively. Due to the flexibility and resolving capabilities, the digital method was utilized to acquire and to analyze transforms. The Fourier transforms were analyzed qualitatively according to venation. High spatial frequency in the veins and veinlets did not determine the amount of detail in the transform. The transforms from the top surface of a leaf had very high order information, but the bottom of the leaf exhibited only a central maximum, characterized by low frequency. Veins are apparent on both sides of the leaf; however, cells are ordered on the top of the leaf and more random underneath. Fourier transforms are produced by even higher spatial frequency resulting from the cellular arrangement at the top of the leaf, independent of the venation. To quantitatively analyze the Fourier transforms, the patterns were measured to determine the spacing between successive maximum orders. Using the “cell values” and “cell coordinates features of ER Mapper, the maximum interference values were located and the distance between the central maximum and each order were measured. The calculated cell dimensions ranged from 15 to 45 microns. Transforms from leaf layers photographed at 400X magnification suggest that the palisade cells and the cell walls both diffract light. Cellular dimensions predicted by the Fourier transform appear to result from the sum of the palisade diameter and the cell wall thickness for a total width of 14.5 to 16.5 microns. A correlation between Fourier transforms produced by individual leaves may be compared quantitatively to those arising from an entire tree to accurately represent patterns of plant physiognomy and cellular dimensions