NDE data fusion using morphological approaches

The objective of most data fusion algorithms is to combine information made available by various sensors synergistically in order to enhance the overall level of information. Since information obtained from data sources such as sensors is often incomplete or imprecise in nature, the application of data fusion techniques has evoked interest in a number of fields ranging from robotics to nondestructive evaluation (NDE). In NDE applications, such techniques can be used to integrate and fuse data obtained using multiple inspection modalities to produce a more comprehensive picture of the condition of the test specimen. As an example, ultrasonic and eddy current imaging techniques are used very widely to inspect a variety of materials. Each technique offers inspection capabilities and limitations that are dictated by the underlying material/energy interaction process. The information generated using the two methods can be construed either as complementary or redundant in nature. Ideally it should be possible to utilize the redundant information to improve the signal-to-noise ratio. Likewise, it should be possible to fuse the complementary information from the two tests to increase the overall level of information made available to the analyst. Unfortunately the task of segmenting data as noise, redundant and complementary components of information can be frustrating. Consequently, most of the approaches proposed to date in NDE have relied on alternate methods;This dissertation proposes a new algorithm for fusing ultrasonic and eddy current images employing morphological imaging processing approaches. The fusion is accomplished in two stages. The first stage basically employs morphological approaches to reduce unwanted artifacts such as speckle noise in the ultrasonic image. The second stage extracts information about the locations and boundaries of defects on the basis of information contained in the morphological granulometric size distribution of the ultrasonic image. Data fusion is accomplished by combining information relating to the locations and boundaries of the defect obtained from the ultrasonic data with the defect depth information derived from the eddy current image. The validity of the approach is demonstrated using several experimentally derived ultrasonic and eddy current images

Perception and intelligent localization for autonomous driving

Mestrado em Engenharia de Computadores e TelemáticaVisão por computador e fusão sensorial são temas relativamente recentes, no entanto largamente adoptados no desenvolvimento de robôs autónomos que exigem adaptabilidade ao seu ambiente envolvente. Esta dissertação foca-se numa abordagem a estes dois temas para alcançar percepção no contexto de condução autónoma. O uso de câmaras para atingir este fim é um processo bastante complexo. Ao contrário dos meios sensoriais clássicos que fornecem sempre o mesmo tipo de informação precisa e atingida de forma determinística, as sucessivas imagens adquiridas por uma câmara estão repletas da mais variada informação e toda esta ambígua e extremamente difícil de extrair. A utilização de câmaras como meio sensorial em robótica é o mais próximo que chegamos na semelhança com aquele que é o de maior importância no processo de percepção humana, o sistema de visão. Visão por computador é uma disciplina científica que engloba àreas como: processamento de sinal, inteligência artificial, matemática, teoria de controlo, neurobiologia e física. A plataforma de suporte ao estudo desenvolvido no âmbito desta dissertação é o ROTA (RObô Triciclo Autónomo) e todos os elementos que consistem o seu ambiente. No contexto deste, são descritas abordagens que foram introduzidas com fim de desenvolver soluções para todos os desafios que o robô enfrenta no seu ambiente: detecção de linhas de estrada e consequente percepção desta, detecção de obstáculos, semáforos, zona da passadeira e zona de obras. É também descrito um sistema de calibração e aplicação da remoção da perspectiva da imagem, desenvolvido de modo a mapear os elementos percepcionados em distâncias reais. Em consequência do sistema de percepção, é ainda abordado o desenvolvimento de auto-localização integrado numa arquitectura distribuída incluindo navegação com planeamento inteligente. Todo o trabalho desenvolvido no decurso da dissertação é essencialmente centrado no desenvolvimento de percepção robótica no contexto de condução autónoma.Computer vision and sensor fusion are subjects that are quite recent, however widely adopted in the development of autonomous robots that require adaptability to their surrounding environment. This thesis gives an approach on both in order to achieve perception in the scope of autonomous driving. The use of cameras to achieve this goal is a rather complex subject. Unlike the classic sensorial devices that provide the same type of information with precision and achieve this in a deterministic way, the successive images acquired by a camera are replete with the most varied information, that this ambiguous and extremely dificult to extract. The use of cameras for robotic sensing is the closest we got within the similarities with what is of most importance in the process of human perception, the vision system. Computer vision is a scientific discipline that encompasses areas such as signal processing, artificial intelligence, mathematics, control theory, neurobiology and physics. The support platform in which the study within this thesis was developed, includes ROTA (RObô Triciclo Autónomo) and all elements comprising its environment. In its context, are described approaches that introduced in the platform in order to develop solutions for all the challenges facing the robot in its environment: detection of lane markings and its consequent perception, obstacle detection, trafic lights, crosswalk and road maintenance area. It is also described a calibration system and implementation for the removal of the image perspective, developed in order to map the elements perceived in actual real world distances. As a result of the perception system development, it is also addressed self-localization integrated in a distributed architecture that allows navigation with long term planning. All the work developed in the course of this work is essentially focused on robotic perception in the context of autonomous driving

Image watermarking, steganography, and morphological processing

With the fast development of computer technology, research in the fields of multimedia security, image processing, and robot vision have recently become popular. Image watermarking, steganogrphic system, morphological processing and shortest path planning are important subjects among them. In this dissertation, the fundamental techniques are reviewed first followed by the presentation of novel algorithms and theorems for these three subjects. The research on multimedia security consists of two parts, image watermarking and steganographic system. In image watermarking, several algorithms are developed to achieve different goals as shown below. In order to embed more watermarks and to minimize distortion of watermarked images, a novel watermarking technique using combinational spatial and frequency domains is presented. In order to correct rounding errors, a novel technique based on the genetic algorithm (GA) is developed. By separating medical images into Region of Interest (ROI) and non-ROI parts, higher compression rates can be achieved where the ROI is compressed by lossless compression and the non-ROI by lossy compression. The GA-based watermarking technique can also be considered as a fundamental platform for other fragile watermarking techniques. In order to simplify the selection and integrate different watermarking techniques, a novel adjusted-purpose digital watermarking is developed. In order to enlarge the capacity of robust watermarking, a novel robust high-capacity watermarking is developed. In steganographic system, a novel steganographic algorithm is developed by using GA to break the inspection of steganalytic system. In morphological processing, the GA-based techniques are developed to decompose arbitrary shapes of big binary structuring elements and arbitrary values of big grayscale structuring elements into small ones. The decomposition is suited for a parallel-pipelined architecture. The techniques can speed up the morphological processing and allow full freedom for users to design any type and any size of binary and grayscale structuring elements. In applications such as shortest path planning, a novel method is first presented to obtaining Euclidean distance transformation (EDT) in just two scans of image. The shortest path can be extracted based on distance maps by tracking minimum values. In order to record the motion path, a new chain-code representation is developed to allow forward and backward movements. By placing the smooth turning-angle constraint, it is possible to mimic realistic motions of cars. By using dynamically rotational morphology, it is not only guarantee collision-free in the shortest path, but also reduce time complexity dramatically. As soon as the distance map of a destination and collision-free codes have been established off-line, shortest paths of cars given any starting location toward the destination can be promptly obtained on-line

Advances of Mathematical Morphology and Its Applications in Signal Processing

This thesis describes some advances of Mathematical Morphology (MM), in order to improve the performance of MM filters in I-D signal processing, . especially in the application to power system protection. MM methodologies are founded on set-theoretic concepts and nonlinear superpositions of signals and images. The morphological operations possess outstanding geometrical properties which make it undoubted that they are efficient image processing methods. However in I-D signal processing, MM filters are not widely employed. To explore the applications of MM for I-D signal processing, our contributions in this area can be summarized in the following two aspects. Firstly, the fram.ework of the traditional signal processing methods is based on the frequency domain representation of the signal and the analysis of the operators' transfer function in the frequency domain. But to the morphological operations, their representations in the frequency domain are uncertain. In order to tackle this problem, this thesis presents our attempt to describe the weighted morphological dilation in the frequency domain. Under certain restrictions to the signal and the structuring element, weighted dilation is transformed to a mathematical expression in the frequency domain. Secondly, although the frequency domain analysis plays an important role in signal processing, the geometrical properties of a signal such as the shape of the signal cannot be ignored. MM is an effective method in dealing with such problems. In this thesis, based on the theory of Morphological Wavelet (MW), three multi-resolution signal decomposition schemes are presented. They are Multiresolution Morphological Top-Hat scheme (MMTH), Multi-resolution Morphov logical Gradient scheme (MMG) and Multi-resolution Noise Tolerant Morphological Gradient scheme (MNTMG). The MMTH scheme shows its significant effect in distinguishing symmetrical features from asymmetrical features on the waveform, which owes to its signal analysis operator: morphological Top-Hat transformation, an effective morphological technique. In this thesis, the MMTH scheme is employed in the identification of transformer magnetizing inrush curr~nt from internal fault. Decomposing the signal by MMTH, the asymmetrical features of the inrush waveform are exposed, and the other irrelevant components are attenuated. The MMG scheme adopts morphological gradient, a commonly used operator for edge detection in image and signal processing, as its signal analysis / operator. The MMG scheme bears significant property in characterizing and recognizing the sudden changes with sharp peaks and valleys on the waveform. Furthermore, to the MMG scheme, by decomposing the signal into different levels, the higher the level is processed, the more details of the sudden changes are revealed. In this thesis, the MMG scheme is applied for the design of fault locator of power transmission lines, by extracting the transient features directly from fault-generated transient signals. The MNTMG decomposition scheme can effectively reduce the noise and extract transient features at the same time. In this thesis, the MNTMG scheme is applied to extract the fault generated transient wavefronts from noise imposed signals in the application of fault location of power transmission lines. The proposed contributions focus on the effect of weighted dilation in the frequency domain, constructions of morphological multi-resolution decomposition schemes and their applications in power systems

Gray Scale Structuring Element Decomposition

Efficient implementation of morphological operations requires the decomposition of structuring elements into the dilation of smaller structuring elements. Zhuang and Haralick presented an algorithm to find optimal decompositions of structuring elements in binary morphology. In this paper we extend the algorithm to find optimal structuring element decomposition for gray scale morphology.