Nonlinear optimal tracking control with application to super-tankers for autopilot design

A new method is introduced to design optimal tracking controllers for a general class of nonlinear systems. A recently developed recursive approximation theory is applied to solve the nonlinear optimal tracking control problem explicitly by classical means. This reduces the nonlinear problem to a sequence of linear-quadratic and time-varying approximating problems which, under very mild conditions, globally converge in the limit to the nonlinear systems considered. The converged control input from the approximating sequence is then applied to the nonlinear system. The method is used to design an autopilot for the ESSO 190,000-dwt oil tanker. This multi-input-multi-output nonlinear super-tanker model is well established in the literature and represents a challenging problem for control design, where the design requirement is to follow a commanded maneuver at a desired speed. The performance index is selected so as to minimize: (a) the tracking error for a desired course heading, and (b) the rudder deflection angle to ensure that actuators operate within their operating limits. This will present a trade-off between accurate tracking and reduced actuator usage (fuel consumption) as they are both mutually dependent on each other. Simulations of the nonlinear super-tanker control model are conducted to illustrate the effectiveness of the nonlinear tracking controller.     

CAD modeling for the components made of multi heterogeneous materials and smart materials

There appear more critical requirements for special functions of components/products in various areas, which can be satisfied only by using heterogeneous materials and/or smart materials. The heterogeneous materials include composite materials, functionally graded materials, and heterogeneous materials with a periodic microstructure. To design and manufacture the components made of these materials, the computer models for representing them need first to be built so that further analysis, optimization and manufacturing can be implemented based on the models. This paper develops such a modeling method, which can be implemented by employing the functions of current CAD graphic software and obtain the model that includes all the material information (about periodic microstructures, constituent composition, inclusions, and embedded parts) along with geometry information in 3D solid modeling without the problem arising from too much data.     

A combination model for orientation field of fingerprints

Orientation field is a global feature of fingerprints that is very important in automatic fingerprint identification systems (AFIS). Establishing an accurate and concise model for orientation fields will not only improve the performance of orientation estimation, but also make it feasible to apply orientation information in the matching process. In this paper, a novel model for the orientation field of fingerprints is proposed. We use a polynomial model to approximate the orientation field globally and a point-charge model at each singular point to improve the approximation locally. These two models are combined together by a weight function. Experimental results are provided to illustrate the fact that this combination model is more accurate and robust with respect to noise compared with the previous works. The application of the model is discussed at the end.     

Perceptual grouping of line features in 3-D space: a model-based framework

In this paper, we propose a novel model-based perceptual grouping algorithm for the line features of 3-D polyhedral objects. Given a 3-D polyhedral model, perceptual grouping is performed to extract a set of 3-D line segments which are geometrically consistent with the 3-D model. Unlike the conventional approaches, grouping is done in 3-D space in a model-based framework. In our unique approach, a decision tree classifier is employed for encoding and retrieving the geometric information of the 3-D model. A Gestalt graph is constructed by classifying input instances into proper Gestalt relations using the decision tree. The Gestalt graph is then decomposed into a few subgraphs, yielding appropriate groups of features. As an application, we suggest a 3-D object recognition system which can be accomplished by selecting a best-matched group. In order to evaluate the performance of the proposed algorithm, experiments are carried out on both synthetic and real scenes.     

A new algorithm for ball recognition using circle Hough transform and neural classifier

A large number of methods for circle detection have been studied in the last years for several image processing applications. The context application considered in this work is the soccer game. In the sequences of soccer images it is very important to identify the ball in order to verify the goal event. This domain is a challenging one as a great number of problems have to be faced, such as occlusions, shadows, objects similar to the ball, real-time processing and so on. In this work a visual framework trying to solve the above-stated problems, mainly considering real-time computational aspects, has been developed. The ball detection algorithm has to be very simple in terms of time processing and also has to be efficient in terms of false positive rate. Our framework consists of two sequential steps for solving the ball recognition problem: the first step uses a modified version of the directional circle Hough transform to detect the region of the image that is the best candidate to contain the ball; in the second step a neural classifier is applied on the selected region to confirm if the ball has been properly detected or a false positive has been found. Some tricks like background subtraction and ball tracking have been applied in order to maintain the search of the ball only in limited areas of the image. Different light conditions have been considered as they introduce strong modifications on the appearance of the ball in the image: when the image sequences are taken with natural light, as the light source is strictly directional, the ball, due to self-shades, appears as a spherical cap; this case has been taken in account and the search of the ball has been modified in order to manage this situation. A large number of experiments have been carried out showing that the proposed method obtains a high detection score.     

Extraction of karyocytes and their components from microscopic bone marrow images based on regional color features

Extracting karyocytes and their components from microscopic bone marrow images is prerequisite for computer-aided early diagnosis of hemopathy. Most existing methods assume all pixels belonging to a karyon region or a cytoplasm region have similar colors. Practically, the color of neither a karyon nor a cytoplasm in a microscopic image is homogeneous in the pixel level. Therefore, the regional color features of a region are emphasized in this paper. Based on this observation, we propose a novel method to karyocyte extraction that first identifies a karyon by 4-connected block growing from a karyon feature block, then identifies feature blocks of its cytoplasm based on the extracted karyon, and finally extracts all cytoplasm regions by growing the cytoplasm feature blocks. Combining the karyon region and the corresponding cytoplasm regions can attain a complete karyocyte. Experimental results show that the proposed method is effective and robust.     

Recognition of shapes by attributed skeletal graphs

In this paper, we propose a framework to address the problem of generic 2-D shape recognition. The aim is mainly on using the potential strength of skeleton of discrete objects in computer vision and pattern recognition where features of objects are needed for classification. We propose to represent the medial axis characteristic points as an attributed skeletal graph to model the shape. The information about the object shape and its topology is totally embedded in them and this allows the comparison of different objects by graph matching algorithms. The experimental results demonstrate the correctness in detecting its characteristic points and in computing a more regular and effective representation for a perceptual indexing. The matching process, based on a revised graduated assignment algorithm, has produced encouraging results, showing the potential of the developed method in a variety of computer vision and pattern recognition domains. The results demonstrate its robustness in the presence of scale, reflection and rotation transformations and prove the ability to handle noise and occlusions.     

Connectivity-based multiple-circle fitting

This paper proposes a connectivity-based method for circle fitting. The use of pixel connectivity effectively avoids false circle detection, improves the robustness against noise and significantly reduces the computational load. The desired circular models are extracted by searching for meaningful circular arcs. The algorithm does not require a good initial guess, and is effective for extracting an a priori unknown number of circles even when the number of outliers exceeds 50%. The experimental results demonstrate that the proposed method performs well in detecting multiple intersecting or occluded circles.     

Qualitative estimation of camera motion parameters from the linear composition of optical flow

In this paper, we propose a new method for estimating camera motion parameters based on optical flow models. Camera motion parameters are generated using linear combinations of optical flow models. The proposed method first creates these optical flow models, and then linear decompositions are performed on the input optical flows calculated from adjacent images in the video sequence, which are used to estimate the coefficients of each optical flow model. These coefficients are then applied to the parameters used to create each optical flow model, and the camera motion parameters implied in the adjacent images can be estimated through a linear composition of the weighted parameters.We demonstrated that the proposed method estimates the camera motion parameters accurately and at a low computational cost as well as robust to noise residing in the video sequence being analyzed.