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Wang, Yingze. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 42-47). / Abstract also in Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Related Work --- p.5 / Chapter 2.1 --- Line labeling and realization problem --- p.5 / Chapter 2.2 --- 3D reconstruction from multiple views --- p.6 / Chapter 2.3 --- 3D reconstruction from single line drawings --- p.7 / Chapter 2.3.1 --- Face identification from the line drawings --- p.7 / Chapter 2.3.2 --- 3D geometry reconstruction --- p.9 / Chapter 2.4 --- Our research topic and contributions --- p.13 / Chapter 3 --- Reconstruction of Curved Manifold Objects --- p.14 / Chapter 3.1 --- Assumptions and terminology --- p.14 / Chapter 3.2 --- Reconstruction of curved manifold objects --- p.17 / Chapter 3.2.1 --- Distinguishing between curved and planar faces --- p.17 / Chapter 3.2.2 --- Transformation of Line Drawings --- p.20 / Chapter 3.2.3 --- Regularities --- p.23 / Chapter 3.2.4 --- 3D Wireframe Reconstruction --- p.26 / Chapter 3.2.5 --- Generating Curved Faces --- p.28 / Chapter 3.2.6 --- The Complete 3D Reconstruction Algorithm --- p.33 / Chapter 4 --- Experiments --- p.35 / Chapter 5 --- Conclusions and Future Work --- p.40 / Chapter 5.1 --- Conclusions --- p.40 / Chapter 5.2 --- Future work --- p.40 / Bibliography --- p.42

Fingerprints have been an invaluable tool for law enforcement and forensics for over a century, motivating research into automated fingerprint based identification in the early 1960's. More recently, fingerprints have found an application in the emerging industry of biometric systems. Biometrics is the automatic identification of an individual based on physiological or behavioral characteristics. Due to its security related applications and the current world political climate, biometrics is presently the subject of intense research by private and academic institutions. Fingerprints are emerging as the most common and trusted biometric for personal identification. However, despite decades of intense research there are still significant challenges for the developers of automated fingerprint verification systems. This thesis includes an examination of all major stages of the fingerprint verification process, with contributions made at each step. The primary focus is upon fingerprint registration, which is the challenging problem of aligning two prints in order to compare their corresponding features for verification. A hierarchical approach is proposed consisting of three stages, each of which employs novel features and techniques for alignment. Experimental results show that the hierarchical approach is robust and outperforms competing state-of-the-art registration methods from the literature. However, despite its power, like most algorithms it has limitations. Therefore, a novel method of information fusion at the registration level has been developed. The technique dynamically selects registration parameters from a set of competing algorithms using a statistical framework. This allows for the relative advantages of different approaches to be exploited. The results show a significant improvement in alignment accuracy for a wide variety of fingerprint databases. Given a robust alignment of two fingerprints, it still remains to be verified whether or not they have originated from the same finger. This is a non-trivial problem, and a close examination of fingerprint features available for this task is conducted with extensive experimental results.

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Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

Self-paced Brain computer interface (SBCI) systems allow individuals with motor disabilities to use their brain signals to control devices, whenever they wish. These systems are required to identify the user’s “intentional control (IC)” commands and they must remain inactive during all periods in which users do not intend control (called “no control (NC)” periods). This dissertation addresses three issues related to the design of SBCI systems: 1) their presently high false positive (FP) rates, 2) the presence of artifacts and 3) the identification of a suitable evaluation metric. To improve the performance of SBCI systems, the following are proposed: 1) a method for the automatic user-customization of a 2-state SBCI system, 2) a two-stage feature reduction method for selecting wavelet coefficients extracted from movement-related potentials (MRP), 3) an SBCI system that classifies features extracted from three neurological phenomena: MRPs, changes in the power of the Mu and Beta rhythms; 4) a novel method that effectively combines methods developed in 2) and 3 ) and 5) generalizing the system developed in 3) for detecting a right index finger flexion to detecting the right hand extension. Results of these studies using actual movements show an average true positive (TP) rate of 56.2% at the FP rate of 0.14% for the finger flexion study and an average TP rate of 33.4% at the FP rate of 0.12% for the hand extension study. These FP results are significantly lower than those achieved in other SBCI systems, where FP rates vary between 1-10%. We also conduct a comprehensive survey of the BCI literature. We demonstrate that many BCI papers do not properly deal with artifacts. We show that the proposed BCI achieves a good performance of TP=51.8% and FP=0.4% in the presence of eye movement artifacts. Further tests of the performance of the proposed system in a pseudo-online environment, shows an average TP rate =48.8% at the FP rate of 0.8%. Finally, we propose a framework for choosing a suitable evaluation metric for SBCI systems. This framework shows that Kappa coefficient is more suitable than other metrics in evaluating the performance during the model selection procedure.

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An experimental approach to traffic flow analysis is presented in which methodology from pattern recognition is applied to a specific dataset to examine its utility in determining traffic patterns. The selected dataset for this work, taken from a 1985 study by JHK and Associates (traffic research) for the Federal Highway Administration, covers an hour long time period over a quarter mile section and includes nine different identifying features for traffic at any given time. The initial step is to select the most pertinent of these features as a target for extraction and local storage during the experiment. The tools created for this approach, a two-level hierarchical group of operators, are used to extract features from the dataset to create a feature space; this is done to minimize the experimental set to a matrix of desirable attributes from the vehicles on the roadway. The application is to identify if this data can be readily parsed into four distinct traffic states; in this case, the state of a vehicle is defined by its velocity and acceleration at a selected timestamp. A three-dimensional plot is used, with color as the third dimension and seen from a top-down perspective, to initially identify vehicle states in a section of roadway over a selected section of time. This is followed by applying k-means clustering, in this case with k=4 to match the four distinct traffic states, to the feature space to examine its viability in determining the states of vehicles in a time section. The method’s accuracy is viewed through silhouette plots. Finally, a group of experiments run through a decision-tree architecture is compared to the kmeans clustering approach. Each decision-tree format uses sets of predefined values for velocity and acceleration to parse the data into the four states; modifications are made to acceleration and deceleration values to examine different results. The three-dimensional plots provide a visual example of congested traffic for use in performing visual comparisons of the clustering results. The silhouette plot results of the k-means experiments show inaccuracy for certain clusters; on the other hand, the decision-tree work shows promise for future work.

We propose to design a DSP based image acquisition and pattern recognition system. This system which could mainly apply to do the vision guided automatic drill on the Flexible Printed Circuit Board (FPCB) includes three sub systems as ¡§Image acquisition system¡¨ , ¡§Pattern recognition system¡¨ and ¡§PCI communication system¡¨ . First , we obtain the FPCB image by the CCD camera , and do the pattern match for the drill goal on it . After computing , DSP transmits the goal coordinates to the computer user interface application . By the experiment result , we successfully make the whole system match the original purpose by using two image pre-process steps.

Abstract On the whole, there are two ways to dispose of multiclass classification problem. One is deal it with directly. And the other is dividing it into several binary-class problems. For this reason, it will be simpler as regards individual binary-class problems. And it can improve the accuracy of the multiclass classification problem by reorganize the effect. So how to decompose several binary-class problems is the most important point. Here, based on our study, we use Dynamic Programming as foundation to get the optimal solution of multiclass¡¦s decomposition. Not only get it simplify but also can achieved the best classified result.