Three dimensional pattern recognition using feature-based indexing and rule-based search

In flexible automated manufacturing, robots can perform routine operations as well as recover from atypical events, provided that process-relevant information is available to the robot controller. Real time vision is among the most versatile sensing tools, yet the reliability of machine-based scene interpretation can be questionable. The effort described here is focused on the development of machine-based vision methods to support autonomous nuclear fuel manufacturing operations in hot cells; This thesis presents a method to efficiently recognize 3D objects from 2D images based on feature-based indexing. Object recognition is the identification of correspondences between parts of a current scene and stored views of known objects, using chains of segments or indexing vectors. To create indexed object models, characteristic model image features are extracted during preprocessing. Feature vectors representing model object contours are acquired from several points of view around each object and stored. Recognition is the process of matching stored views with features or patterns detected in a test scene; Two sets of algorithms were developed, one for preprocessing and indexed database creation, and one for pattern searching and matching during recognition. At recognition time, those indexing vectors with the highest match probability are retrieved from the model image database, using a nearest neighbor search algorithm. The nearest neighbor search predicts the best possible match candidates. Extended searches are guided by a search strategy that employs knowledge-base (KB) selection criteria. The knowledge-based system simplifies the recognition process and minimizes the number of iterations and memory usage; Novel contributions include the use of a feature-based indexing data structure together with a knowledge base. Both components improve the efficiency of the recognition process by improved structuring of the database of object features and reducing data base size. This data base organization according to object features facilitates machine learning in the context of a knowledge-base driven recognition algorithm. Lastly, feature-based indexing permits the recognition of 3D objects based on a comparatively small number of stored views, further limiting the size of the feature database; Experiments with real images as well as synthetic images including occluded (partially visible) objects are presented. The experiments show almost perfect recognition with feature-based indexing, if the detected features in the test scene are viewed from the same angle as the view on which the model is based. The experiments also show that the knowledge base is a highly effective and efficient search tool recognition performance is improved without increasing the database size requirements. The experimental results indicate that feature-based indexing in combination with a knowledge-based system will be a useful methodology for automatic target recognition (ATR)

Nucleation Initiated Spinodal Decomposition in a Polymerizing System

Dynamics of phase separation in a polymerizing system, consisting of carboxyl terminated polybutadiene acrylonitrile/epoxy/methylene dianiline, was investigated by means of time-resolved light scattering. The initial length scale was found to decrease for some early periods of the reaction which has been explained in the context of nucleation initiated spinodal decomposition We have combined the Cahn-Hilliard kinetic equation and polymerization kinetics, and predicted the initial reduction of the length scale triggered by nucleation

Stitching for multi-view videos with large parallax based on adaptive pixel warping

Conventional stitching techniques for images and videos are based on smooth warping models, and therefore, they often fail to work on multi-view images and videos with large parallax captured by cameras with wide baselines. In this paper, we propose a novel video stitching algorithm for such challenging multi-view videos. We estimate the parameters of ground plane homography, fundamental matrix, and vertical vanishing points reliably, using both of the appearance and activity based feature matches validated by geometric constraints. We alleviate the parallax artifacts in stitching by adaptively warping the off-plane pixels into geometrically accurate matching positions through their ground plane pixels based on the epipolar geometry. We also exploit the inter-view and inter-frame correspondence matching information together to estimate the ground plane pixels reliably, which are then refined by energy minimization. Experimental results show that the proposed algorithm provides geometrically accurate stitching results of multi-view videos with large parallax and outperforms the state-of-the-art stitching methods qualitatively and quantitatively

Design and Validation of the Bright Internet

Bright Internet research was launched as a core project of the AIS Bright ICT Initiative, which aims to build an ICT-enabled Bright Society. To facilitate research on the Bright Internet, we explicitly define the goals and principles of the Bright Internet, and review the evolution of its principles. The three goals of the Bright Internet are: the realization of preventive security, the provision of the freedom of anonymous expression for innocent netizens, and protection from the risk of privacy infringement that may be caused by preventive security schemes. We respecify design principles to fulfill these seemingly conflicting goals: origin responsibility, deliverer responsibility, identifiable anonymity, global collaboration, and privacy protection. Research for the Bright Internet is characterized by two perspectives: first, the Bright Internet adopts a preventive security paradigm in contrast to the current self-centric defensive protective security paradigm. Second, the target of research is the development and deployment of the Bright Internet on a global scale, which requires the design of technologies and protocols, policies and legislation, and international collaboration and global governance. This research contrasts with behavioral research on individuals and organizations in terms of the protective security paradigm. This paper proposes validation research concerning the principles of the Bright Internet using prevention motivation theory and analogical social norm theory, and demonstrates the need for a holistic and prescriptive design for a global scale information infrastructure, encompassing the constructs of technologies, policies and global collaborations. An important design issue concerns the business model design, which is capable of promoting the propagation of the Bright Internet platform through applications such as Bright Cloud Extended Networks and Bright E-mail platforms. Our research creates opportunities for prescriptive experimental research, and the various design and behavioral studies of the Bright Internet open new horizons toward our common goal of a bright future