Browsing by Subject "Optical pattern recognition"
Now showing 1 - 8 of 8
Results Per Page
Sort Options
Item Applications of group theory to pattern recognition.(Texas Tech University, 1974-05) Dirilten, HudaiNot availableItem Automated object recognition through reinforcement learning(Texas Tech University, 2002) Ge, Zhanyu; Mitra, Sunanda; Krile, Thomas; Bredeson, Jon G.Object recognition, a branch of pattern recognition, is to identify and localize one or more objects in a given scene. We have to determine what is present and where it is within the input image. Although great achievements have been made during the last decades, currently existing object recognition techniques have shortcomings like unreliability and inefficiency, general inadaptability, manual template marking heavily influenced by human factors, inability to recognize an object without a model, and so on. Any recognition problem can be formulated as a searching process and has to be guided in a controlled manner. All search problems involve optimization, so object recognition requires optimization and control techniques. Reinforcement learning is learning how to behave given a situation and possible actions to maximize the total expected reward in the long run, and therefore needs to be optimized. Most pattern recognition techniques do not combine reinforcement learning for feature understanding. In this dissertation, reinforcement learning is applied both to automatic template generation from a model image and to template matching within the input image. The newly designed affine parameter estimation algorithm provides reliable results based on information contained at all feature point locations. The points are extracted in the scale-space using isophote curvature extreme points, which are invariant to affine transformations. The affine parameter estimation algorithm is applicable to any kind of translations, rotations, and scales, and moderate occlusions and deformations of the object to be recognized. Experiment results showed that the proposed set of algorithms are fast, efficient, and potentially robust. The automatic template generation algorithm, an efficient contour tracing one in gray-level images, can also be used in object recognition without a model. This is a new research field, and a great amount of future work needs to be done before an intelligent recognition system, as efficient as the human vision system, can be developed.Item Calibration and three-dimensional reconstruction using epipolar constraints on a structured light computer vision system(Texas Tech University, 1997-05) Lin, ChangxingA new structured light computer vision system was developed to determine 3 dimensional geometry information of objects. The system was composed of a dot matrix pattern laser projector, and two cameras (labeled as A and B). Here, the camera A is called main camera. The cameras (B) functions as a checking device to determine the correct image matching between the main image and the projector, so it is called checking camera. There are three contributions in this dissertation and they are as follows: First, a new camera calibration technique is provided, in which the image center, uncertainty scale factor, camera focal length, rotation matrix, and translation vector can be determined using at least seven noncoplanar calibration points; the orthogonality of rotation matrix can be satisfied not only theoretical but also numerically with actual calibration; all intrinsic and extrinsic parameters can be determined using the same set of data; no assumption is needed for the world coordinate system setup; and no nonlinear techniques are required. Second, a new linear approach for estimating the epipolar lines on the main camera (A), related to the projector, is developed. The existing methods can not guarantee that all image points on the same epipolar line on the main camera, related to the projector, have the same corresponding epipolar line on the projector. This is against the epipolar geometric constraints. The approach developed here can guarantee that all points on the same epipolar line on the main image, related to the projector, must have the same corresponding epipolar line on the projector. Third, two checking point equations are given to determine the correct image matching among the main image, the checking image, and the projector. The methods developed here, only require use of the epipolar lines on the projector, related to the main camera (A). Calibration of the projector is not required. A review of the state of the art is given in the first three chapters. All methods developed here were verified experimentally.Item Developing computer-generated stereoscopic haptic images(Texas Tech University, 1998-12) Watson, Kirk LNot availableItem Optical enhancement of degraded fingerprints(Texas Tech University, 1986-05) Olimb, Hal ErlingThe similarities and differences between fingerprints enable degraded fingerprints to be optically enhanced for the purpose of identification. A coherent optical processing system was used to locate and analyze the spectra of features that uniquely identify fingerprints. The spectra of these ridges and ridge characteristics (minutiae) are located in harmonic bands of spatial frequencies for all types of fingerprints. A general set of simple filters was developed to enhance the identifying features of degraded fingerprints. Simple binary filters (lowpass, bandpass, highpass, and contrast reversal) use the circular symmetry of fingerprint spectra to enhance the degraded fingerprints. Schlieren and Laplacian filters perform an edge enhancement on the fingerprint ridges. These techniques improved the identifiability of degraded fingerprints while maintaining the generality of the procedure and the integrity of the fingerprints.Item Phase response characterization of object similarity using the Kohonen model(Texas Tech University, 1998-05) Nath, Jagath ChandrikaNot availableItem Recognition of handwritten letters using a locally connected back-propagation neural network(Texas Tech University, 1991-05) Gomez-Gil, Maria del Pilar.Item Semantic representation and recognition of human activities(2008-08) Ryoo, Michael Sahngwon, 1983-; Aggarwal, J. K. (Jagdishkumar Keshoram), 1936-This dissertation describes a methodology for automated recognition of complex human activities. The dissertation presents a general framework which reliably recognizes various types of high-level human activities including human actions, human-human interactions, human-object interactions, and group activities. Our approach is a description-based approach, which enables a user to encode the structure of a high-level human activity as a formal representation. Recognition of human activities is done by semantically matching constructed representations with actual observations. The methodology uses a context-free grammar (CFG) based representation scheme as a formal syntax for representing composite activities. Our CFG-based representation enables us to define complex human activities based on simpler activities or movements. We have constructed a hierarchical framework which automatically matches activity representations with input observations. In the low-level of the system, image sequences are processed to extract poses and gestures. Based on the recognition of gestures, the high-level of the system hierarchically recognizes complex occurring human activities by searching for gestures that satisfies the temporal, spatial, and logical structure described in the representation. The concept of hallucinations and a probabilistic semantic-level recognition algorithm is introduced to cope with imperfect lower-layers. As a result, the system recognizes human activities including 'fighting', 'assault', 'a person leaving a suitcase', and 'a group of thieves stealing an object from owners', which are high-level activities that previous systems had difficulties. The experimental results show that our system reliably recognizes sequences of various types of complex human activities with a high recognition rate.