Prof. Fazel Naghdy Profile

Prof. Fazel Naghdy

at Univ of Wollongong

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Author

Publications (7)

Proceedings Article | 3 September 2009 Paper

A generic approach to haptic modeling of textile artifacts

H. Shidanshidi, F. Naghdy, G. Naghdy, D. Wood Conroy

Proceedings Volume 7444, 74440G (2009) https://doi.org/10.1117/12.829096

KEYWORDS: 3D modeling, Haptic technology, Statistical modeling, 3D image processing, Fuzzy logic, Digital image processing, Neural networks, Virtual reality, Algorithm development, Raw materials

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Proceedings Article | 26 February 2008 Paper

3D geometric modelling of hand-woven textile

H. Shidanshidi, F. Naghdy, G. Naghdy, D. Wood Conroy

Proceedings Volume 6813, 68130B (2008) https://doi.org/10.1117/12.784603

KEYWORDS: 3D modeling, Fuzzy logic, Haptic technology, Algorithm development, Systems modeling, Detection and tracking algorithms, Pattern recognition, Modeling, Data modeling, Visual process modeling

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Proceedings Article | 5 May 2004 Paper

Geometric modeling of the temporal bone for cochlea implant simulation

Catherine Todd, Fazel Naghdy, Stephen O'Leary

Proceedings Volume 5367, (2004) https://doi.org/10.1117/12.533900

KEYWORDS: Bone, Haptic technology, Visualization, Image processing, 3D modeling, Surgery, Computed tomography, Modeling, Ear, Image segmentation

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Proceedings Article | 8 July 1999 Paper

Detection of microcalcification in computer-assisted mammogram analysis

Golshah Naghdy, Fazel Naghdy, L. Yue, A. Drijarkara

Proceedings Volume 3747, (1999) https://doi.org/10.1117/12.351623

KEYWORDS: Mammography, Wavelets, Fuzzy logic, Signal processing, Image processing, Signal detection, Sensors, Databases, Neural networks, Algorithm development

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Proceedings Article | 4 September 1998 Paper

Detection of antipersonnel (AP) mines using mechatronics approach

Ali Shahri, Fazel Naghdy

Proceedings Volume 3392, (1998) https://doi.org/10.1117/12.324175

KEYWORDS: Mining, Land mines, Control systems, Sensors, Robotics, Mechatronics, Modeling, Control systems design, Motion models, Data modeling

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Proceedings Article | 20 October 1993 Paper

Experimental technique for depth maps generation through absorption

Hao Shi, Fazel Naghdy, Christopher Cook

Proceedings Volume 2028, (1993) https://doi.org/10.1117/12.158653

KEYWORDS: Liquids, Light sources and illumination, Cameras, Absorbance, Video, Absorption, Image processing, Reflection, Digital image processing, Geometrical optics

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Proceedings Article | 12 January 1993 Paper

Three-dimensional object modeling and recognition using absorption in a color liquid

Hao Shi, Fazel Naghdy, Christopher Cook

Proceedings Volume 1771, (1993) https://doi.org/10.1117/12.139116

KEYWORDS: 3D modeling, 3D image processing, Object recognition, Digital image processing, Cameras, Image processing, Liquids, Machine vision, Absorption, Computer vision technology

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The application of computer vision in industry has been increasing as greater use is made of flexible automation and robotics. Quality control and sorting can also be heavily dependent on artificial vision interfaced to an intelligent decision making system. Traditionally industrial tasks requiring computer vision are simplified to a 2-D problem in a plane. This permits the use of a single camera and hence reduces the complexity of the procedures of frame grabbing, image processing and decision making. Such a solution is however not suitable when 3-D information is vital in the control or decision making processes. Generation and processing of 3-D images are required for such applications. The work presented in this paper provides a simple method of deriving a 3-D computer model for a special class of industrial objects and then using this model for machine recognition. The object is immersed in a colour liquid and the intensity of the pixels of the captured image is modulated by the depth of the object along the camera axis. The depth maps generated from the image are represented by parallel layers located in planes normal to the camera axis. The 2-D features of the layers are derived and a 3-D model is constructed for the object based on these features. The object is distinguished by contour groups which are classified into three types according to their features. These 3-D features include object features and contour group features. Three steps are adopted for object recognition. The object features are first used in a basic test in order to reduce the number of possible models which an unknown object can match. Secondly, the contour features are used to test each of the contour group models. The models with a higher match rate are then selected for verification using chi-squared (?2) statistical methods. Finally the ?2 test is employed to verity the above test results. The object match is governed by both the ?2 test and the contour group test. From these tests, a model which best matches the object can be obtained.

Showing 5 of 7 publications

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