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This paper suggests the use of symmetric patterns and their corresponding symmetry filters for pattern recognition in computer vision tasks involving multiple views and scales. Symmetry filters enable efficient computation of certain structure features as represented by the generalized structure tensor (GST). The properties of the complex moments to changes in scale and multiple views including in-depth rotation of the patterns and the presence of noise is investigated. Images of symmetric patterns captured using a low resolution low-cost CMOS camera, such as a phone camera or a web-cam, from as far as three meters are precisely localized and their spatial orientation is determined from the argument of the second order complex moment I20 without further computation.

Refractive error is one of optical defect in the human visual system. Refractive error is a very common disease these days in all populations and in all age groups. Uncorrected and undetected refractive error contributes to visual impairment, blindness and places a considerable burden on a person in the world. The long use of technological devices such as smart phones also poses a new burden on the human eye. The intensity and brightness of these digital devices open a new door for high prevalence of eye refractive errors. Early medical diagnosis of the disease may help in avoiding complications and blindness. Data mining algorithms can be applied to help in ophthalmology and detection of an eye disease at an early stage. So mining the ophthalmology data in efficient manner is a critical issue. This research work deals with development of an integrated knowledge-based system that helps to detect eye refractive error early and provides appropriate advice for the patients. In this study, the hybrid knowledge discovery process model of data mining that was developed for academic research is used. About 9000 ophthalmology data from selected eye health centers are used to build the model. The sample data was preprocessed for missing values, outliers, and noise. Then the model is built using decision tree (J48 and REPTree) and rule induction (JRip and part) algorithms. The part algorithm has registered better predictive performance with accuracy of 60% and 96.45% for subjective and objective based model evaluation, respectively as compared to J48, REPTree, and JRip. Finally, the knowledge discovered with this algorithm is further used to build the knowledge-based systems. The Java programing language is used to integrate data mining results to knowledge-based system. The performance of the proposed system is evaluated by preparing test cases. Overall, the knowledge based system resulted in 89.2% accuracy.  Finally the study concludes that discovering knowledge using data mining techniques could be used as a functional eye refractive error detection system.

Video databases and their corresponding evaluation protocols are used to compare classifiers, such as face detection and tracking. In this paper, a six level evaluation protocol for the Damascened XM2VTS (DXM2VTS) database [8] is presented to measure face detection and tracking performance. Additionally, a novel database containing thousands of videos is created by combining video from the XM2VTS database with a set of newly recorded standardized real-life video used as background and with several realistic degradations, such as motion blur, noise, etc. Moreover, two publicly available and published face detection algorithms [2,9], are tested on the six suggested difficulty levels of the protocol. Their performance on video in terms of false acceptance, false rejection, correct detection, and repeatability, are reported and conclusions are drawn.

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