Face Identification system is an active research area in these years. However, the accuracy and its dependency in real life systems are still questionable. Earlier research in face identification systems demonstrated that LBP based face recognition systems are preferred than others and give adequate accuracy. It is robust against illumination changes and considered as a high-speed algorithm. Performance metrics for such systems are calculated from time delay and accuracy. This paper introduces an improved face recognition system that is build using C++ programming language with the help of OpenCV library. Accuracy can be increased if a filter or combinations of filters are applied to the images. The accuracy increases from 95.5% (without applying any filter) to 98.5% when applying a combination of Bilateral filter, Histogram Equalization and Tan and Triggs Algorithm. Finally, the results show degradation in accuracy and increasing in recognition time if images database get bigger.
Gray-Scale Image Brightness/Contrast Enhancement with Multi-Model
Histogram linear Contrast Stretching (MMHLCS) method
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Recognition is one of the basic characteristics of human brain, and also for the living creatures. It is possible to recognize images, persons, or patterns according to their characteristics. This recognition could be done using eyes or dedicated proposed methods. There are numerous applications for pattern recognition such as recognition of printed or handwritten letters, for example reading post addresses automatically and reading documents or check reading in bank.
One of the challenges which faces researchers in character recognition field is the recognition of digits, which are written by hand. This paper describes a classification method for on-line handwrit
... Show MoreAlzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. Deep Learning is one of the modern methods that were used to detect and classify a medical image because of the ability of deep Learning to extract the features of images automatically. However, there are still limitations to using deep learning to accurately classify medical images because extracting the fine edges of medical images is sometimes considered difficult, and some distortion in the images. Therefore, this research aims to develop A Computer-Aided Brain Diagnosis (CABD) system that can tell if a brain scan exhibits indications of
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This study was aimed to investigate the load of bacterial contaminant in fresh meat with different types of bacteria.One handered and seven samples were collected from different regions of Baghdad . These samples included 37 of fresh beef 70 of fresh sheep meat. All samples were cultured on different selective media to identitfy of contaminated bacteria .The result revealed that The percentage of bacterial isolate from raw sheep meat were, % 23.8of StreptococcusgroupD,29.4 % of Staphylococcus aureus ,14.7 % of E.coli , %4.9of Salmonella spp, ,%3.5 of pseudomonas aeruginosa, %14.7.%14.7 of Proteus spp.% 2.1 of Listeria spp while the raw beef meat content %5.55 of Staphylococcus aureus, %8.14 of streptococcus group D , %5.18 %1.85 of E.coli,
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Preparation and Identification of some new Pyrazolopyrin derivatives and their Polymerizations study
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