ABSTRACT: Malaria is a most widely spread dangerous blood disease that caused by a mosquito parasite which is called plasmodium. It transmitted into a human’s blood via a female Anopheles mosquito so the life cycle of malaria starts and destroy the RGB cells. It affects pregnant women and 37% of adults and 79% of children under five year’s i.e. taking life of a child at every minute. Symptoms for this disease usually appears in 10 days to 3 weeks after a bite of an infected mosquito. The common symptoms of this disease are fever, headache, vomiting, muscle pain that may reason of death and coma. So In this research work, we propose detection algorithm for malaria disease by using matlab tool. We use image processing filters and techniques to identify the malaria disease along with the Naive Bayes Classifier. In this research, the proposed method detect the normal cell and abnormal cell. Parameters are used like circularity, Area, Perimeter, Mean and also find the number of parasites if cell is normal no parasite found but if cell is abnormal then it will find the number of parasites. The aim of this research is to detect the malaria cell that either it is infected or not and in last we got the efficient results with the high accuracy.
Keywords: Median Filter, RGB to Gray, Intensity, Feature Vectors, Naive Bayes
Classifier.
ABSTRACT: Intelligence systems are becoming increasingly interested in recent years. For the purpose of defense systems, numerous intelligence systems have been implemented. Biometric and video monitoring systems are two of the best intelligence systems used for security purposes. When a crime scene happens, these intelligence systems are present to detect suspicious persons. Since this study is about human faces, biometric devices can be used to identify them. Different features such as eyes, noses, and lips can be compared to the original images, and suspicious individuals can be easily identified. The second is a video surveillance device, in which multiple photographs are captured at the time of a crime, but there are two big concerns that arise when the images are taken. One is image quality, which is due to brightness, and the other is the absence of a full frontal human face. This thesis focuses on improving the human frontal face and creating a complete human face from the half of the face that is accessible using geometrical measurements. Geometrical measurements of various extracted facial features are taken using various methods. Face is effectively generated using those measurements. Finally, the original image and the created image are distinguished.
Keywords: Image Enhancement; Landmarking; Intelligence System; Feature Extraction; Geometrical Measurement.