ANALYZING THE EFFECT OF POLARIZATION IN IMAGING

Light as the natural element for our life can be characterized by its intensity, wavelength and polarization. The polarization is general characteristic of wave (light, gravitational wave, sound wave etc.) that have the information of their oscillations as well as the reflecting object. Polarization of light could not be viewed naturally by our naked human eyes due to the limitation of capabilities of capturing light on a muscle known as the ciliary muscle. Nowadays, in the computer vision, the polarization is used to determine image segmentation, object and texture recognition. Moreover, in the medical field, polarization is used to allow better the diagnose of skin texture and lesion. This project uses digital image processing technique to analyze the effect of polarization in imaging, which focuses on identifying the textures or patterns of an object. In the polarization on human skin’s imaging, this analysis technique is developed to classify and determine the texture of human skin based on the different races background with the aid of polarized light as well to distinguish between the texture of normal skin and skin lesion

ANALYZING THE EFFECT OF POLARIZATION IN IMAGING

Light as the natural element for our life can be characterized by its intensity, wavelength and polarization. The polarization is general characteristic of wave (light, gravitational wave, sound wave etc.) that have the information of their oscillations as well as the reflecting object. Polarization of light could not be viewed naturally by our naked human eyes due to the limitation of capabilities of capturing light on a muscle known as the ciliary muscle. Nowadays, in the computer vision, the polarization is used to determine image segmentation, object and texture recognition. Moreover, in the medical field, polarization is used to allow better the diagnose of skin texture and lesion. This project uses digital image processing technique to analyze the effect of polarization in imaging, which focuses on identifying the textures or patterns of an object. In the polarization on human skin’s imaging, this analysis technique is developed to classify and determine the texture of human skin based on the different races background with the aid of polarized light as well to distinguish between the texture of normal skin and skin lesion

A COMBINED HISTOGRAM OF ORIENTED GRADIENTS AND COMPLETED LOCAL BINARY PATTERN METHODS FOR PEOPLE COUNTING IN A DENSE CROWD SCENARIO

Estimating the number of people in a dense crowd scenario is one of the most interesting subjects in visual surveillance system application. It is extremely important in controlling and monitoring the crowd for safety control and urban planning. However, estimating the number of people in any dense crowd situation is not an easy task. This problem mostly arises due to some false positive and false negative and it affects the performance of system on detection rate. Therefore in this thesis, an innovative method for people counting in dense crowd scenario is proposed. This method used a collaborative Histogram of Oriented Gradients (HOG) and Completed Local Binary Pattern (CLBP) based on people detection algorithm to detect headshoulder region. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP descriptors are utilized to extract the edge contour and texture features of head-shoulder region, respectively. The two features are then fused together to generate a cumulative feature vectors. Support Vector Machine (SVM) is used to perform classification of the fusion features to people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario

A COMBINED HISTOGRAM OF ORIENTED GRADIENTS AND COMPLETED LOCAL BINARY PATTERN METHODS FOR PEOPLE COUNTING IN A DENSE CROWD SCENARIO

Estimating the number of people in a dense crowd scenario is one of the most interesting subjects in visual surveillance system application. It is extremely important in controlling and monitoring the crowd for safety control and urban planning. However, estimating the number of people in any dense crowd situation is not an easy task. This problem mostly arises due to some false positive and false negative and it affects the performance of system on detection rate. Therefore in this thesis, an innovative method for people counting in dense crowd scenario is proposed. This method used a collaborative Histogram of Oriented Gradients (HOG) and Completed Local Binary Pattern (CLBP) based on people detection algorithm to detect headshoulder region. Head-shoulder region is used as features to detect people against the false positive and false negative issue. HOG and CLBP descriptors are utilized to extract the edge contour and texture features of head-shoulder region, respectively. The two features are then fused together to generate a cumulative feature vectors. Support Vector Machine (SVM) is used to perform classification of the fusion features to people from a mixture of objects. The results show that the detection rate of the proposed method HOG-CLBP, on Recall value and Accuracy, achieves better performance compared to the current method for dense crowd scenario