An Analytical Approach On Feature Extraction For Image Classification With Any Regression Method Using Matlab
In recent years, we are easily able to treat a lot of images because of the spread of smart phones, digital cameras, and the Internet, etc. In addition, the performance of CPU and GPU has been better. Therefore, image classification by a computer has been paid attention. If image classification can be performed with high accuracy, automatic visual inspection and self-driving, etc. will be realized. Therefore, image classification with high accuracy by computer is required. Gaussian filters focus on minimizing the regularization term, whose minimizers are already known. In previous work it is not done properly. For example, if the regularization is Gaussian curvature, the developable surfaces minimize this energy. Therefore, in curvature filter, developable surfaces are used to approximate the data. Traditional solvers, such as gradient descent or Euler Lagrange Equation, start at the total energy and use diffusion scheme to carry out the minimization. When the initial condition is the original image, the data fitting energy always increases while the regularization energy always reduces during the optimization; A fast new algorithm to detect corners defined at different scales is presented. It relies on calculating the shape curvature function using an adaptive filtering factor to remove as much noise as possible without losing corners. Corners are the peaks of the function and they can be detected by thresholding. Experimental results show that detected corners are very stable against noise.