任务:对图像进行边缘检测
思路:
- )将图像的灰度数值进行0-255的维度统计;
- )EM算法分析出几个核心显示区块的灰度;
- )使用通用的边界检测算法(具体哪一种待定)。
编辑于2017.12.24 15:45
EM算法的使用参考:
# -*- coding: utf-8 -*- """ Created on Tue Oct 24 18:40:00 2017 @author: nvidia """ #EM Algorithm from numpy import * import numpy as np import random import copy SIGMA = 6 EPS = 0.0001 #生成方差相同,均值不同的样本 def generate_data(): Miu1 = 20 Miu2 = 40 N = 1000 #N = 10000 X = mat(zeros((N,1))) for i in range(N): temp = random.uniform(0,1) if(temp > 0.5): X[i] = temp*SIGMA + Miu1 else: X[i] = temp*SIGMA + Miu2 return X #EM算法 key def my_EM(X): k = 2 N = len(X) Miu = np.random.rand(k,1) print "This is the origenal key : ",Miu Posterior = mat(zeros((N,2))) dominator = 0 numerator = 0 #先求后验概率 for iter in range(1000): for i in range(N): dominator = 0 for j in range(k): dominator = dominator + np.exp(-1.0/(2.0*SIGMA**2) * (X[i] - Miu[j])**2) #print dominator,-1/(2*SIGMA**2) * (X[i] - Miu[j])**2,2*SIGMA**2,(X[i] - Miu[j])**2 #return for j in range(k): numerator = np.exp(-1.0/(2.0*SIGMA**2) * (X[i] - Miu[j])**2) Posterior[i,j] = numerator/dominator oldMiu = copy.deepcopy(Miu) #最大化 for j in range(k): numerator = 0 dominator = 0 for i in range(N): numerator = numerator + Posterior[i,j] * X[i] dominator = dominator + Posterior[i,j] Miu[j] = numerator/dominator print Miu #print (abs(Miu - oldMiu)).sum() #print ' ' if (abs(Miu - oldMiu)).sum() < EPS: print Miu print iter print (abs(Miu - oldMiu)).sum() break if __name__ == '__main__': X = generate_data() my_EM(X)