Lines Matching refs:mean
120 //Rw is the learning rate for weight and Rg is leaning rate for mean and variance
131 //u[n+1] = u[n] + Rg*(x[n+1] - u[n]) mean value Sg is sum n values of g
207 bg_model->g_point[n].g_values[0].mean[m] = (unsigned char)first_frame->imageData[p + m];
215 bg_model->g_point[n].g_values[k].mean[m] = 0;
388 double d = g_point->g_values[k].mean[m]- src_pixel[m];
419 double d = g_point->g_values[k].mean[m]- src_pixel[m];
449 const double tmpDiff = src_pixel[m] - g_point->g_values[k].mean[m];
450 g_point->g_values[k].mean[m] = g_point->g_values[k].mean[m] +
480 const double tmpDiff = src_pixel[m] - g_point->g_values[k].mean[m];
481 g_point->g_values[k].mean[m] = g_point->g_values[k].mean[m] +
509 // first pass mean is image value
511 g_point->g_values[bg_model_params->n_gauss - 1].mean[m] = (unsigned char)gm_image->imageData[p + m];
542 //first pass mean is image value
544 g_point->g_values[bg_model_params->n_gauss - 1].mean[m] = pixel[m];
583 bg_model->background->imageData[ bg_model->background->widthStep*i + j*nChannels + m] = (unsigned char)(g_point[n].g_values[0].mean[m]+0.5);
