Lines Matching refs:variance
79 icvMatchTest(...) assumes what all color channels component exhibit the same variance
120 //Rw is the learning rate for weight and Rg is leaning rate for mean and variance
126 //The list is maintained in sorted order using w/sqrt(variance) as a key
132 //v[n+1] = v[n] + Rg*((x[n+1] - u[n])*(x[n+1] - u[n])) - v[n]) variance
206 bg_model->g_point[n].g_values[0].variance[m] = var_init;
214 bg_model->g_point[n].g_values[k].variance[m] = var_init;
390 var_threshold += g_point->g_values[k].variance[m];
420 sum_d2 += (d*d) / (g_point->g_values[k].variance[m] * g_point->g_values[k].variance[m]);
452 g_point->g_values[k].variance[m] = g_point->g_values[k].variance[m]+
453 (learning_rate_gaussian*((tmpDiff*tmpDiff) - g_point->g_values[k].variance[m]));
483 g_point->g_values[k].variance[m] = g_point->g_values[k].variance[m]+
484 (learning_rate_gaussian*((tmpDiff*tmpDiff) - g_point->g_values[k].variance[m]));
510 g_point->g_values[bg_model_params->n_gauss - 1].variance[m] = bg_model_params->variance_init;
543 g_point->g_values[bg_model_params->n_gauss - 1].variance[m] = bg_model_params->variance_init;
565 variance_sum += g_point->g_values[k].variance[m];
