# Texture Construction Edge Detection Algorithm
## 3 Experimental Results and Discussion  
The TCEDA proposed in this paper was compared with the other four improved adaptive Canny edge detection algorithms. [PERSON] and [PERSON] [PERSON] and [PERSON] (2010) used the Otsu method to calculate the gradient magnitude histogram to obtain the adaptive high and low thresholds, where the low threshold was set to one-half of the high threshold; [PERSON] et al. [PERSON] et al. (2011) used the Otsu method to perform two operations on the gradient magnitude histogram to obtain the adaptive high and low thresholds; [PERSON] et al. [PERSON] et al. (2012) used the MSE method to perform two operations on the gradient magnitude histogram to obtain the adaptive high and low thresholds; and [PERSON] and [PERSON] [PERSON] and [PERSON] (2014) used the differential operation to calculate the gradient magnitude histogram to obtain the adaptive high and low thresholds, wherein the low threshold was set to be 0.4 times the high threshold. The TCEDA proposed in this paper and the above four adaptive Canny algorithms were implemented using Visual C++ 2015. The source of the images tested in this study was the well-known web album Flickr Flickr (2015), which is a virtual web album that provides photo-sharing and has a rich and diverse range of images. There are tens of billions of digital images stored on the website, and the number is still increasing. Through the defined keywords, the search engine can quickly find the photo you want. After multiple image tests, five images of different attributes were selected from the Flickr website (Figures 14-18) as test images and the proposed TCEDA was compared with the above four adaptive Canny algorithms analysis. Figure 13: The effect of the proposed algorithm on edge detection. (**a**) Original image; (**b**) image after edge texture construction. Figure 14: Hotel image comparison results of different methods. (**a**) Original image (640 \(\times\) 427) [17]; (**b**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 233, TH\({}_{\rm L}\) = 117); (**c**) [PERSON] et al. proposed method (TH\({}_{\rm H}\) = 233, TH\({}_{\rm L}\) = 38); (**d**) [PERSON] et al.’s method (TH\({}_{\rm H}\) = 141, TH\({}_{\rm L}\) = 41); (**e**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 62, TH\({}_{\rm L}\) = 24); (**f**) TCEDA. Figure 15: Glass case image comparison results of different methods. (**a**) Original image (640 \(\times\) 426) [20]; (**b**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 148, TH\({}_{\rm L}\) = 74); (**c**) [PERSON] et al. proposed method (TH\({}_{\rm H}\) = 148, TH\({}_{\rm L}\) = 17); (**d**) [PERSON] et al.’s method (TH\({}_{\rm H}\) = 106, TH\({}_{\rm L}\) = 27); (**e**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 65, TH\({}_{\rm L}\) = 26); (**f**) TCEDA. Figure 16: Embossed tile image comparison results of different methods. (**a**) Original image (640 \(\times\) 480) [21]; (**b**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 238, TH\({}_{\rm L}\) = 119); (**c**) [PERSON] et al. proposed method (TH\({}_{\rm H}\) = 238, TH\({}_{\rm L}\) = 20); (**d**) [PERSON] et al.’s method (TH\({}_{\rm H}\) = 102, TH\({}_{\rm L}\) = 31); (**e**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 74, TH\({}_{\rm L}\) = 29); (**f**) TCEDA. Figure 17: Hakka Compound image comparison results of different methods. (**a**) Original image (640 \(\times\) 426) [22]; (**b**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 149, TH\({}_{\rm L}\) = 75); (**c**) [PERSON] et al. proposed method (TH\({}_{\rm H}\) = 149, TH\({}_{\rm L}\) = 21); (**d**) [PERSON] et al.’s method (TH\({}_{\rm H}\) = 124, TH\({}_{\rm L}\) = 38); (**e**) [PERSON] and [PERSON]’s method (TH\({}_{\rm H}\) = 112, TH\({}_{\rm L}\) = 44); (**f**) TCEDA. Figure 14 shows the results of a comparison of hotel images using different methods. The high-threshold \(\mathrm{TH_{H}}\) and the low-threshold \(\mathrm{TH_{L}}\) obtained by all methods are described below.