How to Detect Edges of an Image using Canny Edge Detection technique. I have already detected edges of Images, but I'm not sure if it is correct or not. Also, I want to add legend command and axis information in this, how would I do this thing?

DICE Sorensen-Dice similarity coefficient for image segmentation. SIMILARITY = DICE(BW1,BW2) computes the Sorensen-Dice similarity coefficient between segmented objects in binary images BW1 and BW2. SIMILARITY is a scalar double in [0,1]. A SIMILARITY of 1 means that the segmentations in BW1 and BW2 are a perfect match. SIMILARITY = DICE(L1,L2) computes the Dice index for each label in label images L1 and L2. SIMILARITY is a vector of doubles in [0,1], where the first coefficient is the Dice index for label 1, the second for label 2, and so on. SIMILARITY = DICE(C1,C2) computes the Dice index for each category in categorical images C1 and C2. SIMILARITY is a vector of doubles in [0,1], where the first coefficient is the Dice index for the first category, the second index for the second category, and so on. Notes ----- [1] The Dice similarity coefficient of two sets A and B is expressed as dice(A,B) = 2 * |intersection(A,B)| / (|A| + |B|) where |A| represents the cardinal of set A. It can also be expressed in terms of true positives (TP), false positives (FP) and false negatives (FN) as dice(A,B) = 2 * TP / (2 * TP + FP + FN) [2] The Dice index is related to the Jaccard index according to dice(A,B) = 2 * jaccard(A,B) / (1 + jaccard(A,B)) [3] The inputs BW, L and C can be 2-D or 3-D arrays. Example 1 --------- Compute the Dice similarity coefficient for a binary segmentation. % Read in an image with an object we wish to segment. A = imread('hands1.jpg'); % Convert the image to grayscale. I = rgb2gray(A); % Use active contours to segment the hand. mask = false(size(I)); mask(25:end-25,25:end-25) = true; BW = activecontour(I, mask, 300); % Read in the ground truth against which to compare the segmentation. BW_groundTruth = imread('hands1-mask.png'); % Compute the Dice index of this segmentation. similarity = dice(BW, BW_groundTruth); % Display both masks on top of one another. figure imshowpair(BW, BW_groundTruth) title(['Dice index = ' num2str(similarity)]) Example 2 --------- Compute the Dice similarity coefficient for n-ary segmentation. % Read in an image with several objects we wish to segment. RGB = imread('yellowlily.jpg'); % Create scribbles for three regions. region1 = [350 700 425 120]; % [x y w h] format BW1 = false(size(RGB,1),size(RGB,2)); BW1(region1(2):region1(2)+region1(4),region1(1):region1(1)+region1(3)) = true; region2 = [800 1124 120 230]; BW2 = false(size(RGB,1),size(RGB,2)); BW2(region2(2):region2(2)+region2(4),region2(1):region2(1)+region2(3)) = true; region3 = [20 1320 480 200; 1010 290 180 240]; BW3 = false(size(RGB,1),size(RGB,2)); BW3(region3(1,2):region3(1,2)+region3(1,4),region3(1,1):region3(1,1)+region3(1,3)) = true; BW3(region3(2,2):region3(2,2)+region3(2,4),region3(2,1):region3(2,1)+region3(2,3)) = true; % Display the seed regions on top of the image. figure imshow(RGB) hold on visboundaries(BW1,'Color','r'); visboundaries(BW2,'Color','g'); visboundaries(BW3,'Color','b'); title('Seed regions') % Segment the image into three regions using % geodesic distance-based color segmentation. L = imseggeodesic(RGB,BW1,BW2,BW3,'AdaptiveChannelWeighting',true); % Load a ground truth segmentation of the image into three regions. L_groundTruth = double(imread('yellowlily-segmented.png')); % Visually compare the segmentation results with the ground truth. figure imshowpair(label2rgb(L),label2rgb(L_groundTruth),'montage') title('Comparison of segmentation results (left) and ground truth (right)') % Compute the Dice similarity index % for each segmented region. similarity = dice(L, L_groundTruth) See also BFSCORE, JACCARD. Documentation for dice doc dice