Build model detection after features extraction

1 visualización (últimos 30 días)
Ilan Moshe
Ilan Moshe el 10 de Mayo de 2021
Respondida: Manas Meena el 13 de Mayo de 2021
Hello,
I'm trying to code a nose detection function from a IR video.
I extracted 2 frames from the video and foud the features and compared between them.
ref_img = imread('frame_1.png');
ref_img_gray=rgb2gray(ref_img);
ref_pts=detectSURFFeatures(ref_img_gray);
[ref_features,ref_validPts]=extractFeatures(ref_img_gray,ref_pts);
figure; imshow(ref_img);
hold on; plot(ref_pts.selectStrongest(50));
image=imread('frame_50.png');
I=rgb2gray(image);
I_pts=detectSURFFeatures(I);
[I_features,I_validPts]=extractFeatures(I,I_pts);
figure;imshow(image);
hold on; plot(I_pts.selectStrongest(50));
index_pairs=matchFeatures(ref_features,I_features);
ref_matched_pts=ref_validPts(index_pairs(:,1)).Location;
I_matched_pts=I_validPts(index_pairs(:,2)).Location;
close all
figure,showMatchedFeatures(image,ref_img,I_matched_pts,ref_matched_pts);
Here the figure obtained :
What I have to do as a next step ? We can see from the figure that we got the 2 nostrils as features, so how to train a model a got a function that tracks the region for all the frames ?
thank you

Respuesta aceptada

Manas Meena
Manas Meena el 13 de Mayo de 2021
After SURF feature detection you can select the strongest points of interest (eg. nostrils) and the use the vision.PointTracker function to track these selected points in the video.

Más respuestas (0)

Categorías

Más información sobre Computer Vision with Simulink en Help Center y File Exchange.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!

Translated by