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\subsection{Αναδρομικός normalized cuts} |
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Η εκδοχή της αναδρομικής εκτέλεσης παρουσιάζεται στο script \texttt{demo3c}. Το script εκτελεί μία σειρά από δύο πειράματα κατά τα οποία γίνεται κατάτμηση δύο δοσμένων εικόνων (``\texttt{d2a}'' και ``\texttt{d2b}'') σε ομάδες (clusters) διαδοχικά. Τα αποτελέσματα φαίνονται και σχολιάζονται στη συνέχεια. |
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Στο πρώτο μέρος του script εκτελείται το πείραμα για την πρώτη εικόνα που δίνεται (``\texttt{d2a}''). Το αποτέλεσμα της κατάτμησης φαίνεται παρακάτω. |
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\begin{multicols}{2} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/demo3c_e1_f1.pdf_tex}} |
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} |
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\caption{Αρχική εικόνα (``\texttt{d2a}'')} |
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\end{figure} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/demo3c_e1_f2.pdf_tex}} |
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} |
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\caption{Κατάτμηση πρώτης εικόνας (``\texttt{d2a}''), τυχαίος χωματισμός των ομάδων} |
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\end{figure} |
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\end{multicols} |
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Σε αυτή τη περίπτωση ο αλγόριθμος κάνει τη βέλτιστη ομαδοποίηση, σταματώντας στις τρεις ομάδες και αντιστοιχίζοντας μία ομάδα σε κάθε χρώμα της αρχικής εικόνας. |
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Στο δεύτερο μέρος του script εκτελείται το πείραμα για την δεύτερη εικόνα που δίνεται (``\texttt{d2b}''). Το αποτέλεσμα της κατάτμησης φαίνεται παρακάτω. |
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\begin{multicols}{2} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/demo3c_e2_f1.pdf_tex}} |
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} |
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\caption{Αρχική εικόνα (``\texttt{d2b}'')} |
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\end{figure} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/demo3c_e2_f3.pdf_tex}} |
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} |
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\caption{Κατάτμηση πρώτης εικόνας (``\texttt{d2b}''), τυχαίος χωματισμός των ομάδων} |
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\end{figure} |
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\end{multicols} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/demo3c_e2_f2.pdf_tex}} |
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} |
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\caption{Κατάτμηση πρώτης εικόνας (``\texttt{d2b}''), χρωματικός διάμεσος} |
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\end{figure} |
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Στη δεύτερη εικόνα ο αλγόριθμος χωρίζει σε τρεις ομάδες με πολύ ικανοποιητικά αποτελέσματα. |
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\section{Εκτέλεση και αποτελέσματα} |
\section{Εκτέλεση και αποτελέσματα} |
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% Παρακάτω παρατίθενται ενδεικτικά οι χρόνοι εκτέλεσης των βασικών συναρτήσεων για επιλεγμένες διαστάσεις τελικής εικόνας 960Χ1280 καθώς: |
Από τη προηγούμενη ανάλυση βγαίνει το συμπέρασμα ότι η αναδρομική εκδοχή του normalized cuts αλγόριθμου δίνει τα βέλτιστα αποτελέσματα. Για την περαιτέρω επίδειξη της ποιότητας του αλγορίθμου, η αναδρομική εκδοχή του δοκιμάστηκε σε πραγματικές εικόνες. Τα αποτελέσματα φαίνονται παρακάτω. |
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% \begin{table}[H] |
\begin{multicols}{2} |
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% \centering |
\begin{figure}[H] |
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% \begin{tabular}{l l} \toprule |
\centering |
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% Συνάρτηση & Χρόνος (seconds) \\ \midrule |
\resizebox{.5\textwidth}{!}{ |
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% \texttt{bayer2rgb} (``nearest'') & 0.164 \\ |
\colorbox{shadecolor}{\input{res/exp_1_o.pdf_tex}} |
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% \texttt{bayer2rgb} (``linear'') & 0.484 \\ |
} |
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% \texttt{imagequant} & 0.0315 \\ |
\caption{Αρχική εικόνα} |
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% \texttt{imagedequant} & 0.024 \\ |
\end{figure} |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/exp_1_c.pdf_tex}} |
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} |
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\caption{Κατάτμηση εικόνας, χρωματικός διάμεσος} |
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\end{figure} |
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\end{multicols} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/exp_2_o.pdf_tex}} |
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} |
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\caption{Αρχική εικόνα} |
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\end{figure} |
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\begin{figure}[H] |
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\centering |
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\resizebox{.5\textwidth}{!}{ |
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\colorbox{shadecolor}{\input{res/exp_2_c.pdf_tex}} |
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} |
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\caption{Κατάτμηση εικόνας, χρωματικός διάμεσος} |
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\end{figure} |
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\end{multicols} |
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%% Creator: Inkscape inkscape 0.92.3, www.inkscape.org |
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%% Creator: Inkscape inkscape 0.92.3, www.inkscape.org |
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%% PDF/EPS/PS + LaTeX output extension by Johan Engelen, 2010 |
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%% Accompanies image file 'exp_1_o.pdf' (pdf, eps, ps) |
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image = imageT; |
image = imageT; |
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k = 3; |
k = 4; |
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graph = Image2Graph(image); |
graph = Image2Graph(image); |
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%clusters = mySpectralClustering(graph, k); |
%clusters = mySpectralClustering(graph, k); |
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clusters = myNCuts(graph, k); |
%clusters = myNCuts(graph, k); |
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clusters = recursiveNCuts(graph); |
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clusters = reshape(clusters, size(image, 1), []); |
%calculateNcut(graph, clusters); |
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unique(clusters) |
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redChannel = image(:, :, 1); |
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greenChannel = image(:, :, 2); |
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blueChannel = image(:, :, 3); |
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segImR = clusters; |
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segImG = clusters; |
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segImB = clusters; |
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for cluster = 1:k |
clusters = reshape(clusters, size(image, 1), []); |
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meanR = mean(redChannel(clusters == cluster)); |
imshow(meanClustersColorRGB(imageT, clusters)) |
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meanG = mean(greenChannel(clusters == cluster)); |
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meanB = mean(blueChannel(clusters == cluster)); |
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segImR(clusters == cluster) = meanR; |
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segImG(clusters == cluster) = meanG; |
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segImB(clusters == cluster) = meanB; |
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end |
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segIm = zeros(size(image, 1), size(image, 2), 3); |
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segIm(:, :, 1) = segImR; |
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segIm(:, :, 2) = segImG; |
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segIm(:, :, 3) = segImB; |
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imshow(segIm) |
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|
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clearvars segImR segImG segImB meanR meanG meanB graph redChannel ... |
clearvars graph clusters k image |
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greenChannel blueChannel clusters k image cluster segIm |
|
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Loading…
Reference in new issue