One method, which may not necessarily correlate to a boxplot in all situations (but probably works for the default boxplot), is to use isoutlier:
T = [1;-1;0.5;-0.5;0.75;-0.75;-6;-7;4;3];
outliers = isoutlier(T);
numOutliers = sum(outliers)
h = boxplot(T,'Labels',{'1'});
If you need to be sure you have the exact count in the boxplot, you can get the data in the image and drill down to find the outliers:
ax = gca; % Get current axes
bxplt = ax.Children; % The boxplot
plts = bxplt.Children % Individual plots that make up the boxplot.
% plts(1) holds the outliers
numRedMarks = numel(plts(1).YData)
% XData or YData -- points that make up the Outliers plot
To programmatically find the plots of outliers (in the event that you have more than one box):
idxs = find(strcmp({plts.Tag},'Outliers'));
outlierCell = {plts(idxs).YData}
numMarksFirstPlot = numel(outlierCell{1})
