Stuck on computer vision example

Hi I am trying to implement the computer vision example found on

http://www.mathworks.com/help/vision/examples/detect-objects-in-a-cluttered-scene-using-point-feature-matching.html?prodcode=VP&language=en

and this is my code:

clear
clc

%%Step1: Read images
% Read reference Image containing the object to be recognised
boxImage = imread('stapleRemover.jpg');

%Convert image to 2D grayscale. Needed by  detectSURFFeatures() function.
grayscaleboxImage = rgb2gray(boxImage);

figure; imshow(grayscaleboxImage);
title('Image of a box');

% Read the target image containing the cluttered scene
sceneImage = imread('clutteredImage.jpg');

%Convert image to 2D grayscale. Needed by  detectSURFFeatures() function.
grayscaleSceneImage = rgb2gray(sceneImage);

figure; imshow(grayscaleSceneImage);
title('Image of a Cluttered Scene');

%%Step2: Detect Feature Points
%Detect feature points in both images
boxPoints = detectSURFFeatures(grayscaleboxImage);
scenePoints = detectSURFFeatures(grayscaleSceneImage);

% visualize the strongest feature points found in the reference image
figure; imshow(grayscaleboxImage);
title('100 strongest feature points from Box Image');
hold on;
plot(boxPoints.selectStrongest(100));

% visualize the strongest feature points found in the target image
figure; imshow(grayscaleSceneImage);
title('3000 stongest feature points from scene Image');
hold on;
plot(scenePoints.selectStrongest(300));

%%Step3: Extract feature descriptors
% xtract feature descriptors at the interest points in both images 

[boxFeatures,boxPoints] = extractFeatures(grayscaleboxImage,boxPoints);
[sceneFeatures,scenePoints] = extractFeatures(grayscaleSceneImage,scenePoints);

%%Step4: Find Putative (assumed) Point Matches

%Match the features using their descriptors.
boxPairs = matchFeatures(boxFeatures,sceneFeatures);

%Display putatively matched features
matchedBoxPoints = boxPoints(boxPairs(:,1),:);
matchedScenePoints = scenePoints(boxPairs(:,2),:);
figure;

showMatchedFeatures(grayscaleboxImage,grayscaleSceneImage,matchedBoxPoints,matchedScenePoints,'montage');
title('Putatively Matched Points (Including Outliers)');

I get this error message:

Warning: Image is too big to fit on screen; displaying at 50% 
> In imuitools\private\initSize at 72
  In imshow at 259
  In ComputerVisionExample at 20 
Warning: Image is too big to fit on screen; displaying at 50% 
> In imuitools\private\initSize at 72
  In imshow at 259
  In ComputerVisionExample at 35 
Error using assert
Too many input arguments.

Error in cvstGetCoordsChoice (line 47)
        assert(strncmp(clientName, 'fcn', 3) || ...

Error in extractFeatures (line 128)
isXY = cvstGetCoordsChoice('fcnExtractFeatures');

Error in ComputerVisionExample (line 43)
[boxFeatures,boxPoints] = extractFeatures(grayscaleboxImage,boxPoints);

I do not know where I went wrong. Can anyone help out?

Answer 1

[Walter Roberson]

Checking around, it appears most probably that you have your own assert.m that is being used instead of the MATLAB assert.m . Please check with

which -all assert

and probably the first entry is something you have added to your MATLAB path.