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## Object Detection in a Cluttered Scene Using Point Feature Matching

This example shows how to detect a particular object in a cluttered scene, given a reference image of the object.

### Overview

This example presents an algorithm for detecting a specific object based on finding point correspondences between the reference and the target image. It can detect objects despite a scale change or in-plane rotation. It is also robust to small amount of out-of-plane rotation and occlusion.

This method of object detection works best for objects that exhibit non-repeating texture patterns, which give rise to unique feature matches. This technique is not likely to work well for uniformly-colored objects, or for objects containing repeating patterns. Note that this algorithm is designed for detecting a specific object, for example, the elephant in the reference image, rather than any elephant. For detecting objects of a particular category, such as people or faces, see `vision.PeopleDetector` and `vision.CascadeObjectDetector`.

### Step 1: Read Images

Read the reference image containing the object of interest.

```boxImage = imread('stapleRemover.jpg'); figure; imshow(boxImage); title('Image of a Box'); ```

Read the target image containing a cluttered scene.

```sceneImage = imread('clutteredDesk.jpg'); figure; imshow(sceneImage); title('Image of a Cluttered Scene'); ```

### Step 2: Detect Feature Points

Detect feature points in both images.

```boxPoints = detectSURFFeatures(boxImage); scenePoints = detectSURFFeatures(sceneImage); ```

Visualize the strongest feature points found in the reference image.

```figure; imshow(boxImage); title('100 Strongest Feature Points from Box Image'); hold on; plot(selectStrongest(boxPoints, 100)); ```

Visualize the strongest feature points found in the target image.

```figure; imshow(sceneImage); title('300 Strongest Feature Points from Scene Image'); hold on; plot(selectStrongest(scenePoints, 300)); ```

### Step 3: Extract Feature Descriptors

Extract feature descriptors at the interest points in both images.

```[boxFeatures, boxPoints] = extractFeatures(boxImage, boxPoints); [sceneFeatures, scenePoints] = extractFeatures(sceneImage, scenePoints); ```

### Step 4: Find Putative 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(boxImage, sceneImage, matchedBoxPoints, ... matchedScenePoints, 'montage'); title('Putatively Matched Points (Including Outliers)'); ```

### Step 5: Locate the Object in the Scene Using Putative Matches

`estimateGeometricTransform` calculates the transformation relating the matched points, while eliminating outliers. This transformation allows us to localize the object in the scene.

```[tform, inlierBoxPoints, inlierScenePoints] = ... estimateGeometricTransform(matchedBoxPoints, matchedScenePoints, 'affine'); ```

Display the matching point pairs with the outliers removed

```figure; showMatchedFeatures(boxImage, sceneImage, inlierBoxPoints, ... inlierScenePoints, 'montage'); title('Matched Points (Inliers Only)'); ```

Get the bounding polygon of the reference image.

```boxPolygon = [1, 1;... % top-left size(boxImage, 2), 1;... % top-right size(boxImage, 2), size(boxImage, 1);... % bottom-right 1, size(boxImage, 1);... % bottom-left 1, 1]; % top-left again to close the polygon ```

Transform the polygon into the coordinate system of the target image. The transformed polygon indicates the location of the object in the scene.

```newBoxPolygon = transformPointsForward(tform, boxPolygon); ```

Display the detected object.

```figure; imshow(sceneImage); hold on; line(newBoxPolygon(:, 1), newBoxPolygon(:, 2), 'Color', 'y'); title('Detected Box'); ```

### Step 7: Detect Another Object

Detect a second object by using the same steps as before.

Read an image containing the second object of interest.

```elephantImage = imread('elephant.jpg'); figure; imshow(elephantImage); title('Image of an Elephant'); ```

Detect and visualize feature points.

```elephantPoints = detectSURFFeatures(elephantImage); figure; imshow(elephantImage); hold on; plot(selectStrongest(elephantPoints, 100)); title('100 Strongest Feature Points from Elephant Image'); ```

Extract feature descriptors.

```[elephantFeatures, elephantPoints] = extractFeatures(elephantImage, elephantPoints); ```

Match Features

```elephantPairs = matchFeatures(elephantFeatures, sceneFeatures, 'MaxRatio', 0.9); ```

Display putatively matched features.

```matchedElephantPoints = elephantPoints(elephantPairs(:, 1), :); matchedScenePoints = scenePoints(elephantPairs(:, 2), :); figure; showMatchedFeatures(elephantImage, sceneImage, matchedElephantPoints, ... matchedScenePoints, 'montage'); title('Putatively Matched Points (Including Outliers)'); ```

Estimate Geometric Transformation and Eliminate Outliers

```[tform, inlierElephantPoints, inlierScenePoints] = ... estimateGeometricTransform(matchedElephantPoints, matchedScenePoints, 'affine'); figure; showMatchedFeatures(elephantImage, sceneImage, inlierElephantPoints, ... inlierScenePoints, 'montage'); title('Matched Points (Inliers Only)'); ```

Display Both Objects

```elephantPolygon = [1, 1;... % top-left size(elephantImage, 2), 1;... % top-right size(elephantImage, 2), size(elephantImage, 1);... % bottom-right 1, size(elephantImage, 1);... % bottom-left 1,1]; % top-left again to close the polygon newElephantPolygon = transformPointsForward(tform, elephantPolygon); figure; imshow(sceneImage); hold on; line(newBoxPolygon(:, 1), newBoxPolygon(:, 2), 'Color', 'y'); line(newElephantPolygon(:, 1), newElephantPolygon(:, 2), 'Color', 'g'); title('Detected Elephant and Box'); ```

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