Project 2 (Feature Detection and Extraction, Image Segmentation)

Object Detection in Images 

Explanation:

In this project activity, you will develop a simple object detection system using feature detection, feature extraction, and image segmentation techniques. Object detection is a fundamental task in computer vision that involves locating and classifying objects within an image. By combining the concepts covered in the lessons “Feature Detection and Extraction,” “Image Segmentation,” and “Object Recognition,” you will gain hands-on experience in detecting and recognizing objects in digital images.

Steps:

1. Dataset Selection: Choose a dataset that contains images with objects of interest. You can use publicly available datasets or create your own dataset by capturing images using a camera or collecting images from the internet. Ensure that the dataset includes images with different objects and various backgrounds.

2. Feature Detection and Extraction: Implement a feature detection algorithm, such as the Harris corner detector or Scale-Invariant Feature Transform (SIFT), to identify keypoints or distinctive features within the images. Extract the descriptors for these keypoints to represent the local characteristics of the objects.

3. Image Segmentation: Apply an image segmentation algorithm, such as the watershed algorithm or k-means clustering, to partition the images into meaningful regions. This will help separate objects from the background and provide a foundation for object detection.

4. Object Detection: Utilize the features detected in Step 2 and the segmented regions from Step 3 to develop an object detection algorithm. This algorithm should identify the presence and location of objects in the images based on the extracted features and the segmented regions.

5. Object Recognition: Implement an object recognition algorithm, such as template matching or machine learning-based approaches (e.g., Support Vector Machines or Convolutional Neural Networks), to classify the detected objects into predefined categories or classes. Train the recognition model using labeled data or pre-trained models if available.

6. Evaluation and Visualization: Evaluate the performance of your object detection and recognition system by comparing the detected and recognized objects with the ground truth annotations or labels from the dataset. Calculate metrics such as precision, recall, and accuracy to assess the system’s performance. Additionally, visualize the results by displaying the detected objects with bounding boxes and their corresponding class labels.

By completing this project activity, you will have gained practical experience in building a basic object detection and recognition system using feature detection, extraction, and image segmentation techniques. This project serves as a stepping stone for further exploration into advanced object detection algorithms and their applications in computer vision tasks.