MATLAB Code Implementation for Edge Detection and Object Extraction

The document describes two main aspects: edge detection and automatic extraction of target objects. Below I provide detailed explanations for both components with code implementation considerations.

1. Edge Detection:

In this phase, we first create a monochrome background image containing color blocks and lines. We apply low-pass filtering using MATLAB's imfilter() function with Gaussian or averaging kernels to generate a degraded image where edges appear blurred. Then we implement two edge detection algorithms - such as Sobel (edge(img,'sobel')) and Canny (edge(img,'canny')) - to detect edges of color blocks and lines, producing binary result images through thresholding operations. Subsequently, we extract boundary coordinates using functions like bwboundaries() or regionprops(), comparing them with ground truth coordinate data from the original image generation process. The analysis focuses on detection errors through metrics like Euclidean distance calculations and false positive/negative rates.

2. Automatic Object Extraction:

This stage involves capturing a black/white or color photograph containing target objects. We implement an edge detection algorithm (e.g., Prewitt, Roberts, or LoG) using MATLAB's edge detection toolbox for automatic object extraction. The process includes preprocessing steps like grayscale conversion (rgb2gray()) and noise reduction (medfilt2()), followed by morphological operations (imopen(), imclose()) to refine edges. Results are analyzed through boundary completeness assessment and segmentation accuracy evaluation using functions like bwareaopen() for noise removal and label2rgb() for visualization.

Through these two phases, we gain comprehensive understanding of edge detection techniques and automated object extraction, with detailed analysis of implementation results including algorithm performance comparisons and error quantification.