Line Detection Implementation in MATLAB Environment

I have implemented line detection in MATLAB environment and achieved excellent results. This algorithm implementation primarily involves the following steps with corresponding code considerations: 1. Image preprocessing operations including noise removal and contrast enhancement to facilitate subsequent line detection. In MATLAB, this typically involves using functions like imnoise() for noise simulation, medfilt2() for median filtering, and imadjust() or histeq() for contrast enhancement. 2. Applying Hough transform to detect lines in images. This process requires appropriate parameter selection for the Hough transform to ensure high-quality line detection. Key MATLAB functions involved are hough() for transform computation and houghpeaks() for identifying significant peaks in the Hough space. Parameter tuning includes setting appropriate theta resolution and Rho values. 3. Post-processing of detected lines, such as filtering the longest lines and merging adjacent lines to obtain final detection results. This can be implemented using houghlines() function combined with custom logic for line segment analysis, including calculating line lengths using Euclidean distance and applying threshold-based filtering. If you're interested in image processing, I encourage you to try this line detection algorithm. It incorporates robust implementation techniques that should provide valuable insights and practical experience in computer vision applications. The code structure emphasizes parameter optimization and result validation for reliable performance across different image types.