Image Matching of Two Images Using MATLAB
Implementing image matching of two images in MATLAB using two distinct algorithms: 1. Grayscale-based template matching 2. Improved template matching algorithm with GUI-based input/output interface.
Enhancing Robustness of Image Encryption Algorithms with Adaptive Approach
To improve the robustness of image encryption algorithms, we propose a novel adaptive image encryption technique. This algorithm partitions grayscale or color images of arbitrary sizes into 2×2 sub-blocks. Using pixel intensity values from the top-left sub-block and Chebyshev chaotic mapping, we construct a matrix matching the dimensions of the top-right sub-block for pixel value substitution. The encryption process follows a clockwise pattern until all sub-blocks including the top-left are processed. Implementation involves creating dynamic encryption keys through chaotic systems and applying pixel-level transformations using matrix operations, making the algorithm size-independent and suitable for both grayscale and color images with enhanced diffusion and confusion properties.
Displaying Grayscale Values of RGB Components Using MATLAB
Implementing RGB Component Grayscale Visualization with MATLAB through Image Loading, Color Channel Separation, and Grayscale Conversion Algorithms
Color-Component Based Vehicle License Plate Localization and Recognition
I developed a license plate recognition system based on color components. The main steps include: 1) Identifying license plates using grayscale values derived from color components (focused on blue plates) 2) Recognizing white characters within the identified blue regions. The implementation involves color space conversion, thresholding techniques, and morphological operations for accurate plate detection.
Fuzzy Local Information C-Means Clustering Algorithm
FLICM overcomes limitations of standard FCM while enhancing clustering performance. Its key feature involves a fuzzy local similarity measure incorporating spatial information and gray values, ensuring noise insensitivity and image detail preservation. MATLAB implementation demonstrates FLICM's superior robustness for noisy image segmentation compared to FCM, using neighborhood pixel analysis and adaptive membership functions.
Gray-Value-Based Encoding Representation Method for Images
In image template matching, although correlation algorithms based on pixel gray values are widely used, they suffer from high time complexity and sensitivity to brightness and size variations. To address these limitations, we propose a novel encoding representation method based on image gray values. This approach divides the image into fixed-size blocks (termed R-blocks), calculates the total gray value for each R-block, and encodes it based on its ordinal relationship with adjacent R-blocks. Template matching is achieved by comparing the encoded values of R-blocks. The computation of R-block encodings is simple, and matching only requires equality checks between codes, enabling fast comparison algorithms. The method demonstrates robustness to gray-level variations and noise.