Image Processing Coursework System For Recognition Of H

Image Processing Courseworka System For The Recognition Of Hand Gestur

Extracted assignment instructions: Prepare a test image of your hand by removing interfering objects, and capture a color photo of your left hand with your SID written on a white paper. Load the image into MATLAB, produce a greyscale version using the standard conversion equation, and display both images side by side. Collect and display statistics: plot normalized histograms of RGB and greyscale images, report extrema and means, and examine histograms for exposure issues. Use thresholding methods to isolate each fingertip with appropriate thresholds and display results. Overlay isolated fingertips on the greyscale image, and report cluster sizes. Calculate mean coordinates of fingertip clusters, annotate with circles, connect adjacent fingertips with green lines, and compute line lengths. Segment the skin from the background using suitable thresholds in RGB and alternative color spaces like HSV; apply edge detection methods (Roberts, Prewitt, Sobel, Canny), trace contours, and annotate boundary overlays. All testing should be performed on MATLAB, with code properly formatted and commented, including figures with labels and high-quality exports.

Paper For Above instruction

The development of an automated hand gesture recognition system via image processing techniques involves several sequential steps, beginning with image acquisition and culminating in detailed analysis of hand features. This project aims to leverage MATLAB's image processing toolbox to detect, isolate, and analyze hand images for gesture recognition purposes. The process starts with capturing a high-quality image of the hand placed on a white background to facilitate segmentation. The image is then converted to greyscale using the standard luminosity method, which involves a weighted sum of the RGB channels, represented as:

Grey = 0.2989 × R + 0.5870 × G + 0.1140 × B

This formula ensures a perceptually uniform conversion that aligns with human visual sensitivity. Both the original RGB image and its greyscale counterpart are displayed side-by-side to verify the conversion process visually. Collecting statistical data such as histograms of color intensities provides insights into the image's exposure and color balance. Normalized histograms offer a uniform scale for comparison, where the maxima, minima, and means for the Red, Green, and Blue channels are computed and plotted with vertical dotted bars to visualize central tendencies.

Thresholding plays a pivotal role in isolating each colored fingertip. Using trial-and-error experimentation, suitable high, low, bandpass, or bandreject thresholds are set for each RGB channel to generate binary masks that highlight the fingertips. For example, thresholds for the red channel could be set to isolate the thumb in the respective hue range, while similar thresholds are used for other fingers with their assigned colors (orange, yellow, blue, green, and red). The masks are displayed alongside isolated pixel maps, illustrating the effectiveness of the thresholding methods in capturing fingertip regions while suppressing background noise.

Overlaying the fingertips onto the greyscale hand image enhances the visualization of fingertip locations. Determining the size of each fingertip cluster involves calculating the number of pixels within each binary mask, revealing the relative sizes of the identified fingertip regions. Subsequently, the centroid of each cluster is computed by averaging the pixel coordinates, which serve as the central points of each fingertip cluster. These centroids are marked visually with circles, and lines are drawn between adjacent fingertips using Euclidean distance calculations to measure the span between the tips, with line lengths reported accordingly.

For skin segmentation, thresholds suitable for skin color are devised based on empirical data or research literature, often involving the HSV color space due to its robustness against illumination variations. Conversion from RGB to HSV allows thresholds set within the hue and saturation ranges characteristic of skin tones, producing binary masks highlighting skin regions. This segmentation is refined further by applying edge detection algorithms—Roberts, Prewitt, Sobel, and Canny—to extract the outer boundary contours of the hand. Each method's edge map is then overlaid on the original image, with red lines tracing the contours, facilitating visual assessment of accuracy.

In conclusion, this process combines color segmentation, morphological filtering, edge detection, and spatial analysis to develop a comprehensive framework for hand gesture recognition. Accurate fingertip detection and spatial relationship analysis form the basis for interpreting gestures—such as open palm or finger positions—that are crucial in human-computer interaction applications. The system's success hinges on precise thresholding, robust segmentation, and reliable feature extraction, all of which are meticulously validated through MATLAB's visualization tools and quantitative metrics.

References

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