Image segmentation based on adaptive quaternion anisotropic gradient for optical inspection applications

Image segmentation is the critical step in different imaging and especially optical inspection applications: detection and recognition of objects, classification, analysis, and identification. Also, image gradient, as a preprocessing step, is an essential tool in image processing in many research areas, such as edge detection, segmentation, inpainting, etc. However, these tools have limitations and could be more accurate since the capture devices usually generate low-resolution images, which are primarily noisy and blurry. It is critical to receive useful gradient estimation on noisy color images while preserving the sharp edges. In the present paper, we develop a new gradient by integrating the quaternion framework with local polynomial approximation and the intersection of confidence intervals based on anisotropic gradient concepts for color image processing applications. We apply the proposed gradient technique in a modified active contour method to perform an automated segmentation for optical inspection applications. Computer simulations on the segmentation dataset for optical inspection applications show that the new adaptive quaternion anisotropic gradient exhibits fewer color artefacts than state-of-the-art techniques.