An imaging approach for the automatic thresholding of photo defects

• Evaluation on limitations of valley emphasis method for thresholding of photo defects.
• Proposed technique of photo defect detection involves imaging of defected photos by sensing the light after passing through them instead of sensing the reflection from their surfaces.
• The proposed approach is able to locate the defects with very small misclassification error. It overcomes the limitation of thresholding techniques to distinguish the defect objects in presence of similar photo objects.
• Proposed approach reduces the time complexity of defect detection by least 1/5 times as the dynamic range of intensity levels for defected pixels in histogram confine to 0–50 instead of 0–255.
• Presently the proposed approach is applied only for physically available photos having light transitivity.

Automatic thresholding of photo defects means to accurately locate defect objects. The available approaches for automatic thresholding determine the optimal threshold values and segment the image into objects based on their gray level distribution. For defect object identification in images with multimodal distributions, these techniques also require knowledge of the defect object features, such as shape and size, which limits the applicability of these techniques because the defect object features may vary widely. Additionally, these methods result in extensive misclassification errors in the presence of photo objects similar to defect objects and unimodal distribution. We evaluated the limitations of the valley emphasis method and proposed a new approach that involves the imaging of a defected photo by sensing the light after it passes through photo and then applying the valley emphasis method for thresholding to identify defect objects. The obtained results are better even with the above discussed constraints of the available automatic thresholding approaches. Although the proposed technique is applicable only for physically available objects, it may contribute significantly towards the accuracy of machine vision based applications.