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The purpose of this paper is to develop a robot vision system for surface defect detection of 3D objects. It aims at the ill‐defined qualitative items such as stains and scratches.

A robot vision system for surface defect detection may counter: high surface reflection at some viewing angles; and no reference markers in any sensed images for matching. A filtering process is used to separate the illumination and reflection components of an image. An automatic marker‐selection process and a template‐matching method are then proposed for image registration and anomaly detection in reflection‐free images.

Tests were performed on a variety of hand‐held electronic devices such as cellular phones. Experimental results show that the proposed system can reliably avoid reflection surfaces and effectively identify small local defects on the surfaces in different viewing angles.

The results have practical implications for industrial objects with arbitrary surfaces.

Traditional visual inspection systems mainly work for two‐dimensional planar surfaces such as printed circuit boards and wafers. The proposed system can find the viewing angles with minimum surface reflection and detect small local defects under image misalignment for three‐dimensional objects.

This study aims to propose a door detection method based on the door properties in both depth and gray-level images. It can further help blind people (or mobile robots) find the doorway to their destination.

The proposed method uses the hierarchical point–line region principle with majority vote to encode the surface features pixel by pixel, and then dominant scene entities line by line, and finally the prioritized scene entities in the center, left and right of the observed scene.

This approach is very robust for noise and random misclassification in pixel, line and region levels and provides sufficient information for the pathway in the front and on the left and right of a scene. The proposed robot vision-assist system can be worn by visually impaired people or mounted on mobile robots. It provides more complete information about the surrounding environment to guide safely and effectively the user to the destination.

In this study, the proposed robot vision scheme provides detailed configurations of the environment encountered in daily life, including stairs (up and down), curbs/steps (up and down), obstacles, overheads, potholes/gutters, hazards and accessible ground. All these scene entities detected in the environment provide the blind people (or mobile robots) more complete information for better decision-making of their own. This paper also proposes, especially, a door detection method based on the door’s features in both depth and gray-level images. It can further help blind people find the doorway to their destination in an unfamiliar environment.

Mobile robots become more and more important for many potential applications such as navigation and surveillance. The paper proposes an image processing scheme for moving object detection from a mobile robot with a single camera. It especially aims at intruder detection for the security robot on either smooth paved surfaces or uneven ground surfaces.

The core of the proposed scheme is the template matching with basis image reconstruction for the alignment between two consecutive images in the video sequence. The most representative template patches in one image are first automatically selected based on the gradient energies in the patches. The chosen templates then form a basis matrix, and the instances of the templates in the subsequent image are matched by evaluating their reconstruction error from the basis matrix. For the two well-aligned images, a simple and fast temporal difference can thus be applied to identify moving objects from the background.

The proposed template matching can tolerate in rotation (±10°) and (±10°) in scaling. By adding templates with larger rotational angles in the basis matrixes, the proposed method can be further extended for the match of images from severe camera vibrations. Experimental results of video sequences from a non-stationary camera have shown that the proposed scheme can reliably detect moving objects from the scenes with either minor or severe geometric transformation changes. The proposed scheme can achieve a fast processing rate of 32 frames per second for an image of size 160×120.

The basic approaches for moving object detection with a mobile robot are feature-point match and optical flow. They are relatively computational intensive and complicated to implement for real-time applications. The proposed template selection and template matching are very fast and easy to implement. Traditional template matching methods are based on sum of squared differences or normalized cross correlation. They are very sensitive to minor displacement between two images. The proposed new similarity measure is based on the reconstruction error from the test image and its reconstruction from the linear combination of the templates. It is thus robust under rotation and scale changes. It can be well suited for mobile robot surveillance.