Abstract-In recent years, the demands for LED are increasing. For testing the quality of LEDs, a lot of LED probes are necessary, so the high precision and efficient methods are paid more attention by industrial applications. This paper is focused on the measurement of the angle and the radius of a LED probe by computer vision. In previous paper, we proposed an effective method based on Canny edge detection and curve fitting method with iteration. In this paper we add a new sub-pixel edge detection method: partial area effect. We improve the preciseness of angle from error 2.3% to 1.43% and enhance the accuracy of radius more than 30%.

In this paper, an improved lossless data hiding method with histogram shifting for medical images is proposed. In general, medical images consist of many pure black and white points. In the previous studies, it may need a lot of data as a location map to reconstruct the watermark and the cover image. To solve this problem, we present a new method to record the location map. We use two bits for each block to record the information of histogram shifting and one bit to denote the change of each pixel value on the cover image. The purpose of the former two bits is to avoid wrong information in the extracting process, while that of the latter one is to avoid overflow and underflow. Experimental results show that our method can reduce the size of location map up to 95.04% compared to the previous studies.

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LED probes are essential for testing the quality of LEDs, gaining its attention among industrial applications. Disturbance factors such as dust or noise affections may occur during the manufacturing process of the LED probes, which leads angle error to increase. With the increasing demand for LED probes, higher precision and efficiency are expected by users. Efficient method for edge detection and the preciseness of angle is crucial in our study. The previous study presents a method using Scharr Edge Detection and Adaptive Reconstruction. In this paper, we add a new method based on various segmentation and the averaging of sub-pixels(VSAS). Experimental result indicates that this method provides higher precision, with and an average error less than 1% compared to the other previous methods.