## 4 Discussion  
### ISME Recognition  
This research was intended to propose a method that can automatically and accurately calculate the relative distance between the two micro-objects in a microscopic image and facilitate automated micromanipulation research, e.g., ISME and detected objects. In this work, ISME recognition using the proposed algorithm was a crucial step for subsequent operations. However, there were some differences in each ISME's shape, and each ISME may have been randomly rotated to some angle in the experiment. The template matching-based algorithms  
Figure 19: Tip of ISME and root identification. Figure 20: Distance error distribution over the 41 tracked frames. were sensitive to noise without gray scale invariance and scale invariance. Accordingly, without continually changing the ISME templet in the ISME detection, the recognition rates could hardly meet the requirements (Figure 13). Besides that, the time cost of the template matching algorithm was higher than the best cascade classifier (Table 1). The results suggest that the improved LBP features with the advantages of both gray scale invariance and scale invariance, as well as the Haar-like features with rich edge information, are suitable for object detection in complex situations (Table 1). The performance of the proposed methods is dependent on image resolution and the amount of training data. Our results show that with the improvement of image resolution of training set, more effective features were detected, which improved the detection rate of classifiers but caused an increase of training time. When the training data set was small, the detection rate of Haar-like cascade classifier was slightly better than that of the LBP cascade classifier. With the increase of training data in the same resolution of training samples, the detection rate of the LBP cascade classifier was higher than that of the Haar-like cascade classifier. Additionally, the training time of the Haar-like cascade classifier was much longer than that of the LBP cascade classifier. When the resolution of the sample was relatively larger (e.g., \(48\times 48\)) the training speed of the Haar-like cascade classifier became very slow--even the training of a weak classifier needed more than one day. Therefore, the LBP cascade classifier is a better choice when there is little difference in detection rate between the two kinds of classifiers. Between the ISME detection with Haar-like and LBP cascade classifiers, the Haar-like cascade classifiers had lower time costs. Therefore, Haar-like cascade classifiers are the better choice when real-time requirement is the primary choice. The cascade classifier with both Haar-like and LBP features had the best detection rate. This shows that the detection rate of an ISME cascade classifier can be improved by a multi-feature fusion method. In the in-situ ion flux measurement process of a live tissue, the ISME was kept at 5-30 \(\upmu\)m from the measured object to ensure accuracy and a growth speed of the root is 1-5 \(\upmu\)m/min [13; 19]. Thus, we needed monitor the root at least one time per minute. According to the processing time cost of each frame (Table 1), the frame rate could achieve 4.35-14 frames/s at a resolution of \(1360\times 1024\), which could perfectly meet the requirement. In addition, the reasonable selection of training samples' resolution could effectively reduce training time and improve detection rate. Because the width-to-height ratio of the ISME tip was about 3:1, when the width-to-width ratio of the training sample was 1:1, there was a large number of irrelevant pixels in the training sample. These irrelevant pixels increased the computation of feature extraction, thus increasing the training time. What is worse, some disturbing features were extracted from irrelevant pixels in the feature extraction process, which increased the difficulty of subsequent feature selection.