A key step is to extract valid information region in the fusion of multi-voltage X-ray image sequence for complicated components. To improve the self-adaption of extraction, a method is presented in this paper. In this paper, the valid informa-tion region is selected by the grey level interval, which is computed by the optimization of image quality evaluation model. The model is based on the histogram equalization and the grey level interval. Then, every valid region of images at different voltages is extracted and they are fused according their grey level transformation function. The fusion image contains completed struc-ture information of the component. The fusion experiment of a cylinder head shows the effectiveness of the presented method.
In order to detect and process underground vibration signal, this paper presents a system with the combination of software and hardware. The hardware part consists of sensor, memory chips, USB, etc. , which is responsible for capturing original signals from sensors. The software part is a virtual oscilloscope based on LabWindows/CVI （C vitual instrument）, which not only has the functions of traditional oscilloscope but also can analyze and process vibration signals in special ways. The experimental results show that the designed system is stable, reliable and easy to be operated, which can meet practical requirements.
The conventional X-ray gray weighted image fusion method based on variable energy cannot characterize the phys- ical properties of complicated objects correctly, therefore, the gray correction method of X-ray fusion image based on neural network is proposed. The conventional method acquires 12 bit images on variable energy, and then fuses the images in a tra- ditional way. While the new method takes the fusion image as the input of neural network simulation system and takes the acquired 16 bit image as the output of neural network. The X-ray image physical characteristic model based on neural net- work is obtained through training. And then it takes steel ladder block as the test object to verify the feasibility of the mod- el. In the end, the gray curve of output image is compared with the gray curve of 16 bit real image. The experiment results show that this method can fit the nonlinear relationship between the fusion image and the real image, and also can expand the scope of application of low dynamic image acquisition equipment.
Real-time detection for object size has now become a hot topic in the testing field and image processing is the core algorithm. This paper focuses on the processing and display of the collected dynamic images to achieve a real-time image pro- cessing for the moving objects. Firstly, the median filtering, gain calibration, image segmentation, image binarization, cor- ner detection and edge fitting are employed to process the images of the moving objects to make the image close to the real object. Then, the processed images are simultaneously displayed on a real-time basis to make it easier to analyze, understand and identify them, and thus it reduces the computation complexity. Finally, human-computer interaction （HCI）-friendly in- terface based on VC ＋＋ is designed to accomplish the digital logic transform, image processing and real-time display of the objects. The experiment shows that the proposed algorithm and software design have better real-time performance and accu- racy which can meet the industrial needs.
X 射线递变能量成像是依次获取复杂结构件在递变能量下的局部有效信息，并通过多谱融合获取完整结构信息。但是目前的能量选择主要以人工设定管电压步进为主，无法匹配检测对象的有效厚度变化率，成像效率及射线利用率较低。基于递变能量成像规律，提出一种最佳 X 射线管电压预测算法。该方法通过对检测物体进行变能量预扫描，提取图像序列中有效厚度（高质量区域）和临近厚度（预测区域），建立有效厚度的图像灰度与管电压、X 射线光谱之间的物理模型，及临近厚度灰度差与电压的函数模型，进而得到临近厚度最佳成像时的能量预测模型。通过模型求解，实现了能量的自适应预测。以不同厚度钢块为对象，利用该算法逐一预测各个厚度钢块最佳成像时的管电压，并与实际值对比。实验结果显示，在低能时可跨3～4 mm 准确预测，高能时可跨7～10 mm 预测，精度可以达到95％以上。
