Multivariate analysis-based image enhancement model for machine vision inspection

Image enhancement is an essential procedure in machine vision-based inspection. In practical applications, image enhancement is usually a part of image pre-processing, intended to make the following inspection more effective. The image enhancement method is usually selected by trial-and-error or on the basis of experience. This paper presents an automatic procedure for fast and effective image enhancement. The procedure uses multivariate analysis to automatically construct an optimal image enhancement model. First, an optimally enhanced image was selected from the literature as a basis for the model. Then, the image features were identified and Wilks’ statistic was used for feature selection. Next, discriminate functions were built to select the optimal image enhancement method. To verify the model, 53 training images from the literature and 12 test images from a local company were used in an experimental analysis. The model achieved 98.11% accuracy in selecting the most suitable image enhancement method, and the average increase in contrast was 98% for the 53 training images. The enhancement method selection results for the 12 test images were also in agreement with the 53 training images from the literature. The results show that the proposed method is effective and appropriate for quickly improving image contrast.     

Automatic bubble defect inspection for microwave communication substrates using multi-threshold technique based co-occurrence matrix

The microwave communication substrate is currently using manual inspection from ultrasonic image. The bubble defect ratio is an important index for evaluating the quality of the substrate. It is difficult to formalise standards for the quality inspection procedure by an operator since different numbers of bubbles may be realised. This paper proposes a novel inspection procedure for the calculation of the bubble ratio of microwave communication substrate. First, the optimal threshold is determined for binary image segmentation on the basis of a co-occurrence matrix. Further, the image of the microwave communication substrate is divided into three parts, namely, the image skeleton area, soldering area, and IC working area. The image subtraction technique is used to obtain the global and critical regions for bubbles. After data analysis, if the critical region bubble ratio is larger than 10% of the critical region, then it is judged to be a defect. When a substrate is judged to be non-defective, we should further proceed with the global area detection. If the global region bubble ratio is larger than 12.3% of the global region, then it is judged as a defect. An experiment was conducted to demonstrate the application of this technique. The results showed that the inspection accuracy reached 97.49%. The results of this study provide an effective solution for the inspection of the interface bond quality of a microwave communication substrate.     

实际边缘轮廓下的螺栓综合尺寸质量检测

为了检测螺栓的尺寸质量,提出一种基于实际边缘轮廓的螺栓综合尺寸质量检测算法.该算法首先根据螺栓图纸公差要求,设计螺栓综合尺寸公差带图作为模板图;然后对螺栓图像进行预处理,包括三段线性灰度增强、中值滤波和最大类间方差法等;进一步将螺栓图像旋转为水平位姿并进行canny边缘检测,将螺栓图像和模板图裁剪,得到具有相同位姿、尺...     

Image Enhancement of Blurry DR Images Using FLIT-LBP Texture Descriptors

The blurring of DR image often affects defect inspection. In this paper, a novel method called FLIT-LBP for the enhancement of blurry DR image is presented. The proposed method utilizes finite line integral transform (FLIT) to extract direction information, based on which appropriate weight arrangement can be chosen. Weight arrangement is a sorting order of weights that converts binary codes to decimal codes in local binary patterns (LBP). And the central pixel information is extracted by computing the mean value in LBP. By combining the direction extraction of FLIT and the local comparison of LBP, FLIT-LBP is able to enhance blurry defect images with different directions. Experimental results show that FLIT-LBP performs better than LBP and FLIT respectively. In addition, in the case of defects with different contrast ratios lying in the same image, our method achieves better enhancement than grayscale stretch does.     

Binary Image Steganalysis Based on Distortion Level Co-Occurrence Matrix

In recent years, binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information security. In most state-of-the-art binary image steganographic schemes, they always find out the flippable pixels to minimize the embedding distortions. For this reason, the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial domain. However, the distortion maps can be calculated for cover and stego images and the difference between them is significant. In this paper, a novel binary image steganalytic scheme is proposed, which is based on distortion level co-occurrence matrix. The proposed scheme first generates the corresponding distortion maps for cover and stego images. Then the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego images. Finally, support vector machine, based on the gaussian kernel, is used to classify the features. Compared with the prior steganalytic methods, experimental results demonstrate that the proposed scheme can effectively detect stego images.     

Position-invariant, rotation-invariant, and scale-invariant process for binary image recognition

A novel recognition process is presented that is invariant under position, rotation, and scale changes. The recognition process is based on the Fang-H?usler transform [Appl. Opt. 29, 704 (1990)] and is applied to the autoconvolved image, rather than to the image itself. This makes the recognition process sensitive not only to the image histogram but also to its detailed pattern, resulting in a more reliable process that is also applicable to binary images. The proposed recognition process is demonstrated, by use of a fast algorithm, on several types of binary images with a real transform kernel, which contains amplitude, as well as phase, information. Good recognition is achieved for both synthetic and scanned images. In addition, it is shown that the Fang-Hausler transform is also invariant under a general affine transformation of the spatial coordinates.     

Practical image encryption and decryption by phase-coding technique for optical security systems

A method of image encryption and decryption is proposed for optical security systems. A phase-coded image to be encrypted together with a random phase pattern is Fourier transformed and the result of the complex-valued data is used as an encrypted pattern. The decryption is simply performed by an inverse-Fourier transform for the addition of the encrypted pattern and the Fourier transform of the random phase. The intensity of the inverse-Fourier transformed image gives the exact result forthe decryption. Further, the binarization of the encrypted pattern is performed for the easiness of the optical and electronic readout of the images, and it also gives rise to the enhancement of the degree of security for the encryption. The binary pattern is optimized by a statistical iteration technique and an excellent decryption image is obtained by the optimization.     

基于图像处理的药片包装视觉检测系统滤波算法

目的为了有效滤除药片包装视觉检测系统中的噪声,提升图像清晰度,保证后期图像分割、边缘处理顺利进行。方法针对药片视觉检测图像中存在大量不确定噪声,提出一种自适应模糊神经网络的图像滤波算法。在模糊神经网络结构中引入一个鲁棒性较强的隶属函数,并通过梯度下降法对模糊神经网络中的参数进行优化训练,利用优化后的网络结构对被噪声污染的图像进行滤波处理。结果仿真结果表明,该算法能够在保留较完整的图像边缘和重要细节的前提下,有效滤除药片中的噪声。结论该滤波算法有效提高了药片图像的清晰度,对于后期药片图像分割以及边缘化处理具有重要意义。     

基于机器视觉的二维图像质量缺陷检测研究进展

目的 机器视觉图像处理技术是近年在图像处理领域发展起来的一门新兴边缘交叉学科，二维图像的质量检测是印刷行业中必不可少的环节，分析基于机器视觉的二维图像质量缺陷检测流程，探索影响基于机器视觉的二维图像质量缺陷检测精度的相关因素，为后续研究印刷品的二维图像自动化检测和质量控制提供参考。方法 在此基础上，围绕图像预处理中的灰度转换、噪声过滤、固定阈值分割、自适应阈值分割、Otsu法及边缘检测，对图像配准中的基于灰度统计信息分布配准方法、基于特征的图像配准方法进行总结，然后归纳分析图像的缺陷提取和分类。结论 以实际例子对上述研究内容进行了提炼，通过图像预处理中的噪声过滤为后续缺陷提取提供清晰图像，减少伪影干扰；通过图像预处理中的灰度变换、阈值分割、感兴趣区域提取减少系统处理时间，为实现高效的缺陷检测奠定了坚实的基础；通过图像配准消除了机械振动引起的图像位置偏移，确保后续缺陷提取的准确性；通过图像缺陷提取和分类帮助印刷企业找出生产问题，提供有针对性的改进措施，可为生产高质量产品提供支持。     

钎焊环检测中的图像拼接方法研究

钎焊环检测系统中,采集的图像序列可能具有不完整环或重叠环。通过调整传送带速度和设置固定相机的曝光时间,确定了2幅图像的重叠区域;将图像去噪、灰度化和二值化,得到了具有清晰轮廓的图像;然后结合LSSVM和CA对图像边缘进行了检测,利用互信息法实现了图像配准;最后采用渐入渐出法进行了图像融合,得到了最终的拼接图像。实验表明,这种方法可以快速准确地实现钎焊环图像的拼接。     

电视制导序列图像快速分割及目标特征提取

针对电视制导系统需从包含多个干扰目标的序列图像中快速识别和跟踪导弹目标的要求,提出了一种基于二值图像索引图的序列图像快速分割及目标特征提取算法,在序列图像二值化后,只需由FPGA对其遍历一次就可得到一张含有目标信息的索引图表,再由DSP对该索引图表边遍历边计算就可得到图像所含目标的数量.面积,质心坐标,二阶矩不变量等特征.实验结果表明,该算法并行处理效率高,实时性好,完全可满足电视制导系统的要求.     

图像纹理分析在彩色印刷品质量检测中的应用研究

图像处理技术为彩色印刷品的缺陷检测提供了一种新的途径.将图像的纹理分析应用于缺陷检测过程,比较标准图像和待检图像的纹理特征参数就可以分析待检图像细微层次方面的质量,从而检测出印刷缺陷.实验表明,这种缺陷检测方法精度和效率高,满足精细彩色印刷品的要求.     

Calibration of High-Resolution X-Ray Tomography With Atomic Force Microscopy

For two-dimensional x-ray imaging of thin films, the technique of scanning transmission x-ray microscopy (STXM) has achieved images with feature sizes as small as 40 nm in recent years. However, calibration of three-dimensional tomographic images that are produced with STXM data at this scale has not yet been described in the scientific literature, and the calibration procedure has novel problems that have not been encountered by x-ray tomography carried out at a larger scale. In x-ray microtomography, for example, one always has the option of using optical imaging on a section of the object to verify the x-ray projection measurements; with STXM, on the other hand, the sample features are too small to be resolved by light at optical wavelengths. This fact implies that one must rely on procedures with higher resolution, such as atomic force microscopy (AFM), for the calibration. Such procedures, however, generally depend on a highly destructive sectioning of the sample, and are difficult to interpret because they give surface information rather than depth information. In this article, a procedure for calibration is described that overcomes these limitations and achieves a calibration of an STXM tomography image with an AFM image and a scanning electron microscopy image of the same object.A Ge star-shaped pattern was imaged at a synchrotron with a scanning transmission x-ray microscope. Nineteen high-resolution projection images of 200 × 200 pixels were tomographically reconstructed into a three-dimensional image. Features in two-dimensional images as small as 40 nm and features as small as 80 nm in the three-dimensional reconstruction were resolved. Transverse length scales based on atomic force microscopy, scanning electron microscopy, x-ray transmission and tomographic reconstruction agreed to within 10 nm. Toward the center of the sample, the pattern thickness calculated from projection images was (51 ± 15) nm vs (80 ± 52) nm for tomographic reconstruction, where the uncertainties are evaluated at the level of two standard deviations.     

Automatic defect inspection of patterned thin film transistor-liquid crystal display (TFT-LCD) panels using one-dimensional Fourier reconstruction and wavelet decomposition

Large-sized flat-panel displays have become increasingly important for use in computer monitors and televisions. This paper has considered the problem of automatic visual inspection of micro-defects including pinholes, scratches and particles in patterned thin film transistor-liquid crystal display (TFT-LCD) panel surfaces. For large-sized TFT-LCD panel inspection, high-resolution line scan is demanded. We propose a global one-dimensional (1-D) Fourier-based image reconstruction scheme that directly works on the 1-D line images instead of the traditional two-dimensional area images. The proposed method fully uses the inherent geometric structure of a TFT-LCD panel. It first eliminates the frequency components that represent the periodic pattern of a TFT-LCD line image in the 1-D Fourier spectrum and then back-transforms the 1-D Fourier-domain image to the 1-D spatial domain image using the inverse Fourier transform. The Fourier reconstruction process can effectively remove the patterned background and distinctly preserve local anomalies in the resulting 1-D image. Wavelet decomposition is further applied to remove uneven illumination in the filtered image so that defects can be easily segmented with simple statistical control limits. Experimental results on a number of micro-defects embedded in TFT-LCD panels show that the proposed method can reliably detect various ill-defined defects without designing and measuring the quantitative features of individual defect types.     

A dynamic threshold-based local mesh ternary pattern technique for biomedical image retrieval

Many content-based image retrieval techniques like local binary pattern (LBP), local ternary pattern (LTP), local mesh peak valley edge pattern (LMePVEP), local mesh ternary pattern (LMeTerP), etc. extract texture features of an image for retrieval purposes. These techniques use fixed threshold to encode the input image and selection of such threshold value is not guided, that is, a chosen threshold may not be optimal for all images in the database. Moreover the performance of these texture-based static threshold algorithms also decreases if the input images are noisy. In this paper, a dynamic threshold value-based local mesh ternary pattern method is proposed in which the threshold value is chosen from the neighborhood of a central pixel using median of all pixels. The proposed modification reduces the overall effect of noise component and thereby improves the average retrieval rate (ARR) and average retrieval precision (ARP) of the original technique. The proposed modified technique has been compared with five other image retrieval approaches to prove its worthiness - the original local mesh ternary pattern technique (LMeTerP), a local ternary pattern technique (LTP), a Best ensemble technique, a multi-label classification CNN model and a CNN-based model of the proposed approach using a VIA ELCAP lung database and an Emphysema database. An improvement of 3.92% in ARR and 2.53% in ARP is observed over the basic local mesh ternary pattern method. Further the proposed modification has been combined with CNN concept and its results have also been analyzed.     

Advances in RGB Projection Technique for Thermographic NDT: Channels Selection Criteria and Visualization Improvement

Infrared thermographic nondestructive testing is a proven technology for material inspection and characterization that is based on the analysis of the dynamic thermal behavior of specimens induced by an intentional and controlled external stimulation. The analysis and review of the image sequences resulting from the inspections is time consuming, which cancels out the advantage provided by the quick inspection process. The RGB projection method has been recently proposed to overcome this limitation. This method synthesizes long sequences into only one true color image, selecting the three most representative images from the original sequence. This study advances the development of this methodology by automating the selection of the channels to be included in the RGB image, making the process objective. Seven different criteria based on different metrics are proposed to select the channels. They are analyzed and evaluated by theoretical and real thermographic tests obtaining RGB images that contain up to 99.6 % of the defects detected in the original thermographic sequences. Visualization improvement is also provided to the RGB image by image processing algorithms.     

结合小波与增量符号相关的鲁棒图像匹配

结合小波变换和增量符号相关提出了一种有效的鲁棒图像匹配方法。将实时图和基准图做小波分解以获得粗尺度的平滑图像;利用相邻像素间灰度的升降对所获得的平滑图像进行二值化重新编码;匹配二值化后的实时图和基准图,找出前者在后者中的坐标,进而确定实时图在基准图中的位置,以完成匹配。小波分解能有效的去除噪声并能大大提高匹配算法的实时性。增量符号相关方法对图像灰度值的变化不敏感。因此,二者相结合不仅有效的解决了存在噪声干扰、阴影混入（块误差）和部分遮挡等影响下的图像匹配问题,且与传统匹配方法相比,匹配速度提高了约10倍以上。理论分析说明了方法的可行性,多组实验显示了其有效性。     

Secret image transmission based on VQ and data embedding

A new secret image transmission scheme suitable for narrow communication channel is proposed in this article. A set of secret images can be simultaneously and efficiently delivered to the receiver via a small and meaningless data stream by the proposed scheme. To reduce the volume of secret images, a codebook is first generated and these secret images are encoded into binary indexes based on the vector quantization (VQ) technique. The compressed message is then embedded into the VQ codebook utilized in the encoding procedure by an adaptive least‐significant‐bits (LSB) modification technique. For the purpose of security, the slightly modified codebook is further encrypted into a meaningless data stream by the AES cryptosystem. Simulation results show that the proposed scheme provides an impressive improvement both in the visual quality of the extracted secret images at the receiver and in the hiding capacity of the cover medium. Experimental data also reveal the feasibility of the proposed secret image transmission scheme for limited‐bandwidth environment. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 1–9, 2007