An application of texture analysis to materials inspection

A large set of texture features were measured for a collection of samples of an industrial material which had been graded in quality by inspectors. Features were identified, based on gray-level co-occurrence statistics, that had a high correlation with the grades. Such features could be used for on-line inspection of the material.     

Real-time structured texture synthesis and editing using image-mesh analogies

We present a novel texture synthesis technique designed to reproduce at real-time frame-rates example texture images, with a special focus on patterns characterized by structural arrangements. Unlike current pixel-, patch- or texton-based schemes, that operate in image space, our approach is structural. We propose to assimilate texture images to corresponding 2D geometric meshes (called texture meshes). Our analysis mainly consists in generating automatically these meshes, while synthesis is then based on the creation of new vertex/polygon distributions matching some arrangement map. The output texture image is obtained by rasterizing the previously generated polygons using graphics hardware capabilities, which guarantees high speed performance. By operating in geometry space instead of image/pixel space, the proposed structural approach has a major advantage over current techniques: beyond pure texture reproduction, it allows us to define various tools, which allow users to further modify locally or globally and in real-time structural components of textures. By controlling the arrangement map, users can substitute new meshes in order to completely modify the structural appearance of input textures, yet maintaining a certain visual resemblance with the initial example image.     

Real-time image segmentation for visual inspection of pharmaceutical tablets

Visual appearance is an important quality factor of pharmaceutical tablets. Due to the vast quantities of produced tablets and high-quality requirements, pharmaceutical companies are interested in employing automated systems for real-time visual tablet inspection with the speeds of up to 100 tablets per second. Such systems require reliable tablet manipulation, illumination, image acquisition, tablet image analysis, classification, and sorting system. Tablet image segmentation, in which each tablet image is partitioned into the tablet region and background, is the first and very important step in tablet image analysis. It this paper, we propose a novel real-time segmentation method for grey-level images that is based on border tracking. The proposed method was designed to be accurate, robust, and computationally undemanding. The performances of the method were objectively assessed on a large number of simulated and real tablet images. The obtained results indicated high reliability, accuracy, and speed. The 100% reliability was obtained for segmentation of real images of pharmaceutical tablets, while the segmentation times were no more than 1.5 ms or 15% of the whole time available for tablet image analysis. As such, the proposed method proved feasible for real-time visual quality inspection of pharmaceutical tablets. Based on just a few assumptions that are usually fulfilled, the method may be a valuable segmentation tool for many other visual quality inspection applications.     

统计纹理的自动表面检测*

提出了一种用于砂纸、铸件和许多工业材料中的随机纹理表面缺陷的自动检测的全局方法.该方法不依赖于纹理的局部特征,它应用傅里叶变换进行全局图像恢复.应用逆傅里叶变换去除任何统计纹理中的周期性、重复性结构.在恢复图像中,原图像中的同质区域灰度近似一致,而缺陷区域被明显地保留下来了.对不同实际统计纹理的实验结果表明了该方法的有效性.     

Adaptive surface inspection via interactive evolution

An increasingly frequent application of Machine Vision technologies is in automated surface inspection for the detection of defects in manufactured products. Such systems offer significant benefits in terms of cost, detection rates, and user-satisfaction over conventional human inspection systems. However, they usually require significant investment of expert time to set up, are “brittle” in the sense of being highly specialised to the task for which they are tuned, and are consequently sensitive to changes in operating conditions or product specifications. This raises problems within an industrial setting, where operating conditions or requirements may change, and the end-users are experts in their manufacturing field, but not in image processing.In this paper, we describe the development of a rapidly reconfigurable system in which the users’ tacit knowledge and requirements are elicited via a process of Interactive Evolution, finding the image processing parameters to achieve the required goals without any need for specialised knowledge of the machine vision system. We show that the resulting segmentation can be quickly and easily evolved from scratch, and achieves detection rates comparable to those of a hand-tuned system on a hot-rolled steel defect recognition problem.     

晶圆表面微米级缺陷检测

为降低晶圆缺陷对半导体制造的影响,在基于改进的多重中值滤波算法的基础上,以差影法为基本原理,采用归一化互相关的模版匹配方法实现晶圆表面缺陷检测。改进的多重中值滤波算法有效实现噪声点与非噪声点的分辨,归一化模版匹配算法对光照具有很好的鲁棒性。对大量的晶粒进行实验,实验结果表明,该方法可有效检测出晶圆表面的缺陷,精度达到15μm左右,所提检测算法在实际的应用中可代替人工,快速、准确地实现晶圆的缺陷检测。     

基于软件寻位的自由曲面零件超声检测方法

针对复杂曲面工件内部缺陷的超声检测,本文提出了一种基于软件寻位的超声检测轨迹生成算法,只需获取检测工件的少量信息,就能自动求解工件状态,从而实时生成检测轨迹.使用该方法无需使用精密夹具和人工找正,因而可减少产品成本,提高检测效率.     

Automatic defect detection of texture surface with an efficient texture removal network

Automated defect inspection of texture surface is still a challenging task in the industrial automation field due to the tremendous changes in the appearance of various surface textures. We present a simple but powerful image transformation network to remove textures and highlight defects at full resolution. The simple full convolution network consists only of 3 × 3 regular convolution and several dilated convolution blocks, which makes it compact and able to capture multi-scale features effectively. To further improve the ability of the network to suppress texture and highlight defects, a polynomial loss function combining perceptual loss, structural similarity loss and image gradient loss is proposed. In addition, a semi-automatic annotation method mainly composed of wavelet transform and relative total variation is designed to generate a data set of image pairs containing the original texture image and the desired texture removal image. We conducted experiments on a milled metal surface defect dataset and an open data set containing various textured backgrounds to evaluate the performance of our method. Compared with other convolutional neural network approaches, the results demonstrate the superiority of the proposed method. The method has been applied to the surface defect online detection system of an aluminum ingot milling production line, which effectively improves the surface inspection efficiency and product quality.     

Slider vibration reduction using slider surface texture

Surface texture with a height of 4.5 nm was fabricated on two types of pico-sliders using argon plasma etching. The nominal flying height of the sliders was 5 and 7 nm, respectively. Laser Doppler vibrometry (LDV) was used to investigate the dynamics of the textured and untextured sliders during steady-state flying and during contact start/stop (CSS). During steady-state flying, the texture was found to significantly reduce both rigid body slider vibration modes and air-bearing modes. During CSS, the amplitude of both out-of-plane and in-plane vibrations was found to be reduced as a consequence of the texture on the slider surfaces.     

On-the-fly texture computation for real-time surface shading

Standard texture mapping hardware enables rapid rendering of color mapped surfaces with interpolated surface shading. New algorithms extend this to bump mapping, Phong shading, and reflection mapping. We first introduce the bidirectional reflectance function we wish to optimize, split into diffuse, specular and environment terms. Casting the diffuse term as a table lookup, we introduce lighting tables and efficient ways to compute them for distant lights. We also revisit the geometry of bump mapping, extending Blinn's (1978) results. We consider caching intermediate results for rendering animated rigid bodies, generalizing this to animated surfaces using a technique called parametric rasterization. Finally, we describe efficient reflection mapping and discuss implications for bump-mapped surfaces. We present a fast method for rendering Phong highlights and discuss a special case of a planar surface with simulated water ripples     

Detection of surface orientation and motion from texture by a stereological technique

A new approach is given to detect the surface orientation and motion from the texture on the surface by making use of a mathematical principle called ‘stereology’. Information about the surface orientation is contained in ‘features’ computed by scanning the image by parallel lines and counting the number of intersections with the curves of the texture. A synthetic example is given to illustrate the technique. This scheme can also detect surface motions relative to the viewer by computing features of its texture at one time and a short time later. The motion is specified by explicit formulae of the computed features.     

Real-time moustache detection by combining image decolorization and texture detection with applications to facial gender recognition

There are still many challenging problems in facial gender recognition which is mainly due to the complex variances of face appearance. Although there has been tremendous research effort to develop robust gender recognition over the past decade, none has explicitly exploited the domain knowledge of the difference in appearance between male and female. Moustache contributes substantially to the facial appearance difference between male and female and could be a good feature to be incorporated into facial gender recognition. Little work on moustache segmentation has been reported in the literature. In this paper, a novel real-time moustache detection method is proposed which combines face feature extraction, image decolorization and texture detection. Image decolorization, which converts a color image to grayscale, aims to enhance the color contrast while preserving the grayscale. On the other hand, moustache appearance is normally grayscale surrounded by the skin color face tissue. Hence, it is a fast and efficient way to segment the moustache by using the decolorization technology. In order to make the algorithm robust to the variances of illumination and head pose, an adaptive decolorization segmentation has been proposed in which both the segmentation threshold selection and the moustache region following are guided by some special regions defined by their geometric relationship with the salient facial features. Furthermore, a texture-based moustache classifier is developed to compensate the decolorization-based segmentation which could detect the darker skin or shadow around the mouth caused by the small lines or skin thicker from where he/she smiles as moustache. The face is verified as the face containing a moustache only when it satisfies: (1) a larger moustache region can be found by applying the decolorization segmentation; (2) the segmented moustache region is detected as moustache by the texture moustache detector. The experimental results on color FERET database showed that the proposed approach can achieve 89 % moustache face detection rate with 0.1 % false acceptance rate. By incorporating the moustache detector into a facial gender recognition system, the gender recognition accuracy on a large database has been improved from 91 to 93.5 %.     

Autoencoder-based anomaly detection for surface defect inspection

In this paper, the unsupervised autoencoder learning for automated defect detection in manufacturing is evaluated, where only the defect-free samples are required for the model training. The loss function of a Convolutional Autoencoder (CAE) model only aims at minimizing the reconstruction errors, and makes the representative features widely spread. The proposed CAE in this study incorporates a regularization that improves the feature distribution of defect-free samples within a tight range. It makes the representative feature vectors of all training samples as close as possible to the mean feature vector so that a defect sample in the evaluation stage can generate a distinct distance from the trained center of defect-free samples. The proposed CAE model with regularizations has been tested on a variety of material surfaces, including textural and patterned surfaces in images. The experimental results reveal that the proposed CAE with regularizations significantly outperforms the conventional CAE for defect detection applications in the industry.     

In-line inspection of surface feature and defect

Microsystem Technologies - An efficient 3D profile measurement technique is proposed for in-line full-field inspection of surface feature and defect with high resolution. In this technique, a...     

Machine vision system for curved surface inspection

This application-oriented paper discusses a non-contact 3D range data measurement system to improve the performance of the existing 2D herring roe grading system. The existing system uses a single CCD camera with unstructured halogen lighting to acquire and analyze the shape of the 2D shape of the herring roe for size and deformity grading. Our system will act as an additional system module, which can be integrated into the existing 2D grading system, providing the additional third dimension to detect deformities in the herring roe, which were not detected in the 2D analysis. Furthermore, the additional surface depth data will increase the accuracy of the weight information used in the existing grading system. In the proposed system, multiple laser light stripes are projected into the herring roe and the single B/W CCD camera records the image of the scene. The distortion in the projected line pattern is due to the surface curvature and orientation. Utilizing the linear relation between the projected line distortion and surface depth, the range data was recovered from a single camera image. The measurement technique is described and the depth information is obtained through four steps: (1) image capture, (2) stripe extraction, (3) stripe coding, (4) triangulation, and system calibration. Then, this depth information can be converted into the curvature and orientation of the shape for deformity inspection, and also used for the weight estimation. Preliminary results are included to show the feasibility and performance of our measurement technique. The accuracy and reliability of the computerized herring roe grading system can be greatly improved by integrating this system into existing system in the future.     

Reconstruction of surface texture based on spatial information measured with a pen-type texture sensor

Surface texture is one of the important properties for the human to identify objects by touch. Effective reconstructions of textures are necessary for realistic interactions between the human and environment via human–computer interfaces. This paper presents a systematic approach for sensing and reconstructing periodic surface textures. Three significant issues are discussed: a pen-type texture sensor that measures the spatial information based on the measurements of contact forces; an algorithm for the reconstruction of periodic textures based on the obtained spatial information; and the method of incremental scanning to identify the polar spectrum of a surface by limited number of scans. The concept of polar spectrum is introduced to describe the spatial properties of the surface, that is, the relation between spatial frequencies and the direction of measurement. The pattern of polar spectrum is used to facilitate surface reconstructions. Experimental results based on the spatial information obtained with a laser displacement sensor and the pen-type texture sensor demonstrate the effectiveness of the proposed methods for the measurement and reconstruction of periodic textures.     

An approach to in-process surface texture condition monitoring

Surface textures formed in the machining process have a great influence on parts’ mechanical behaviours. Normally, the surface textures are inspected by using the images of the machined and cleaned parts. In this paper, an in-process surface texture condition monitoring approach is proposed. Based on the grey-level co-occurrence matrices, some surface texture image features are extracted to describe the texture characteristics. On the basis of the empirical model decomposition, some sensitive features are also extracted from the vibration signal. The mapping relationship from texture characteristics to texture image features and vibration signal features is found. A back propagation neural network model is built when the signal features and the texture conditions are respectively inputs and outputs. The particle swarm optimization is used to optimise the weights and thresholds of the neural network. Experimental study verifies the approach's effectiveness in monitoring the surface texture conditions during the machining process. The approach's accuracy and robustness are also verified. Then, the surface texture condition can be monitored efficiently during the machining process.     

Capture and fusion of 3d surface texture

Image fusion is a process that multiple images of a scene are combined to form a single image. The aim of image fusion is to preserve the full content and retain important features of each original image. In this paper, we propose a novel approach based on wavelet transform to capture and fusion of real-world rough surface textures, which are commonly used in multimedia applications and referred to as3D surface texture. These textures are different from 2D textures as their appearances can vary dramatically with different illumination conditions due to complex surface geometry and reflectance properties. In our approach, we first extract gradient/height and albedo maps from sample 3D surface texture images as their representation. Then we measure saliency of wavelet coefficients of these 3D surface texture representations. The saliency values reflect the meaningful content of the wavelet coefficients and are consistent with human visual perception. Finally we fuse the gradient/height and albedo maps based on the measured saliency values. This novel scheme aims to preserve the original texture patterns together with geometry and reflectance characteristics from input images. Experimental results show that the proposed approach can not only capture and fuse 3D surface texture under arbitrary illumination directions, but also has the ability to retain the surface geometry properties and preserve perceptual features in the original images.