Guifang Duan

Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation

We address the problem of automatic optical flank wear measurement for microdrill bits’ inspection. Cutting plane segmentation of microdrill bits is a fundamental step in the measurement. However, in the case of smeared microdrill bits, cutting planes cannot be segmented correctly by conventional methods. To exactly extract the cutting plane, a level set technique is proposed to segment it from a drill bit image; then we adopt a projection profile-based method for measuring the features of flank wear of microdrills. A new feature of flank wear, called “end wear length”, is also introduced for flank wear evaluation with three other existing features. Experimental results indicate that it is a robust and effective approach for automatic flank wear measurement of microdrills in printed circuit board (PCB) manufacturing.

A Machine Learning-Based Framework for Automatic Visual Inspection of Microdrill Bits in PCB Production

In this paper, an automatic visual inspection scheme with phase identification of microdrill bits in printed circuit board (PCB) production is proposed. Different from conventional methods in which the geometric quantities of microdrill bits are measured to compare with the prior standards, the proposed method adopts a strategy of machine learning. Thus, it lowers the requirement for the enlargement of lens and the resolution of charge-coupled device; therefore, the cost of inspecting instrument can be relatively reduced. Our method mainly includes two procedures: First, the statistical shape models of microdrill bit are built to get the shape subspace, and then the phase identification is performed in the shape subspace using some pattern recognition techniques. In this paper, we compared the performance of two statistical model methods, principal component analysis (PCA) and linear discriminate analysis, together with three classifiers, support vector machines (SVMs), neural networks, and k-nearest neighbors, respectively, for phase identification of microdrill bits. The experimental results demonstrate that using low enlargement and resolution microdrill bit images the proposed method can measure up to high inspection accuracy, and provide a conclusion that the highest identification rates are obtained by PCA-SVMs, which are higher than that of the conventional method.

Automatic Optical Inspection of Micro Drill Bit in Printed Circuit Board Manufacturing Based on Pattern Classification

Automatic optical inspection (AOI) of micro drill bit becomes more and more important with the rapid expanding of printed circuit board (PCB) manufacturing industry. Distinguished from most traditional manual inspection approach, AOI is time-saving, objective and non contact. In this work, a pattern classification method is proposed for the AOI of micro drill bit in PCB manufacturing, in which three features of drill bit blade are extracted for classification. In order to be independent on the clamp that can guarantee the exact position of drill bit blade for photography, and reduce the cost of the AOI system, an image registration method is used to align the drill bit blade, which can also make the feature extraction much easier. The evaluation result indicates that the approach works well for the AOI of micro drill bit. It is real time, more detailed result providing and low requirement on photographic device.

Generalized N-dimensional independent component analysis and its application to multiple feature selection and fusion for image classification

We propose a multilinear independent component analysis (ICA) framework called generalized N-dimensional ICA (GND-ICA) by extending the conventional linear ICA based on the multilinear algebra. Unlike the linear ICA that only treats one-dimensional data, the proposed GND-ICA treats N-dimensional data as a tensor without any preprocess of data vectorization. We furthermore introduce two types of GND-ICA solutions and analyze their efficiency and effectiveness. As an application, the GND-ICA can be used for multiple feature fusion and representation for color image classification. Many features extracted from a given image are constructed as a tensor. The feature tensor can be effective represented by GND-ICA. Compared with the conventional linear subspace learning methods, GND-ICA is capable of obtaining more distinctive representation for color image classification.

Batch-incremental principal component analysis with exact mean update

Incremental principal component analysis (IPCA) has been of great interest in computer vision and machine learning. In this paper, we introduce a new incremental learning procedure for principal component analysis (PCA). The proposed method can keep an accurate track of the mean of the data, and can deal with a set of new observed data in batch each time in subspace updating. Furthermore, a weighting function is proposed for contribution balance of the current data and the new observed data to the new subspace. The performance of our method is illustrated in the experiments on face modeling and face recognition.

Ring artifacts reduction in cone-beam CT images based on independent component analysis

Cone-beam CT (CBCT) scanners are based on volumetric tomography, using a 2D extended digital array providing an area detector [1]. Compared to traditional CT, CBCT has many advantages, such as less X-ray beam limitation, high image accuracy, rapid scan time, etc. However, in CBCT images there are always some ring artifacts that appear as rings centered on the rotation axis. Due to the data of the constructed images are corrupted by these ring artifacts, qualitative and quantitative analysis of CBCT images will be compromised. Post processing and application such as image segmentation and registration also turn more complex as the presence of such artifacts. In this paper, a method based on independent component analysis (ICA) is presented. It deals with the reconstructed CBCT image and can effectively reduce such ring artifacts.

Reconstruction of 3D dynamic expressions from single facial image

Recently automatic facial expression analysis and recognition is rapidly gaining more and more interest in the field of computer vision. The capture and construction of 3D dynamic expressions often take large time and need specialized hardware, which limits its possible applications. In this paper, we try to reconstruct 3D dynamic expression images from single 2D facial image. The proposed method is based on statistical learning, where multiple subspaces are learned and support for 3D dynamic expression generation. The results show that the proposed method can effectively generate 3D dynamic expressions using only one input 2D facial image.

Automatic optical phase identification of microdrill bits using Active Shape Models

Inspection of microdrill bits is very important for quality control in Printed Circuit Board (PCB) production. Traditional methods mainly focus on geometric defects inspection. This paper proposes an automatic optical inspection scheme which can not only be used for geometric defects inspection but also identify the phase of microdrill bits. Our approaches employ active shape models (ASM) technique to model the shape of the cutting plane in the acquired microdrill bit image, and then we classify microdrill bits in the eigenshape space using model parameters. Experimental results show that the proposed method is effective for automatic inspection of microdrill bits in Printed Circuit Board (PCB) manufacturing.

Improved Active Shape Model for automatic optical phase identification of microdrill bits in Printed Circuit Board production

An improved Active Shape Model (ASM), for automatic optical phase identification of microdrill bits in Printed Circuit Board (PCB) production, is presented. To overcome the limitations of conventional ASM on fitting new instants of microdrill bits, six key landmarks are defined for the initialization and optimization of ASM, and a novel method based on projection profiles is also proposed for these key landmarks detection. In addition, local structures of landmarks are redefined according to the feature of microdrill bit images. The fitted shape points are employed for phase identification of microdrill bits with a correlation coefficient as the distance criterion. Experimental results show that our proposed method outperforms the conventional ASM and can improve the accuracy of phase identification of microdrill bits.

Automatic optical phase identification of micro-drill bits based on improved ASM and bag of shape segment in PCB production

This paper addresses the problem of automatic optical phase identification of micro-drill bits for micro-drilling tool inspection in printed circuit board production. To overcome the limitations of conventional active shape model (ASM) on shape modeling of micro-drill bits, six key landmarks are defined for the initialization and optimization of ASM, and a novel method based on projection profiles is also proposed for these key landmarks detection. In addition, to involve the local shape feature, a bag of shape segment (BoSS) model is developed. Based on the improved ASM and BoSS, a new shape representation of micro-drill bits is proposed for phase identification. Experimental results show that the proposed method outperforms the conventional ASM and can improve the phase identification accuracy of micro-drill bits.