Trajectory planning is a fundamental step for robotic spraying. This article proposes a semantic segmentation based spraying trajectory planning method for complex products. In order to divide the complex product surface into different painting regions, a geometric feature guided segmentation network (GFGS-Net) is developed. In the GFGS-Net, a geometric local encoding module is constructed to capture edge points based on the proposed local geometric feature, then encode the geometric feature and semantic feature of spraying surface. An attentive feature augmentation module is further designed to adaptively reweight and concatenate the encoded geometric feature and encoded semantic feature. With the aid of such feature representation, GFGS-Net can better describe the industrial product surface and perform accurate product surface segmentation. Afterwards, different spraying patterns are designed for each region, and key spraying parameters spraying overlap distance and spray gun speed are optimized considering the paint thickness and spraying time. Based on these two aspects, the spraying trajectory can be generated through point cloud slicing followed by alpha-shapes and intercept reduction. Alpha-shapes is used to obtain the contour lines of complex product surface, and the contour lines is further smoothed by intercept reduction to make the painting robot work more smoothly. The experiment results demonstrate that the proposed method can significantly improve painting quality and efficiency for complex products.

Nowadays, most high-precision products are still assembled manually, which leads to a low one-time pass rate of products. Workers need to adjust unqualified products repeatedly based on experience, resulting in inefficiency and poor quality consistency. In this work, we propose an explore-exploit reinforcement learning (EERL) framework to suggest the assembly parameters and quantity that workers need to adjust at each step. Jointed with the pretrained product performance prediction model, EERL can output a sequential decision to guide workers hand in hand to adjust unqualified products. EERL includes a two-phase learning process: 1) exploration; and 2) exploitation. In exploration phase, agents are encouraged by the curiosity to fully explore the qualified assembly states in the assembly-adjustment feature space. The regulated difference of random network distillation is used as a measure of curiosity. During exploitation, the agent is trained to learn an assembly-adjustment guidance policy of moving from any unqualified initial assembly state to the corresponding qualified state while satisfying the assembly-adjustment constraints. The curriculum learning mechanism is introduced to learn effectively in the complex environment with sparse reward and adjustment constraints. The proposed approach is validated on an benchmark optimization function and a case study of the gyroscope. The experimental results demonstrate that the proposed approach outperforms other existing approaches.

For metal surface defect inspection, deep-learning-based methods have largely improved the inspection accuracy. However, insufficient data and the diversity of defects usually pose challenges for these methods. To solve these problems, traditional data augmentation methods often augment data by applying image-level geometric variations, usually without introducing new features of unknown defects, which yields limited improvements in defect inspection. Given such circumstances, in this article, a new data augmentation algorithm named Mask2Defect is proposed. Via prior knowledge-based data infusing, this method is able to generate defects with varied features. A large volume of defects with different shapes, severities, scales, rotation angles, spatial locations, and part numbers can be generated in a controllable manner. These generated defects will work as teacher samples to fine-tune the inspection model and automatically adapt it to a wider range of defects. To be specific, we first encode the prior knowledge into the teacher mask via the industrial prior knowledge encoder and render the defect details according to the mask with the mask-to-defect construction network. Then, the fake-to-real domain transformation GAN is used to transform the rendered samples from the fake domain into the real defect domain. Experiments reveal that the synthesized image quality of our method outperforms the state-of-the-art generative methods, and the performance of the inspection model in defect classification and localization has also been improved by fine-tuning with the generated samples.

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.

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, andk-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.

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.

Religious iconography is an integral component of the cultural heritage of Cyprus, which was once a part of the great Byzantine empire. On one hand, icons exhibit strict adherence to conventional symbols, poses and apparel. On the other hand, there is a great variety in the style of depiction that can be attributed to different schools and periods. This paper proposes an active shape model (ASM) based technique for icon face representation that can be used for style comparison and attribution. For centuries-old icons suffering from loss of paint, cracks and added noise from digitization artifacts, we apply an independent component analysis (ICA) technique to enhance the paintings' original work. The experimental results show that our method can effectively characterize Cypriot icons.

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.