Local Gray Information Research Articles

The robustness of complex product development projects under design change risk propagation with gray attack information

Disruptive design change risk propagation is a key obstacle restricting the success of complex product development (CPD) projects. Considering the incomplete information of initial change source selection, this paper develops the design change risk propagation model with gray attack information based on complex network theory. In this model, parameters π and σ are introduced to describe the gray information from both global and local perspectives. Based on the gray information, four kinds of gray attack strategies are provided. The robustness of the CPD project against design change risk propagation under different gray attacks is analyzed through numerical simulation. The results show that the CPD project is most robust under the small out-degree attack, while is most vulnerable under the large out-degree attack. No matter based on global or local gray information, increasing the gray level of information is beneficial to improve the project robustness under large out-degree attack, while reduces the project robustness under small out-degree attack under certain circumstances. Parameters σ and π have thresholds σ* and π*, which can keep the project robustness stable. This paper has implications for project managers to make reasonable decisions for project information management and provides a new perspective for CPD project robustness prediction and improvement.

A hybrid eye movement feature recognition of classroom students based on machine learning

Online classroom teaching is difficult to identify students’ learning status in real time. Therefore, we need to combine intelligent image recognition technology to analyze student status through eye movement features. This study solves the problem of inaccurate positioning of the initial position of the shape model in the process of eyelid matching through machine learning. Moreover, this study improves the algorithm and uses the AK-EYE model based on the combination of ASM algorithm and Kalman filtering to establish a local feature model for each feature point. According to the gray information in the normal direction of the feature point, the local gray information is modeled. After training through the sample set to obtain the state model, the target eye can be searched, and the pose parameters can be determined. Finally, this study designs a control experiment to analyze the performance of the model proposed in this study. The research shows that the algorithm proposed in this paper has a high recognition accuracy and has a practical basis, which can be used as one of the subsequent classroom teaching system algorithms.

Robust Multi-scale Prior L-K Tracking Based on Local Features

Due to the limitation of algorithm principle, inverse compositional L-K algorithm always fails to track fast moving targets. An improved tracking method based on multi-scale motion prior information is proposed in this paper. The multi-scale priori error Jacobi matrix is calculated offline by artificially constructed multi-scale disturbance. The target searching strategy based on multi-scale stratification can greatly increase the convergence range of the algorithm. At the same time, in order to improve the stability of global feature tracking in the case of local occlusion, the pyramid iterative L-K tracking method based on local feature is employed. Experimental results demonstrate outstanding stability of our method for fast moving targets. And by taking advantage of local gray information around feature points, our algorithm can still effectively track the target in the presence of partial occlusion.

Moving target segmentation using Markov random field-based evaluation metric in infrared videos

A method for moving target detection and segmentation using Markov random field (MRF)-based evaluation metric in infrared videos has been proposed. Starting with the most useful seeds of a moving object, which are extracted based on the “holes” effect of temporal difference; the proposed method employs a region growing method using local gray information and a spatial and temporal MRF model-based evaluation metric without ground truth for moving target segmentation in infrared videos. The segmented mask of a moving target is grown from the most useful seeds using the region growing method with thresholds. The proposed evaluation metric is utilized to determine the best growing threshold, where the performance of moving target segmentation is measured by that of segmented mask’s boundary. Thus, an MRF modeling for each boundary point of the segmented mask in spatial and temporal directions was considered by us. This problem is formulated using maximum a posteriori (MAP) estimation principle. At last, the global optimum of MRF-MAP framework is achieved using simulated annealing algorithm. The best segmented mask of a moving target is grown from the most useful seeds with the best growing threshold. Experimental results are reported to demonstrate the accuracy and robustness of our algorithm.

An Adaptive Image Binarization Method for Laser Direct Marking Data Matrix Symbols on Metal Surface

The image binarization of laser direct marking Data Matrix symbols on metal surface is a key procedure during recognition. Contrast and light uniformity of the symbols is greatly affected by the uneven illumination and reflection of the parts. To avoid the limitation of present binarized methods, an adaptive binarization method composed of Otsu method and Nearest neighbor pixels contributing threshold method is proposed. It takes global and local gray information into consideration, as well as the distribution of gray gradient. Experimental results show that the proposed method has a higher recognition rate and reliability.

Facial Feature Detection Algorithm Based on Main Characteristics of Eyes

This paper proposed a new facial feature points localization algorithm based on main characteristics of eyes.Use the result of pupil center position to initialize the model of hybrid improved active shape model (ASM) and active appearance model (AAM). The algorithm will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it optimizes measure functions of ASM and AAM to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

Active Shape and Active Apparent Hybrid Model of Human Facial Feature Points Localization Algorithm

An improved active shape model (ASM) and active appearance model (AAM) based new method is proposed, this method will use two-dimensional local gray information to update the feature point position when using ASM to locate the face contour feature points. As to the internal features point location, it establishes facial organs independent AAM model. At the same time, it uses different measure functions to judge the convergence of search algorithm. The experimental results show that the new algorithm greatly improved the localization accuracy of facial feature points.

An Image Retrieval Method Based on Local Features of Interest Points

To measure the similarity between two images, the local information around all the points were usually used for traditional image retrieval methods based on interest points. The accuracy would be influenced by the dissimilar interest points in the background region or in the uninterested regions. A new image retrieval method based on interest points was proposed in this paper. The most similar interest points were chosen firstly based on feature matching techniques, then the similarity was measured based on the local gray information around the preserved similar interest points. Experiments show that the proposed method is more accurate than traditional image retrieval method based on interest points.