Current Issue : April - June Volume : 2021 Issue Number : 2 Articles : 5 Articles
Urban traffic congestion is a severe and widely studied problem over the decade because of the negative impacts. However, in recent years some approaches emerge as proper and suitable solutions. The Carpooling initiative is one of the most representative efforts to propitiate a responsible use of particular vehicles. Thus, the paper introduces a carpooling model considering the users’ preference to reach an appropriate match among drivers and passengers. In particular, the paper conducts a study of 6 of the most avid classified techniques in machine learning to create a model for the selection of travel companions. The experimental results show the models’ precision and assess the best cases using Friedman’s test. Finally, the conclusions emphasize the relevance of the proposed study and suggest that it is necessary to extend the proposal with more drives and passengers’ data....
Facial expression recognition (FER) plays a significant part in artificial intelligence and computer vision. However, most of facial expression recognition methods have not obtained satisfactory results based on low-level features. The existed methods used in facial expression recognition encountered the major issues of linear inseparability, large computational burden, and data redundancy. To obtain satisfactory results, we propose an innovative deep learning (DL)model using the kernel entropy component analysis network (KECANet) and directed acyclic graph support vectormachine (DAGSVM). We use the KECANet in the feature extraction stage. In the stage of output, binary hashing and blockwise histograms are adopted. We sent the final output features to the DAGSVM classifier for expression recognition. We test the performance of our proposed method on three databases of CK+, JAFFE, and CMU Multi-PIE. According to the experiment results, the proposed method can learn high-level features and provide more recognition information in the stage of training, obtaining a higher recognition rate....
Computer vision for control is a growing domain of research and it is widespread in industry and the autonomous vehicle field. A further step is the employment of low-cost cameras to perform these applications. To apply such an approach, the development of proper algorithms to interpret vision data is mandatory. Here, we firstly propose the development of an algorithm to measure the displacement of a mechanical system in contactless mode. Afterwards, we show two procedures that use a 3D camera as a feedback in control strategies. The first one aims to track a moving object. In the second one, the information gained from vision data acquisition allows the mechanical system control to ensure the equilibrium of a ball placed on a moving slide....
In the industrial production of expanded thermoplastic polyurethane (E-TPU) midsoles, the surface defects still rely on manual inspection at present, and the eligibility criteria are uneven. Therefore, this paper proposes an E-TPU midsole surface defect detection method based on machine vision to achieve automatic detection and defect classification. The proposed method is divided into three parts: image preprocessing, block defect detection, and linear defect detection. Image preprocessing uses RGB three channel self-inspection to identify scorch and color pollution. Block defect detection uses superpixel segmentation and background prior mining to determine holes, impurities, and dirt. Linear defect detection uses Gabor filter and Hough transform to detect indentation and convex marks. After image preprocessing, block defect detection and linear defect detection are simultaneously performed by parallel computing. The false positive rate (FPR) of the proposed method in this paper is 8.3%, the false negatives rate (FNR) of the hole is 4.7%, the FNR of indentation is 2.1%, and the running time does not exceed 1.6 s. The test results show that this method can quickly and accurately detect various defects in the E-TPU midsole....
Object tracking has been one of themost active research directions in the field of computer vision. In this paper, an effective singleobject tracking algorithm based on two-step spatiotemporal feature fusion is proposed, which combines deep learning detection with the kernelized correlation filtering (KCF) tracking algorithm. Deep learning detection is adopted to obtain more accurate spatial position and scale information and reduce the cumulative error. In addition, the improved KCF algorithm is adopted to track and calculate the temporal information correlation of gradient features between video frames, so as to reduce the probability of missing detection and ensure the running speed. In the process of tracking, the spatiotemporal information is fused through feature analysis. A large number of experiment results show that our proposed algorithm hasmore tracking performance than the traditional KCF algorithm and can efficiently continuously detect and track objects in different complex scenes, which is suitable for engineering application....
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