Visual surveillance aims to reliably extract foreground objects. Traditional algorithms usually use a background model image which is generated through the probabilistic modeling of changes over time and space. They detect foreground objects by comparing a background model image with a current image. Hard shadows, illumination changes, camouflage, camera jitter, and ghost object motion make the robust detection of foreground objects difficult in visual surveillance. Recently, various methods based on deep learning have been applied to visual surveillance. It has been shown that deep learning approaches can stably extract salient features, and they give a superior result compared to traditional algorithms. However, they show a good performance only for scenes that are similar to a scene used in training. Without retraining on a new scene, they give a worse result compared to traditional algorithms. In this paper, we propose a stable foreground object detection algorithm through the integration of a background model image used in traditional methods and deep learning methods. A background model image generated by SuBSENSE and multiple images are used as the input of a fully convolutional network. Also, it is shown that it is possible to improve a generalization power by training the proposed network using diverse scenes from an open dataset. We show that the proposed algorithm can have a superior result compared to deep learning-based and traditional algorithms in a new scene without retraining the network. The performance of the proposed algorithm is evaluated using various datasets such as the CDnet 2014, SBI, LASIESTA, and our own datasets. The proposed algorithm shows improvement of 17.5%, 8.9%, and 4.3%, respectively, in FM score compared to three deep learning-based algorithms. INDEX TERMS Foreground objects detection, visual surveillance, generalization power, deep learning, fully convolutional network.

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This paper proposes a new system for verification of the alignment of loading fixtures and test specimens during tensile testing of thin film with a micrometer size through direct imaging. The novel and reliable image recognition system to evaluate the misalignment between the load train and the specimen axes during tensile test of thin film was developed using digital image processing technology with CCD. The decision of whether alignment of the tensile specimen is acceptable or not is based on a probabilistic analysis through the edge feature extraction of digital imaging. In order to verify the performance of the proposed system and investigate the effect of the misalignment of the specimen on tensile properties, the tensile tests were performed as displacement control in air and at room temperature for metal thin film, the beryllium copper (BeCu) alloys. In the case of the metal thin films, bending stresses caused by misalignment are insignificant because the films are easily bent during tensile tests to eliminate the bending stresses. And it was observed that little effects and scatters on tensile properties occur by stress gradient caused by twisting at in-plane misalignment, and the effects and scatters on tensile properties are insignificant at out-of-plane misalignment, in the case of the BeCu thin film.

This paper proposes a new method for measuring strain during a tensile test of the specimen with micrometre size through direct imaging. A specimen was newly designed for adoption of direct imaging which was the main contribution of the proposed system. The structure of the specimen has eight indicators that make it possible to adopt direct imaging and it is fabricated using the same process of microelectromechanical system (MEMS) devices to guarantee the feasibility of the tensile test. We implemented a system for non-contact in situ measurement of strain with the specimen, the image-based displacement measurement system. Extension of the gauge length in the specimen could be found robustly by computing the positions of the eight rectangular-shape indicators on the image. Also, for an easy setup procedure, the region of interest was found automatically through the analysis of the edge projection profile along the horizontal direction. To gain confidence in the reliability of the system, the tensile test for the Al–3%Ti thin film was performed, which is widely used as a material in MEMS devices. Tensile tests were performed and displacements were measured using the proposed method and also the capacitance type displacement sensor for comparison. It is demonstrated that the new strain measurement system can be effectively used in the tensile test of the specimen at microscale with easy setup and better accuracy.