Content-aware image retargeting has been investigated since the last decade as a paradigm of image modification for proper display on the different screen sizes. Modifications, such as seam carving or seam insertion, have been introduced to achieve aforesaid image retargeting. The changes in an image are not easily recognizable by human eyes. Inspired by the Blocking Artifact Characteristics Matrix (BACM), a method to detect tampers caused by seam modification on JPEG retargeted images without knowledge of the original image is proposed in this paper. In a BACM block matrix, we found that the original JPEG image demonstrates a regular symmetrical data, whereas the symmetrical data in a block reconstructed by seam modification is destroyed. Twenty-two features are proposed to train the data by using a Support Vector Machine (SVM) classification method. The experimental results clearly demonstrate that the proposed method provides a very high recognition rate for those JPEG retargeted images. The source codes and the complete experimental data can be accessed at http://video.minelab.tw/DETS/.

With the advancements of the human computer interaction field, nowadays it is possible for the users to use their body motions, such as swiping, pushing and moving, to interact with the content of computers or smart phones without traditional input devices like mouse and keyboard. With the introduction of gesture-based interface Kinect from Microsoft it creates promising opportunities for educators to offer students easier and intuitive ways to interact with the learning systems. The integration of Kinect based applications into classroom make students' learning experience very active and joyful. In this context, this paper proposes a system for assessment in a smart classroom environment. An interactive framework is designed using Microsoft Kinect sensor for virtual learning environment with new gesture-based questions supporting QTI-based assessment, and further a rich set of gesture commands are also introduced for practical usage in the classroom. Proposed system was experimented with teachers and students then collected feedback of the users using a usability questionnaire. The results show that the participants are satisfied with the system and it demonstrates that the proposed system is simple to use, provides better functionality and motivates student learning by assessment.

Deep learning based on convolutional neural network (CNN) has been successfully applied to stereo matching, which has achieved greater improvement in speed and accuracy compared with traditional methods. However, existing CNN-based stereo matching frameworks frequently encounter two problems. First, the existing stereo matching network has a large number of parameters, which results in too long matching running time since excessive network width and excessive number of convolution kernels. Second, in some areas where reflection, refraction and fine structure may lead to ill-posed problems, the disparity estimation errors can be occurred. In this paper, we proposed a lightweight network, convolution attention residual network (CAR-Net), which can balance the real-time matching and matching accuracy for stereo matching. Besides, a multi-scale residual network called CBAM-ResNeXt, which combines attention, was proposed for features extraction. With an aim is to simplify the parameters of the network model by reducing the size of filters and to extract the semantic features such as categories and locations from the image through convolutional block attention module (CBAM). Here, the CBAM consists of channel attention module and spatial attention module, where the semantic information of the feature map can be fully maintained after the parameters were simplified. Moreover, we proposed a dimension-extended 3D-CBAM, which is connected to 3DCNN for cost aggregation. By combining these two sub-modules of attention, the network is guided to selectively focus on the foreground or background regions, so as to improve the disparity accuracy in the ill-posed regions. The experimental results showed that our proposed method generated high accuracy and optimized the velocity compared to the state-of-the-art benchmark on KITTI 2012, KITTI 2015 and Scene Flow. INDEX TERMS Stereo matching, residual network, attention module, running time.

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