In this paper, we present a novel vision-based method of recognizing finger actions for use in electronic appliance interfaces. Human skin is first detected by color and consecutive motion information. Then, fingertips are detected by a novel scale-invariant angle detection based on a variable k-cosine. Fingertip tracking is implemented by detected region-based tracking. By analyzing the contour of the tracked fingertip, fingertip parameters, such as position, thickness, and direction, are calculated. Finger actions, such as moving, clicking, and pointing, are recognized by analyzing these fingertip parameters. Experimental results show that the proposed angle detection can correctly detect fingertips, and that the recognized actions can be used for the interface with electronic appliances.

We propose a novel real-time video stitching method using camera path estimation and homography refinement. The method can stably stitch multiple frames acquired from moving cameras in real time. In the proposed method, one initial between-camera (BC) homography and each camera path (CP) homography are used to estimate the BC homography at every frame. The BC homography is refined by using block matching to adjust the errors of estimated CPs (homography refinement). For fast processing, we extract features using the difference of intensities and use the optical flow to estimate camera motion (CM) homographies, which are multiplied with the previous CMs to calculate CPs (camera path estimations). In experiments, we demonstrated the performance of the CP estimation and homography refinement approach by comparing it with other methods. The experimental results show that the proposed method can stably stitch two image sequences at a rate exceeding 13 fps (frames per second).

In deep learning architectures, rectified linear unit based functions are widely used as activation functions of hidden layers, and the softmax is used for the output layers. Two critical problems of the softmax are introduced, and an improved softmax method to resolve the problems is proposed. The proposed method minimises instability of the softmax while reducing its losses. Moreover, this method is straightforward so its computation complexity is low, but it is substantially reasonable and operates robustly. Therefore, the proposed method can replace the softmax functions.

We propose a novel fusion method of IR (infrared) and visual images to combine distinct information from two sources. To decompose an image into its low and high frequency components, we use Gaussian and Laplacian decomposition. The strong high frequency information in the two sources can be easily fused by selecting the large magnitude of Laplacian images. The distinct low frequency information, however, is not as easily determined. As such, we use histogram distributions of the two sources. Therefore, experimental results show that the fused images can contain the dominant characteristics of both sources.

With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper.

StageMix is a video compilation of multiple stage performances of the same song, edited seamlessly together using appropriate editing points. However, generating a StageMix requires specialized editing techniques and is a considerable time-consuming process. To address this challenge, we introduce AutoStageMix, an automated StageMix generation system designed to perform all processes automatically. The system is structured into five principal stages: preprocessing, feature extraction, identifying a transition point, editing path determination, and StageMix generation. The initial stage of the process involves audio analysis to synchronize the sequences across all input videos, followed by frame extraction. After that, the facial features are extracted from each video frame. Next, transition points are identified, which form the basis for face-based transitions, inter-stage cuts, and intra-stage cuts. Subsequently, a cost function is defined to facilitate the creation of cross-edited sequences. The optimal editing path is computed using Dijkstra’s algorithm to minimize the total cost of editing. Finally, the StageMix is generated by applying appropriate editing effects tailored to each transition type, aiming to maximize visual appeal. Experimental results suggest that our method generally achieves lower NME scores than existing StageMix generation approaches across multiple test songs. In a user study with 21 participants, AutoStageMix achieved viewer satisfaction comparable to that of professionally edited StageMixes, with no statistically significant difference between the two. AutoStageMix enables users to produce StageMixes effortlessly and efficiently by eliminating the need for manual editing.