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In the era of inclusive education, students with attention deficits are integrated into the general classroom. To ensure a seamless transition of students' focus towards the teacher's instruction throughout the course and to align with the teaching pace, this paper proposes a continuous recognition algorithm for capturing teachers' dynamic gesture signals. This algorithm aims to offer instructional attention cues for students with attention deficits. According to the body landmarks of the teacher's skeleton by using vision and machine learning-based MediaPipe BlazePose, the proposed method uses simple rules to detect the teacher's hand signals dynamically and provides three kinds of attention cues (Pointing to left, Pointing to right, and Non-pointing) during the class. Experimental results show the average accuracy, sensitivity, specificity, precision, and F1 score achieved 88.31%, 91.03%, 93.99%, 86.32%, and 88.03%, respectively. By analyzing non-verbal behavior, our method of competent performance can replace verbal reminders from the teacher and be helpful for students with attention deficits in inclusive education. [ABSTRACT FROM AUTHOR]

Underwater images often encounter challenges such as attenuation, color distortion, and noise caused by artificial lighting sources. These imperfections not only degrade image quality but also impose constraints on related application tasks. Improving underwater image quality is crucial for underwater activities. However, obtaining clear underwater images has been a challenge, because scattering and blur hinder the rendering of true underwater colors, affecting the accuracy of underwater exploration. Therefore, this paper proposes a new deep network model for single underwater image enhancement. More specifically, our framework includes a light field module (LFM) and sketch module, aiming at the generation of a light field map of the target image for improving the color representation and preserving the details of the original image by providing contour information. The restored underwater image is gradually enhanced, guided by the light field map. The experimental results show the better image restoration effectiveness, both quantitatively and qualitatively, of the proposed method with a lower (or comparable) computing cost, compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Region-of-interest (ROI) determination is very important for video processing and it is desirable to find a simple method to identify the ROI. Along this direction, this paper investigates a user attention model based on visual rhythm analysis for automatic determination of ROI in a video. The visual rhythm, which is an abstraction of a video, is a thumbnail version of a video by a 2-D image that captures the temporal information of a video sequence. Four sampling lines, including diagonal, anti-diagonal, vertical, and horizontal lines, are employed to obtain four visual rhythm maps in order to analyze the location of the ROI from video data. Via the variation on visual rhythms, object and camera motions can be efficiently distinguished. As for hardware design consideration, the proposed scheme can accurately extract ROI with very low computational complexity for real-time applications. The promising results from the experiments demonstrate that the moving object is effectively and efficiently extracted. Finally, we present a way to use flexible macroblock ordering in combination with ROI determination as a preprocessing step for H.264/AVC video coding, and experimental results show the quality of ROI regions is significantly enhanced. Index Terms--Content analysis, feature extraction, region-of-interest (ROI), user attention, visual rhythm.

Compressed video bitstream is sensitive to channel errors that may degrade the reconstructed pictures severely. Error concealment is an effective approach to reduce the impact of errors, and it is implemented at the decoders. Its aim is to mask the effect of the missing blocks to create image or video of subjectively acceptable quality by using the spatial or/and temporal correlation. This paper proposes a temporal error concealment method that exploits the temporal redundancy of a video sequence using a block-matching approach. A recursive block-matching algorithm, based on the two-step block-matching principle, is presented to refine the concealed video. The proposed algorithm can also conceal the multiple erroneous slices. The proposed algorithm outperforms the algorithms presented in previous studies. Index Terms--Block-matching, error slice, recursive, temporal error concealment.

In this paper, new methods for error concealment of shape information are proposed for compressed MPEG-4 bit-streams transmitted over error prone channels. For spatial domain, the proposed approach employs the Bezier curve fitting to approximate and outline the intra-video object plane (I-VOP). For temporal domain, the proposed scheme uses the weighted side matching criterion to reconstruct the inter-video object planes (P-/B-VOPs). The proposed algorithms get better subjective and objective qualities than previous works. Index Terms--Bezier curve, error concealment, MPEG-4, shape coding, side match.