The Visual Voice Activity Detection (V-VAD) problem in unconstrained environments is investigated in this paper. A novel method for V-VAD in the wild, exploiting local shape and motion information appearing at spatiotemporal locations of interest for facial video description and the Bag of Words (BoW) model for facial video representation, is proposed. Facial video classification is subsequently performed using state-of-theart classification algorithms. Experimental results on one publicly available V-VAD data set denote the effectiveness of the proposed method, since it achieves better generalization performance in unseen users, when compared with recently proposed state-ofthe-art methods. Additional results on a new, unconstrained, data set provide evidence that the proposed method can be effective even in such cases in which any other existing method fails.

A novel method for Visual Voice Activity Detection (V-VAD) that exploits local shape and motion information appearing at spatiotemporal locations of interest for facial region video description and the Bag of Words (BoW) model for facial region video representation is proposed in this paper. Facial region video classification is subsequently performed based on Single-hidden Layer Feedforward Neural (SLFN) network trained by applying the recently proposed kernel Extreme Learning Machine (kELM) algorithm on training facial videos depicting talking and non-talking persons. Experimental results on two publicly available V-VAD data sets, denote the effectiveness of the proposed method, since better generalization performance in unseen users is achieved, compared to recently proposed state-of-the-art methods.

Abstract. Stereoscopic medical videos are recorded, e.g., in stereo endoscopy or during video recording medical/dental operations. This paper examines quality issues in the recorded stereoscopic medical videos, as insufficient quality may induce visual fatigue to doctors. No attention has been paid to stereo quality and ensuing fatigue issues in the scientific literature so far. Two of the most commonly encountered quality issues in stereoscopic data, namely stereoscopic window violations and bent windows, were searched for in stereo endoscopic medical videos. Furthermore, an additional stereo quality issue encountered in dental operation videos, namely excessive disparity, was detected and fixed. The conducted experiments prove the existence of such quality issues in stereoscopic medical data and highlight the need for their detection and correction.