This paper presents a saliency-based video object extraction (VOE) framework. The proposed framework aims to automatically extract foreground objects of interest without any user interaction or the use of any training data (i.e., not limited to any particular type of object). To separate foreground and background regions within and across video frames, the proposed method utilizes visual and motion saliency information extracted from the input video. A conditional random field is applied to effectively combine the saliency induced features, which allows us to deal with unknown pose and scale variations of the foreground object (and its articulated parts). Based on the ability to preserve both spatial continuity and temporal consistency in the proposed VOE framework, experiments on a variety of videos verify that our method is able to produce quantitatively and qualitatively satisfactory VOE results.

In this paper, we propose a saliency inspired video object extraction (VOE) method to extract and segment foreground objects of interest from videos captured by freely moving cameras. Our method aims at detecting visual and motion salient regions from an input video, and thus we integrate such cosaliency information with the associated foreground and background color models to achieve VOE. A conditional random field (CRF) is applied in our framework to automatically identify the foreground object regions based on the above features, while our method does not need any prior knowledge on the foreground objects of interest or any interaction from the users. Experiments on a variety of videos confirm that our method is able to provide quantitatively and qualitatively more satisfactory results when comparing to state-of-the-art VOE approaches.

The traditional E-learning often offers the online examination to assess the learning effect of a student after completion of the online learning. Basically, this traditional learning assessment mechanism is a passive and negative assessment mechanism, which cannot provide an real-time learning warning mechanism for teachers or students to find out problems as early as possible (including such learning conditions as "absence of mind" resulting from poor learning stage or physical or psychological factor). The proposed research attempts to acquire the electroencephalogram to analyze the characteristic frequency band of the brainwave related to learning and formulate the learning energy index (LEI) for the learner at the time when the learner is reasoning logically via the proposed brain-wave detector based on the cognitive neuroscience. With the established LEI, the physical and psychological conditions of an online leaner can be provided instantly for teachers for assessment. Given that the learning system is integrated into the brainwave analytic sensing component, the system not only can provide learners an instant learning warming mechanism, but also help teachers and learning partners to further understand the causes of learning disorder of learners, and can also provide relevant learning members with timely care and encouragement.