Abstract
Video object segmentation aims to extract different video objects from a video (i.e., a sequence of consecutive images). It has attracted vast interests and substantial research effort for the past decade because it is a prerequisite for visual content retrieval (e.g., MPEG-7 related schemes), object-based compression and coding (e.g., MPEG-4 codecs), object recognition, object tracking, security video surveillance, traffic monitoring for law enforcement, and many other applications. Video object segmentation is a nonstandardized but indispensable component for an MPEG4/7 scheme in order to successfully develop a complete solution. In fact, in order to utilize MPEG-4 object-based video coding, video object segmentation must first be carried out to extract the required video object masks. Video object segmentation is an even more important issue in military applications such as real-time remote missile/vehicle/soldier’s identification and tracking. Other possible applications include home/office/warehouse security where monitoring and recording of intruders/foreign objects, alarming the personnel concerned or/and transmitting the segmented foreground objects via a bandwidth-hungry channel during the appearance of intruders are of particular interest. Thus, it can be seen that fully automatic video object segmentation tool is a very useful tool that has very wide practical applications in our everyday life where it can contribute to improved efficiency, time, manpower, and cost savings.
TopBackground
For segmentation of objects from video sequences, temporal and spatial information and their appropriate combination have been extensively exploited (Aach & Kaup, 1993; Bors & Pitas, 1998; Castagno, Ebrahimi, & Kunt, 1998; Chen, Chen, & Liao, 2000; Chen & Swain, 1999; Chien, Ma, & Chen, 2002; Cucchiara, Onfiani, Prati, & Scarabottolo, 1999; Kim, Choi, Kim, Lee, Lee, Ahn, & Ho, 1999; Kim, Jeon, Kwak, Lee, & Ahn, 2001; Koller, Weber, Huang, Malik, Ogasawara, Rao, & Russel, 1994; Li et al., 2001; Li, Tye, Ong, Lin, & Ko, 2002; Li, Gu, Leung, & Tian, 2004; Liu, Hong, Herman, & Chellappa, 1998; Liu, Chang, & Chang, 1998; Mech & Wollborn, 1997; Meier & Ngan, 1999; Mester & Aach, 1997; Neri, Colonnese, Russo, & Talone, 1998; Odobez & Bouthemy, 1998; Ong & Spann, 1999; Shao, Lin, & Ko, 1998a, 1998b; Toklu, Tekalp, & Erdem, 2000). Fully automatic extraction of semantically meaningful objects in video is extremely useful in many practical applications but faces problems like limited domain of application, ad hoc approaches, need of excessive parameter/threshold setting and fine-tuning, and overly complicated algorithms. With current level of development of algorithms, in general, only supervised segmentation approaches (e.g., Castagno et al., 1998; Toklu et al., 2000) are capable of detecting semantic objects more accurately from video. Supervised approaches can be found in applications such as studio editing and content retrieval.
Key Terms in this Chapter
CSCL (Computer Supported Collaborative Learning): It is a field of research promoting the development of web technologies supporting teaching and learning models based upon collaborative learning.
Virtual/Online Community: It refers to a group of people that primarily interact via a computer network. The term is attributed to Howard Rheingold (1993). Even if it still does not exist an universal definition for this term, it can be defined as a social network with a common interest or goal that interact in a virtual space across time and geographical boundaries and is capable of to developing personal relationships.
Collaboration/Cooperation: In the studies on collaborative learning the terms collaboration and cooperation are usually distinguished from each other. The former refers to a working together of a group towards a common objective through mutual interventions and sharing, and the latter refers to the joint effort of a group towards the achievement of an objective through strategies based on the partitioning of work. This distinction is not to be taken as dichotomy. In reality cooperation and collaboration can be seen as two activities that place themselves along a continuum. In some cases, especially for collaborative online activities, a certain degree of structuring may be necessary.
Conversational Script: A conversational script can be intended as a complex path of pertinent communicative interactions in a certain stage of collaborative work, which is consciously assumed by a collaborative group as hopeful reference of interaction.
Thinking Type: It is a scaffold label often implemented in the Web-based groupware for Computer Supported Collaborative Learning in order to support online discussions in the Web forum, facilitating reflection and metacognitive processes. Every time the user posts his/her message in the Web forum, he/she has to attribute a label (or thinking type) to his/her message according predefined categories.
Collaborative Learning: Research has widely deepened the concept of collaborative learning. The expression “collaborative learning” can be defined broadly as an instruction method in which learners work together in small groups towards a common goal. It means that students are responsible for their own learning as well as that of the others. Thus, it promotes peer-learning and tutoring.
Virtual Learning Environment (VLE): It is a Web-based software system designed for the management of educational courses. They are often also referred to by the acronym LMS (learning management system).