@conference {
	title = {Scalable Coding of Video Objects},
	author = {Haridasan, Radhakrishnan., Baras, John S.},
	year = {1998},
	month = {05/1998},
	abstract = {This paper provides a methodology to encode video objects in a scalable manner with regard to both content and quality. Content scalability and quality scalability have been identified as required features in order to support video coding across different environments. Following the object-based approach to coding video, we extend our previous work on motion-based segmentation by using a time recursive approach to segmenting image sequences and decomposing a video "shot" into its constituent objects. Our formulation of the segmentation problem enables us to design a codec in which the information (shape, texture and motion) pertaining to each video object is encoded independently of the other. The multiresolution wavelet decomposition used in encoding texture information is shown to be helpful in providing spatial scalability. Our codec design is also shown to be temporally scalable.},
	address = {Monterey, CA},
	booktitle = {IEEE International Symposium on Circuits and Systems 1998 (ISCAS\&rsquo;98)},
	pages = {pp. 289-292}
}
@conference {
	title = {Accurate Segmentation and Estimation of Parametric Motion Fields for Object-Based Video Coding Using Mean Field Theory},
	author = {Haridasan, Radhakrishnan., Baras, John S.},
	year = {1998},
	month = {01/1998},
	abstract = {We formulate the problem of decomposing a scene into its constituent objects as one of partitioning the current frame into objects comprising it. The motion parameter is modeled as a nonrandom but unknown quantity and the problem is posed as one of Maximum Likelihood (ML) estimation. The MRF potentials which characterize the underlying segmentation field are defined in a way that the spatio-temporal segmentation is constrained by the static image segmentation of the current frame. To compute the motion parameter vector and the segmentation simultaneously we use the Expectation Maximization (EM) algorithm. The E-step of the EM algorithm, which computes the conditional expectation of the segmentation field, now reflects interdependencies more accurately because of neighborhood interactions. We take recourse to Mean Field theory to compute the expected value of the conditional MRF. Robust M-estimation methods are used in the M- step. To allow for motions of large magnitudes image frames at various scales and the EM procedure is embedded in a hierarchical coarse-to-fine framework. Our formulation results in a highly parallel algorithm that computes robust and accurate segmentations as well as motion vectors for use in low bit rate video coding.},
	address = {San Jose, CA},
	booktitle = {SPIE 10th Annual Symposium on Visual Communications and Image Processing 1998 (VICP \&rsquo;98)},
	pages = {pp. 361-369}
}
