This paper proposes a dynamic conditional random field (DCRF) model for foreground object and moving shadow segmentation in indoor video scenes. Given an image sequence, temporal dependencies of consecutive segmentation fields and spatial dependencies within each segmentation field are unified by a dynamic probabilistic framework based on the conditional random field (CRF). An efficient approximate filtering algorithm is derived for the DCRF model to recursively estimate the segmentation field from the history of observed images. The foreground and shadow segmentation method integrates both intensity and gradient features. Moreover, models of background, shadow, and gradient information are updated adaptively for nonstationary background processes. Experimental results show that the proposed approach can accurately detect moving objects and their cast shadows even in monocular grayscale video sequences.

This paper proposes a probabilistic framework for spatiotemporal segmentation of video sequences. Motion information, boundary information from intensity segmentation, and spatial connectivity of segmentation are unified in the video segmentation process by means of graphical models. A Bayesian network is presented to model interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notion of the Markov random field is used to encourage the formation of continuous regions. Given consecutive frames, the conditional joint probability density of the three fields is maximized in an iterative way. To effectively utilize boundary information from the intensity segmentation, distance transformation is employed in local objective functions. Experimental results show that the method is robust and generates spatiotemporally coherent segmentation results. Moreover, the proposed video segmentation approach can be viewed as the compromise of previous motion based approaches and region merging approaches.

This paper presents a knowledge-driven algorithm to identify and segment the central sulcus (CS) from human brain MR images. The dataset is reformatted along the anterior and posterior commissures (AC-PC) plane first. Then, the 3D region within the two coronal planes passing through the AC and PC is defined as the region of interest (ROl) to search for all the sulci within it. The CS is the sulcus with the largest volume within the ROI. Together with the sulci, grey matter (GM) is included for the region growing in order to deal with the partial volume effect. The GM is removed through skeletonization. Experimental results are given.

S-AdaBoost is a new variant of AdaBoost and is more effective than the conventional AdaBoost in handling outliers in pattern detection and classification in real world complex environment. Utilizing the divide and conquer principle, S-AdaBoost divides the input space into a few sub-spaces and uses dedicated classifiers to classify patterns in the sub-spaces. The final classification result is the combination of the outputs of the dedicated classifiers. S-AdaBoost system is made up of an AdaBoost divider, an AdaBoost classifier, a dedicated classifier for outliers, and a non-linear combiner. In addition to presenting face detection test results in a complex airport environment, we have also conducted experiments on a number of benchmark databases to test the algorithm. The experiment results clearly show S-AdaBoost's effectiveness in pattern detection and classification.

This paper proposes a unified framework for spatiotemporal segmentation of video sequences. A Bayesian network is presented to model the interactions among the motion vector field, the intensity segmentation field, and the video segmentation field. The notions of distance transformation and Markov random field are used to express spatiotemporal constraints. Given consecutive frames, an optimization method is proposed to maximize the conditional probability density of the three fields in an iterative way. Experimental results show that the approach is robust and generates spatiotemporally coherent segmentation results.

We propose a switching hypothesized measurements (SHM) model supporting multimodal probability distributions and present the application of the model in handling potential variability in visual environments when tracking multiple objects jointly. For a set of occlusion hypotheses, a frame is measured once under each hypothesis, resulting in a set of measurements at each time instant. A computationally efficient SHM filter is derived for online joint region tracking. Both occlusion relationships and states of the objects are recursively estimated from the history of hypothesized measurements. The reference image is updated adaptively to deal with appearance changes of the objects. The SHM model is generally applicable to various dynamic processes with multiple alternative measurement methods.

This paper presents a probabilistic method for foreground segmentation that distinguishes moving objects from their cast shadows in monocular indoor image sequences. The models of background, shadow, and edge information are set up and adaptively updated. A Bayesian framework is proposed to describe the relationships among the segmentation label, background, intensity, and edge information. A Markov random field is used to boost the spatial connectivity of the segmented regions. The solution is obtained by maximizing the posterior probability density of the segmentation field.

We use well-established results in biological vision to construct a novel vision model for handwritten digit recognition. We show empirically that the features extracted by our model are linearly separable over a large training set (MNIST). Using only a linear classifier on these features, our model is relatively simple yet outperforms other models on the same data set.

The paper describes a new spline formulation that supports deformation of polygonal shapes into smooth spline surface models. Once a polygonal shape with underlying rectangular topology is specified by the user, it is deformed into a smooth surface that interpolates all the polygonal vertices. The user can then modify the default smooth surface by increasing or decreasing the amount of deformation, either globally or locally. This is accomplished by interactively controlling the shape parameters associated with the polygonal vertices. This modeling paradigm is conceptually simple, and allows C/sup 2/ continuous surfaces to be easily designed, even by a novice user.

We present a learning-based semantics approach for classifying and retrieving images.Our approach defines semantics at two levels: (1) primitive semantics at the patch level, extracted automatically from pixel characteristics of patches with supervised learning; and (2) scene semantics at the image level, recognized from the association of primitive semantics of the patches in a self-organizing manner.Images are classified and retrieved according to the similarity in scene semantics.Our experiments so far have yielded highly accurate scene classification results and very promising retrieval performance on a set of diverse natural scene images.

No abstract available.

The /spl beta/-spline provides bins and tension control facilities for creating geometrically continuous curves and surfaces. Although geometric continuity is a more appropriate geometric measurement of smoothness than parametric continuity, parametric continuity is still necessary in some applications. The paper proposes a new C/sup 2/ continuous spline scheme called the /spl alpha/-spline which provides weights and tension control. The new scheme is based on blending a sequence of singular reparametrized line segments with a piecewise NURBS curve. The idea is extended to produce /spl alpha/-spline surfaces.