Due to the significant complexity of membrane morphology and the generally poor image quality in electron tomographic volumes, current automatic methods for segmentation of membranes perform poorly. Users must resort to manual tracing of recognized patterns on 2-D slices of the volume, a method that suffers from subjectivity and is very labor intensive, preventing quantitative analyses of tomographic data that require comparative analyses of many volumes. To overcome these limitations, we develop an automatic 3-D segmentation method that fully exploits the prior knowledge about the shape of the membranes as well as the 3-D information provided by the tomograms, and systematically combines this knowledge with the image data to improve segmentation results. The method is based on the watersnake framework. By mathematically reformulating the traditional watershed segmentation as an energy minimization problem, the watersnake inherits the many strengths of the watershed method while overcoming the limitations of the traditional energy-based segmentation methods. In our previous work (H. Nguyen et al., 2003), the original watersnake model was successfully modified by incorporating smoothness into watershed segmentation. In this work, we further extend that model to incorporate into the energy function various constraints representing our prior knowledge about the global shape of the cellular features to be segmented. Segmentation can, therefore, be accomplished via minimization of the energy function subject to the shape prior constraints. Finally, the mathematical framework is further extended from 2-D to 3-D so that segmentation can be carried out in 3-D to take advantage of the additional information provided by the tomograms. We apply this method for the automatic extraction of biological membranes of varying complexities including those of bacterial walls and mitochondrial boundaries.

In multitarget tracking, the main challenge is to maintain the correct identity of targets even under occlusions or when differences between the targets are small. The paper proposes a new approach to this problem by incorporating the context information. The context of a target in an image sequence has two components: the spatial context including the local background and nearby targets and the temporal context including all appearances of the targets that have been seen previously. The paper considers both aspects. We propose a new model for multitarget tracking based on the classification of each target against its spatial context. The tracker searches a region similar to the target while avoiding nearby targets. The temporal context is included by integrating the entire history of target appearance based on probabilistic principal component analysis (PPCA). We have developed a new incremental scheme that can learn the full set of PPCA parameters accurately online. The experiments show robust tracking performance under the condition of severe clutter, occlusions, and pose changes.

The watershed algorithm has many nice properties in terms of robustness to image noise, topology, and effective handling non-rigid deformations. It also has drawbacks including oversegmentation and lack of regularization. We present new methods to overcome these drawbacks. We propose a novel region merging algorithm based on the water diffusion principle. Starting with a large number of markers, lakes formed around the markers are merged in the order they meet during the immersion. The merging is not performed immediately but delayed until the amount of water diffusion between two lakes is significant enough to overwhelm the small lake. The delay makes the merging result robust to "leaks" in object boundaries, when weak edges could trigger the merging of object into the background as in traditional methods. Regularization is achieved by imposing priors of local shape configurations. Local shape features are extracted from Gaussian derivatives of the object indicator function. The ensemble of shape features at multiple scales increases representation power. These features are used to incorporate smoothness and domain knowledge into the evolution of region boundaries in the watershed algorithm. The method has been successfully applied to segmentation of worms.

In multi-target tracking, the maintaining of the correct identity of targets is challenging. In the presented tracking method, accurate target identification is achieved by incorporating the appearance information of the spatial and temporal context of each target. The spatial context of a target involves local background and nearby targets. The first contribution of the paper is to provide a new discriminative model for multi-target tracking with the embedded classification of each target against its context. As a result, the tracker not only searches for the image region similar to the target but also avoids latching on nearby targets or on a background region. The temporal context of a target includes its appearances seen during tracking in the past. The past appearances are used to train a probabilistic PCA that is used as the measurement model of the target at the present. As the second contribution, we develop a new incremental scheme for probabilistic PCA. It can update accurately the full set of parameters including a noise parameter still ignored in related literature. The experiments show robust tracking performance under the condition of severe clutter, occlusions and pose changes.

The paper is concerned with two-class active learning. While the common approach for collecting data in active learning is to select samples close to the classification boundary, better performance can be achieved by taking into account the prior data distribution. The main contribution of the paper is a formal framework that incorporates clustering into active learning. The algorithm first constructs a classifier on the set of the cluster representatives, and then propagates the classification decision to the other samples via a local noise model. The proposed model allows to select the most representative samples as well as to avoid repeatedly labeling samples in the same cluster. During the active learning process, the clustering is adjusted using the coarse-to-fine strategy in order to balance between the advantage of large clusters and the accuracy of the data representation. The results of experiments in image databases show a better performance of our algorithm compared to the current methods.

We propose a new method for object tracking in image sequences using template matching. To update the template, appearance features are smoothed temporally by robust Kalman filters, one to each pixel. The resistance of the resulting template to partial occlusions enables the accurate detection and handling of more severe occlusions. Abrupt changes of lighting conditions can also be handled, especially when photometric invariant color features are used. The method has only a few parameters and is computationally fast enough to track objects in real time.

The watershed algorithm from mathematical morphology is powerful for segmentation. However, it does not allow incorporation of a priori information as segmentation methods that are based on energy minimization. In particular, there is no control of the smoothness of the segmentation result. In this paper, we show how to represent watershed segmentation as an energy minimization problem using the distance-based definition of the watershed line. A priori considerations about smoothness can then be imposed by adding the contour length to the energy function. This leads to a new segmentation method called watersnakes, integrating the strengths of watershed segmentation and energy based segmentation. Experimental results show that, when the original watershed segmentation has noisy boundaries or wrong limbs attached to the object of interest, the proposed method overcomes those drawbacks and yields a better segmentation.

In our method for tracking objects, appearance features are smoothed by robust and adaptive Kalman filters, one to each pixel, making the method robust against occlusions. While existing methods use only intensity to model the object appearance, we concentrate on multivalue features. Specifically, one option is to use photometric invariant color features, making the method robust to illumination effects such as shadow and object geometry. The method is able to track objects in real time.

We propose a new method for contour tracking in video. The inverted distance transform of the edge map is used as an edge indicator function for contour detection. Using the concept of topographical distance, the watershed segmentation can be formulated as a minimization. This new viewpoint gives a way to combine the results of the watershed algorithm on different surfaces. In particular, our algorithm determines the contour as a combination of the current edge map and the contour, predicted from the tracking result in the previous frame. We also show that the problem of background clutter can be relaxed by taking the object motion into account. The compensation with object motion allows to detect and remove spurious edges in background. The experimental results confirm the expected advantages of the proposed method over the existing approaches.

In this paper we introduce a new model-free method for object tracking. The tracking is posed as a segmentation problem which we solve using the watershed algorithm. A framework is defined to compute the required topographic surface from distances to the predicted contour intensity, edges and motion edges. This multifeature tracking approach yields accurate results in the presence of object corners, image clutter and camera motion. Results on real sequences confirm the stability and robustness of the method. Objects are tracked over long sequences and in the presence of fast object motion.

This correspondence deals with the segmentation of a video clip into independently moving visual objects. This is an important step in structuring video data for storage in digital libraries. The method follows a bottom-up approach. The major contribution is a new well-founded measure for motion similarity leading to a robust method for merging regions. The improvements with respect to existing methods have been confirmed by experimental results.