Many classification algorithms (such as Adaboost) aim to minimize training error, that is, the ratio of the number of mis-classified examples to the total number of examples. However, it is often desirable to minimize a more general cost metric, where distinct examples have different costs. For example, in industrial inspection, the cost of clearing a defective part is substantially greater than the cost of incorrectly alarming on a good part. Further, defects themselves vary in severity and, hence, cost. Extending boosting algorithms to account for this variable cost (cost-sensitive boosting) has been attempted by a number of investigators, resulting in a number of Adaboost variants, including AdaCost, Asymboost, CSB0, CSB1, and CSB2. These algorithms all focus only on the distribution update step in Adaboost-leaving the error computation step unchanged (or only indirectly modified). In this paper, we present a novel algorithm, termed "distributed cost boosting" (DCB), that incorporates the cost metric into the error computation step, rather than merely adjusting the distribution update step. DCB produces large empirical reductions in total cost when compared to current state-ofthe- art cost-sensitive Adaboost variants. Further, we show that distributed cost boosting provides error bounds on misclassification cost similar to AdaBoost’s bounds on training error rate.

A common difficulty encountered in tracking applications is how to track an object that becomes totally occluded, possibly for a significant period of time. Another problem is how to associate objects, or tracklets, across non-overlapping cameras, or between observations of a moving sensor that switches fields of regard. A third problem is how to update appearance models for tracked objects over time. As opposed to using a comprehensive multi-object tracker that must simultaneously deal with these tracking challenges, we present a novel, modular framework that handles each of these problems in a unified manner by the initialization, tracking, and linking of high-confidence tracklets. In this track/suspend/match paradigm, we first analyze the scene to identify areas where tracked objects are likely to become occluded. Tracking is then suspended on occluded objects and re-initiated when they emerge from behind the occlusion. We then associate, or match, suspended tracklets with the new tracklets using full kinematic models for object motion and Gibbsian distributions for object appearance in order to complete the track through the occlusion. Sensor gaps are handled in a similar manner, where tracking is suspended when the sensor looks away and then re-initiated when the sensor returns. Changes in object appearance and orientation during tracking are also seamlessly handled in this framework. Tracklets with low lock scores are terminated. Tracking then resumes on untracked movers with corresponding updated appearance models. These new tracklets are then linked back to the terminated ones as appropriate. Fully automatic tracking results from a moving sensor are presented.

The estimation of local arterial structure and vascular deformation is hampered by regions of poor contrast and by the presence of ultrasound imaging artifacts such as speckle noise and specular reflections. Conventional methods for boundary extraction and velocity estimation can exhibit poor performance under these conditions. We demonstrate a new multi-stage processing scheme which uses a directionally sensitive variational energy segmentation to suppress noise and preserve image boundaries for robust extraction of the vessel contour. This contour information is used to generate a motion region-of-interest (mROI) for a modified optical flow scheme which estimates local boundary motion based only on image data lying within the mROI.

Modeling the electrical properties of neurons (e.g. with a distributed transmission line) requires extensive knowledge of 3D morphological structure, such as dendritic trajectories from the soma. Confocal microscopy provides sharp contrast for acquiring such 3D information, but these microscopes are expensive and not widely accessible, which prompts many researchers to resort to the more accessible optical sectioning microscopy (OSM) techniques using camera lucida to recover 3D structure. Unfortunately, this method is slower and subjective because it requires extensive human intervention. We present and validate a new elegantly simple nonparametric method for semi-automated 3D spatial reconstruction of dendritic trajectories within an OSM slice. We exploit the thin structure of the dendrites and apply depth from defocus techniques for automatic range-finding in the microscope slide without specific knowledge of the optical transfer function of the microscope. Specifically, we interpolate the depth of focus which maximizes the radial high frequency energy in an image patch. In this way, we accurately and efficiently estimate the 3D structure of the dendritic arborization.

Many large multidimensional space-time signal processing and data inversion applications (e.g. deconvolution) require some form of regularization to extract meaningful information. A popular approach to regularizing such problems in a statistical context is via a Gauss-Markov random field (GMRF) prior model in a maximum a posteriori (MAP) estimation framework. While providing good reconstructions, the high dimensionality of these problems can lead to prohibitive computational constraints which limit their practical applicability, particularly in real or near-real time applications. It has been shown that GMRF models possess a particular recursive structure. Conversely, complementary work in suboptimal filtering has been based on reduced order GMRF modeling. In, this work, we combine these two results to present a suboptimal filter design which repeatedly takes advantage of this recursive GMRF structure to subdivide a large problem into a series of smaller, more tractable problems. In this way we present a method for approximate, model-based, recursive solution to such high dimensional problems based on their inherent recursive structure.

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid aids in morphological quantification of brain features. The large amount of data associated with typical MRI brain scans makes completely manual segmentation prohibitive on a large scale. We develop an estimation-theoretic interpretation of the segmentation problem which leads to a computationally efficient, statistically based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are useful both for evaluation of the estimates as well as their combination with other sources of information.

Magnetic resonance imaging (MRI) has become a widely used research and clinical tool in the study of the human brain. The ability to robustly and accurately quantify repeatable morphological measures from such data is aided by the ability to accurately segment the MRI data set into homogeneous regions such as gray matter, white matter, and cerebro spinal fluid. The large amount of data associated with typical MRI scans makes completely manual segmentation prohibitive on a large scale. In this paper an efficient approach to the segmentation of such MR imagery is presented. The approach uses an estimation-theoretic interpretation of the segmentation problem to develop a computationally efficient, statistically-based recursive technique for its solution. Being statistically based, the method also provides associated measures of uncertainty of the resulting estimates, which are extremely important both for evaluation of the estimates as well as their combination with other sources of information.