Craig Carthel

Bio: Craig Carthel is an academic researcher from Centre for Maritime Research and Experimentation. The author has contributed to research in topics: Sensor fusion & Graph (abstract data type). The author has an hindex of 15, co-authored 75 publications receiving 1957 citations. Previous affiliations of Craig Carthel include University of Houston & NATO.

Objective comparison of particle tracking methods

Abstract: Particle tracking is of key importance for quantitative analysis of intracellular dynamic processes from time-lapse microscopy image data. Because manually detecting and following large numbers of individual particles is not feasible, automated computational methods have been developed for these tasks by many groups. Aiming to perform an objective comparison of methods, we gathered the community and organized an open competition in which participating teams applied their own methods independently to a commonly defined data set including diverse scenarios. Performance was assessed using commonly defined measures. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, leading to notable practical conclusions for users and developers.

A benchmark for comparison of cell tracking algorithms

Abstract: Motivation: Automatic tracking of cells in multidimensional time� lapse fluorescence microscopy is an important task in many biomed� ical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this paper, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark. Results: The main contributions of the challenge include the crea� tion of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmenta� tion and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately. Availability and implementation: The challenge website (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Win� dows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.

Advances in asynchronous and decentralized estimation

Abstract: Two key challenges associated with fusion of information in large-scale systems are the asynchronous nature of information flow and the consistency requirements associated with decentralized processing. This paper provides contributions in both these areas. First, we build on an existing minimum variance estimation algorithm for out-of-sequence processing of sensor measurements, extending the algorithm to handle multiple lags and multiple dynamic models. We study the performance of the algorithms with numerical examples. Second, we establish a connection between the maximum entropy of a partially known multivariable Gaussian distribution and a particular Bayesian network, whose structure is based on the available information. The connection leads to a useful methodology for identifying missing information in systems described by Bayesian networks, a key tool in developing algorithms for information flow in decentralized systems.

On exact and approximate boundary controllabilities for the heat equation: a numerical approach

Abstract: The present article is concerned with the numerical implementation of the Hilbert uniqueness method for solving exact and approximate boundary controllability problems for the heat equation. Using convex duality, we reduce the solution of the boundary control problems to the solution of identification problems for the initial data of an adjoint heat equation. To solve these identification problems, we use a combination of finite difference methods for the time discretization, finite element methods for the space discretization, and of conjugate gradient and operator splitting methods for the iterative solution of the discrete control problems. We apply then the above methodology to the solution of exact and approximate boundary controllability test problems in two space dimensions. The numerical results validate the methods discussed in this article and clearly show the computational advantage of using second-order accurate time discretization methods to approximate the control problems.

Distributed tracking in multistatic sonar

Abstract: This paper develops centralized and distributed tracker performance models that account for the fading detection performance that is common in active sonar contact data. We apply centralized and distributed tracking algorithms to simulated active sonar data that exhibits detection fading, and compare tracking performance to that predicted by the tracker models. We find qualitative agreement of model-based and simulation-based results, and we identify a tradeoff between centralized and distributed approaches: the former achieves better localization accuracy, while the latter achieves better performance in a receiver operating characteristic (ROC) curve sense.

U-Net: Convolutional Networks for Biomedical Image Segmentation

Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .

U-Net: Convolutional Networks for Biomedical Image Segmentation

Abstract: There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at this http URL .

Principles Of Optics Electromagnetic Theory Of Propagation Interference And Diffraction Of Light

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