Iterative X-ray computed tomography (CT) algorithms have the potential for producing high-quality images but are computationally very demanding, especially when applied to high-resolution problems. Focusing on simultaneous iterative reconstruction technique (SIRT), we provide an eigenvalue based scheme for automatically determining a near-optimal value of the relaxation parameter. This accelerates the convergence rate of SIRT to the point where only half the number of iterations normally required is needed. We also modify the way SIRT uses preconditioning to solve a weighted least squares problem. The resulting algorithm, which we call PSIRT, is associated with a smaller memory footprint and calls for less data to be communicated in a distributed-memory implementation. Experimental residual norm and timing results are provided based on cone-beam micro-CT mouse data, including for an ordered subsets study.

The support vector machine (SVM) is considered here in the context of pattern classification. The emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of the learning samples that we call the support set and then determines not only the weights of the classifier but also the hyperparameter that controls the influence of the regularizing penalty term on basis thereof. We provide numerical results using several data sets from the public domain.

Conventional X-ray computed tomography (CT) imaging is based on the assumption that the entire cross-section of an object is illuminated with X-rays at each view angle. When imaging a large object, a large detector array is thus needed. As an alternative, we propose to offset a normal sized detector array such that slightly more than one half of the required projection data is acquired. During reconstruction, the missing data is accounted for by means of an interpolation and weighting scheme. This algorithmic approach for extending the field of view, which we here present in the context of the popular Feldkamp algorithm, is simple but effective. Supportive experimental results are provided based on simulated phantom data as well as real data obtained from a MicroCAT/sup TM/ which is a circular orbit microCT system for small animal imaging.

Describes how to estimate 3D surface models from dense sets of noisy range data taken from different points of view, i.e., multiple range maps. The proposed method uses a sensor model to develop an expression for the likelihood of a 3D surface, conditional on a set of noisy range measurements. Optimizing this likelihood with respect to the model parameters provides an unbiased and efficient estimator. The proposed numerical algorithms make this estimation computationally practical for a wide variety of circumstances. The results from this method compare favorably with state-of-the-art approaches that rely on the closest-point or perpendicular distance metric, a convenient heuristic that produces biased solutions and fails completely when surfaces are not sufficiently smooth, as in the case of complex scenes or noisy range measurements. Empirical results on both simulated and real ladar data demonstrate the effectiveness of the proposed method for several different types of problems. Furthermore, the proposed method offers a general framework that can accommodate extensions to include surface priors, more sophisticated noise models, and other sensing modalities, such as sonar or synthetic aperture radar.

The expectation-maximization maximum-likelihood (EM-ML) algorithm belongs to a family of algorithms that compute positron emission tomography (PET) reconstructions by iteratively solving a large linear system of equations. We describe a preprocessing scheme for automatically focusing the attention, and thus the computational resources, on a subset of the equations and unknowns. Experimental work with a CM-5 parallel computer implementation using a simulated phantom as well as real data obtained from an ECAT 921 PET scanner indicates that quite significant savings can be obtained with respect to both time and space requirements of the EM-ML algorithm without compromising the quality of the reconstructed images.

Within the field of structural pattern analysis, algorithms for inference of discrete mathematical models from samples are an important area of research. This paper gives an extensive survey of state-of-the-art methods for data-driven inductive inference of finite-state automata. In addition to providing notationally consistent descriptions of the methods’ fundamental mode of operation, aspects such as sequential learning, advantages and disadvantages, and the extension to stochastic automata are also addressed.

String alignment by dynamic programming is generalized to include cyclic shift and corresponding optimal alignment cost for strings representing cyclic patterns. A guided search algorithm uses bounds on alignment costs to find all optimal cyclic shifts. The bounds are derived from submatrices of an initial dynamic programming matrix. Algorithmic complexity is analyzed for major stages in the search. The applicability of the method is illustrated with satellite DNA sequences and circularly permuted protein sequences.>

Markov networks are inferred automatically for different classes of learning strings. In subsequent string-to-network alignments for test samples, the networks are used to deduce structural characteristics and to provide similarity measures. By processing the similarity measures as numerical-value features, standard nonparametric decision-theoretic pattern classifiers may be applied to determine class membership. The nearest-neighbor rule and linear discriminant-function classifiers are discussed, and their performances are compared with that of a maximum-likelihood classifier. The hybrid system's ability to determine string orientation correctly is investigated. Experiments with several thousand human banded chromosomes are reported.>

A method for segmentation of composite strings into their constituent substrings is presented as an extension to the Markov network inference technique. The basis for the segmentation is forced landmarks which are network representations of the positions where two substrings meet. The landmarks are forced into the Markov model in the inference thereof through supervised labeling. From subsequent string-to-network alignments, it is possible to estimate the positions in the strings that correspond to forced landmarks and thus segment the strings into their substrings. For discrimination between different classes of composite strings, classification probability measures are modified to include model-based information about estimated positions of the landmarks for individual classes. The potential of the method is illustrated with data representing banded human chromosomes.