We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric...
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We describe the construction of accurate panoramic mosaics from multiple images taken with a rotating camera, or alternatively of a planar scene. The novelty of the approach lies in (i) the transfer of photogrammetric bundle adjustment techniques to mosaicing;(ii) a new representation of image line measurements enabling the use of lines in camera self-calibration, including computation of the radial and other non-linear distortion;and (iii) the application of the variable state dimension filter to obtain efficient sequential updates of the mosaic as each image is added. We demonstrate that our method achieves better results than the alternative approach of optimising over pairs of images. (C) 2002 Elsevier Science B.V. All rights reserved.
In this paper we propose a complete framework that enables big-data tools to execute sequential computer vision algorithms in a scalable and parallel mechanism with limited modifications. Our main objective is to para...
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ISBN:
(纸本)9781538672662
In this paper we propose a complete framework that enables big-data tools to execute sequential computer vision algorithms in a scalable and parallel mechanism with limited modifications. Our main objective is to parallelize the processing operation in order to speed up the required processing time. Most of the present big-data processing frameworks distribute the input data randomly across the available processing units to utilize them efficiently and preserve working load fairness. Therefore, the current big-data frameworks are not suitable for processing huge video data content due to the existence of interframe dependency. When processing such sequential computer vision algorithms on big-data tools, splitting the video frames and distributing them on the available cores will not yield the correct output and will lead to inefficient usage of underlying processing resources. Our proposed framework divides the input big-data video files into small chunks that can be processed in parallel without affecting the quality of the resulting output. An intelligent data grouping algorithm was developed to distribute these data chunks among the available processing resources and gather the results out of each chunk using Apache Storm. The proposed framework was evaluated against several computer vision algorithms and achieved a speedup from 2.6x up to 8x based on the algorithm.
A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composit...
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A surrogate based particle swarm optimization （SBPSO） algorithm which combines the surrogate modeling technique and particle swarm optimization is applied to the reliability- based robust design （RBRD） of composite pressure vessels. The algorithm and efficiency of SBPSO are displayed through numerical examples. A model for filament-wound composite pressure vessels with metallic liner is then studied by netting analysis and its responses are analyzed by using Finite element method （performed by software ANSYS）. An optimization problem for maximizing the performance factor is formulated by choosing the winding orientation of the helical plies in the cylindrical portion, the thickness of metal liner and the drop off region size as the design variables. Strength constraints for composite layers and the metal liner are constructed by using Tsai-Wu failure criterion and Mises failure criterion respectively. Numerical examples show that the method proposed can effectively solve the RBRD problem, and the optimal results of the proposed model can satisfy certain reliability requirement and have the robustness to the fluctuation of design variables.
We consider the problem of determining an optimal experimental design for estimation of parameters of a class of complex curves characterizing nanowire growth that is partially exponential and partially linear. Locall...
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We consider the problem of determining an optimal experimental design for estimation of parameters of a class of complex curves characterizing nanowire growth that is partially exponential and partially linear. Locally D-optimal designs for some of the models belonging to this class are obtained by using a geometric approach. Further, a Bayesian sequential algorithm is proposed for obtaining D-optimal designs for models with a closed-form solution, and for obtaining efficient designs in situations where theoretical results cannot be obtained. The advantages of the proposed algorithm over traditional approaches adopted in recently reported nanoexperiments are demonstrated using Monte Carlo simulations. The computer code implementing the sequential algorithm is available as supplementary materials.
The concepts of step graphs and networks in which the edges are divided into ordered classes (shortage classes) are introduced. Each path in such graphs is assigned a tuple (an ordered sequence of nonnegative integers...
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The concepts of step graphs and networks in which the edges are divided into ordered classes (shortage classes) are introduced. Each path in such graphs is assigned a tuple (an ordered sequence of nonnegative integers). The tuples are compared lexicographically. The problem of finding the path with the minimum tuple according to this ordering is stated. Functionals are compared using the lexicographic rule. An algorithm for finding the optimal path is described, and the relationship between step networks and weighted networks is investigated. The proposed formalism is applied to the problem of filling a network subject to constraints imposed on communication line capacities with communication flows under the condition that requests for the organization of such flows arrive to the network sequentially in time and the filling strategy must maximize the number of satisfied requests. The idea of an algorithm for the approximate solution of the integer multicommodity problem that uses the concepts of step networks and sequential algorithms is proposed.
Threshold group testing introduced by Damaschke (2006) is a generalization of classical group testing where a group test yields a positive (negative) outcome if it contains at least u (at most!) positive items, and an...
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Threshold group testing introduced by Damaschke (2006) is a generalization of classical group testing where a group test yields a positive (negative) outcome if it contains at least u (at most!) positive items, and an arbitrary outcome for otherwise. Motivated by applications to DNA sequencing, group testing with consecutive positives has been proposed by Balding and Torney (1997) and Colbourn (1999) where n items are linearly ordered and up to d positive items are consecutive in the order. In this paper, we introduce threshold-constrained group tests to group testing with consecutive positives. We prove that all positive items can be identified in [log(2)(n/u-1)] + 2[log(2)(u+2)] +log(2)(u+ 1)]-2 tests for the gap-free case (u = l+1) while the information-theoretic lower bound is [log(2) n(d- u+1)]-1 when n >= d+u-2 and for u = 1 the best adaptive algorithm provided by Juan and Chang (2008) takes at most rlog2 ni rlog2 di tests. We further show that the case with a gap (u > l + 1) can be dealt with by the subroutines used to conquer the gap-free case. (C) 2013 Elsevier B.V. All rights reserved.
The mapping of algorithms structured as depth-p nested FOR loops into special-purpose systolic VLSI linear arrays is addressed. The mappings are done by using linear functions to transform the original sequential algo...
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The mapping of algorithms structured as depth-p nested FOR loops into special-purpose systolic VLSI linear arrays is addressed. The mappings are done by using linear functions to transform the original sequential algorithms into a form suitable for parallel execution on linear arrays. A feasible mapping is derived by identifying formal criteria to be satisfied by both the original sequential algorithm and the proposed transformation function. The methodology is illustrated by synthesizing algorithms for matrix multiplication and a version of the Warshall-Floyd transitive closure algorithm
In this paper, we propose an optimal algorithm for the Multiple-choice Multidimensional Knapsack Problem MMKP. The main principle of the approach is twofold: (i) to generate an initial feasible solution as a starting ...
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In this paper, we propose an optimal algorithm for the Multiple-choice Multidimensional Knapsack Problem MMKP. The main principle of the approach is twofold: (i) to generate an initial feasible solution as a starting lower bound, and (ii) at different levels of the search tree to determine an intermediate upper bound obtained by solving an auxiliary problem called MMKPaux and perform the strategy of fixing items during the exploration. The approach which we develop is of best-first search strategy. The method was able to optimally solve the MMKP. The performance of the exact algorithm is evaluated on a set of small and medium instances, some of them are extracted from the literature and others are randomly generated. This algorithm is parallelizable and it is one of its important feature.
The following restricted model of coin-weighing problem is considered: there is a heavier coin in a set of n coins, n-1 of which are good coins having the same weight. The test device is a two-arms balance scale and e...
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The following restricted model of coin-weighing problem is considered: there is a heavier coin in a set of n coins, n-1 of which are good coins having the same weight. The test device is a two-arms balance scale and each test-set is of the form A : B with vertical bar A vertical bar = vertical bar B vertical bar <= e, where e >= 1 is a given integer. We present an optimal sequential algorithm requiring the minimal average cost of weighings when the probability distribution on the coin set is uniform distribution. (c) 2006 Elsevier B.V. All rights reserved.
The albedo of a Lambertian object is a surface property that contributes to an object's appearance under changing illumination. As a signature independent of illumination, the albedo is useful for object recogniti...
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The albedo of a Lambertian object is a surface property that contributes to an object's appearance under changing illumination. As a signature independent of illumination, the albedo is useful for object recognition. Single image-based albedo estimation algorithms suffer due to shadows and non-Lambertian effects of the image. In this paper, we propose a sequential algorithm to estimate the albedo from a sequence of images of a known 3D object in varying poses and illumination conditions. We first show that by knowing/estimating the pose of the object at each frame of a sequence, the object's albedo can be efficiently estimated using a Kalman filter. We then extend this for the case of unknown pose by simultaneously tracking the pose as well as updating the albedo through a Rao-Blackwellized particle filter (RBPF). More specifically, the albedo is marginalized from the posterior distribution and estimated analytically using the Kalman filter, while the pose parameters are estimated using importance sampling and by minimizing the projection error of the face onto its spherical harmonic subspace, which results in an illumination-insensitive pose tracking algorithm. Illustrations and experiments are provided to validate the effectiveness of the approach using various synthetic and real sequences followed by applications to unconstrained, video-based face recognition.
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