IEEE Transactions on Pattern Analysis and Machine Intelligence

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The IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) is a monthly journal published by the IEEE Computer Society. (Wikipedia.org)




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Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Circuits and Systems for Video Technology, IEEE Transactions on

Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...


Computational Biology and Bioinformatics, IEEE/ACM Transactions on

Specific topics of interest include, but are not limited to, sequence analysis, comparison and alignment methods; motif, gene and signal recognition; molecular evolution; phylogenetics and phylogenomics; determination or prediction of the structure of RNA and Protein in two and three dimensions; DNA twisting and folding; gene expression and gene regulatory networks; deduction of metabolic pathways; micro-array design and analysis; proteomics; ...


Computer-Aided Design of Integrated Circuits and Systems, IEEE Transactions on

Methods, algorithms, and human-machine interfaces for physical and logical design, including: planning, synthesis, partitioning, modeling, simulation, layout, verification, testing, and documentation of integrated-circuit and systems designs of all complexities. Practical applications of aids resulting in producible analog, digital, optical, or microwave integrated circuits are emphasized.


Fuzzy Systems, IEEE Transactions on

Theory and application of fuzzy systems with emphasis on engineering systems and scientific applications. (6) (IEEE Guide for Authors) Representative applications areas include:fuzzy estimation, prediction and control; approximate reasoning; intelligent systems design; machine learning; image processing and machine vision;pattern recognition, fuzzy neurocomputing; electronic and photonic implementation; medical computing applications; robotics and motion control; constraint propagation and optimization; civil, chemical and ...


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Editorial

IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995

THE IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) has become the premier journal in our field due to the vision and leadership provided by past editors- in-chief, King-Sun Fu, Theo Pavlidis, Steve Tanimoto and Anil Jain. Thus, it is with a great sense of honor I have accepted the responsibilities of becoming PAMI's fifth Editorin- Chief. My primary ...


Twenty years of document image analysis in PAMI

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

The contributions to document image analysis of 99 papers published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) are clustered, summarized, interpolated, interpreted, and evaluated.


Geometric structure analysis of document images: a knowledge-based approach

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

This paper presents a knowledge-based method for sophisticated geometric structure analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form of rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result ...


Clarification of Assumptions in the Relationship between the Bayes Decision Rule and the Whitened Cosine Similarity Measure

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008

This paper first clarifies Assumption 3 (which misses a constant) and Assumption 4 (where the whitened pattern vectors represent the whitened means) in the paper ";The Bayes Decision Rule Induced Similarity Measures"; (IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1086- 1090, June 2007) and then provides examples to show that the assumptions after the ...


Erratum to "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements"

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014

The author of the paper "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements," which appeared in the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 378-384, Feb. 2010, points out various corrections to equation (15), Table 3, and the first sentence following this table on page 383. The author is ...


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IEEE-USA E-Books

  • Editorial

    THE IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) has become the premier journal in our field due to the vision and leadership provided by past editors- in-chief, King-Sun Fu, Theo Pavlidis, Steve Tanimoto and Anil Jain. Thus, it is with a great sense of honor I have accepted the responsibilities of becoming PAMI's fifth Editorin- Chief. My primary goal is to maintain its excellent reputation and quality while enhancing its value to the subscribers. In this editorial I would like to share some of my plans to achieve this goal.

  • Twenty years of document image analysis in PAMI

    The contributions to document image analysis of 99 papers published in the IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) are clustered, summarized, interpolated, interpreted, and evaluated.

  • Geometric structure analysis of document images: a knowledge-based approach

    This paper presents a knowledge-based method for sophisticated geometric structure analysis of technical journal pages. The proposed knowledge base encodes geometric characteristics that are not only common in technical journals but also publication-specific in the form of rules. The method takes the hybrid of top-down and bottom-up techniques and consists of two phases: region segmentation and identification. Generally, the result of the segmentation process does not have a one-to-one matching with composite layout components. Therefore, the proposed method identifies non-text objects, such as images, drawings, and tables, as well as text objects, by splitting or grouping segmented regions into composite layout components. Experimental results with 372 images scanned from the IEEE Transactions on Pattern Analysis and Machine Intelligence show that the proposed method has performed geometric structure analysis successfully on more than 99 percent of the test images.

  • Clarification of Assumptions in the Relationship between the Bayes Decision Rule and the Whitened Cosine Similarity Measure

    This paper first clarifies Assumption 3 (which misses a constant) and Assumption 4 (where the whitened pattern vectors represent the whitened means) in the paper ";The Bayes Decision Rule Induced Similarity Measures"; (IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1086- 1090, June 2007) and then provides examples to show that the assumptions after the clarification are consistent.

  • Erratum to "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements"

    The author of the paper "Visualization of Spatiotemporal Behavior of Discrete Maps via Generation of Recursive Median Elements," which appeared in the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 2, pp. 378-384, Feb. 2010, points out various corrections to equation (15), Table 3, and the first sentence following this table on page 383. The author is grateful to Raghvendra Sharma for finding these typos/errors while understanding the algorithm's description.

  • Classification Bias of the k-Nearest Neighbor Algorithm

    The k-nearest neighbor classifier has been used extensively in pattern analysis applications. This classifier can, however, have substantial bias when there is little class separation and the sample sizes are unequal. This classification bias is examined for the two-class situation and formulas presented that allows selection of values of k that yields minimum bias.

  • Estimation three-dimensional motion of rigid objects from noisy observations

    An estimate is made of the motion of a rigid body from two noisy 2-D perspective projections using the least-squares method and the algebra of R.Y. Tsai and T.S. Huang (1984). The accuracy of the estimated motion parameters is influenced by the position of the features of the object used in the calculation. Four test variables are derived that indicate how the accuracy is affected, and they are used for discarding inaccurate estimates. Monte Carlo tests demonstrate the obtained accuracy.<<ETX>>

  • A Small Sphere and Large Margin Approach for Novelty Detection Using Training Data with Outliers

    We present a small sphere and large margin approach for novelty detection problems, where the majority of training data are normal examples. In addition, the training data also contain a small number of abnormal examples or outliers. The basic idea is to construct a hypersphere that contains most of the normal examples, such that the volume of this sphere is as small as possible, while at the same time the margin between the surface of this sphere and the outlier training data is as large as possible. This can result in a closed and tight boundary around the normal data. To build such a sphere, we only need to solve a convex optimization problem that can be efficiently solved with the existing software packages for training nu-support vector machines. Experimental results are provided to validate the effectiveness of the proposed algorithm.

  • Recovery of ego-motion using region alignment

    A method for computing the 3D camera motion (the ego-motion) in a static scene is described, where initially a detected 2D motion between two frames is used to align corresponding image regions. We prove that such a 2D registration removes all effects of camera rotation, even for those image regions that remain misaligned. The resulting residual parallax displacement field between the two region-aligned images is an epipolar field centered at the FOE (Focus- of-Expansion). The 3D camera translation is recovered from the epipolar field. The 3D camera rotation is recovered from the computed 3D translation and the detected 2D motion. The decomposition of image motion into a 2D parametric motion and residual epipolar parallax displacements avoids many of the inherent ambiguities and instabilities associated with decomposing the image motion into its rotational and translational components, and hence makes the computation of ego-motion or 3D structure estimation more robust.

  • Estimation of Nonlinear Errors-in-Variables Models for Computer Vision Applications

    In an errors-in-variables (EIV) model, all the measurements are corrupted by noise. The class of EIV models with constraints separable into the product of two nonlinear functions, one solely in the variables and one solely in the parameters, is general enough to represent most computer vision problems. We show that the estimation of such nonlinear EIV models can be reduced to iteratively estimating a linear model having point dependent, i.e., heteroscedastic, noise process. Particular cases of the proposed heteroscedastic errors-in-variables (HEIV) estimator are related to other techniques described in the vision literature: the Sampson method, renormalization, and the fundamental numerical scheme. In a wide variety of tasks, the HEIV estimator exhibits the same, or superior, performance as these techniques and has a weaker dependence on the quality of the initial solution than the Levenberg-Marquardt method, the standard approach toward estimating nonlinear models



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