The Experts below are selected from a list of 9627 Experts worldwide ranked by ideXlab platform

Paul L Rosin - One of the best experts on this subject based on the ideXlab platform.

  • training cellular automata for image processing
    IEEE Transactions on Image Processing, 2006
    Co-Authors: Paul L Rosin
    Abstract:

    Experiments were carried out to investigate the possibility of training cellular automata (CA) to perform several image processing tasks. Even if only binary images are considered, the space of all possible rule sets is still very large, and so the training process is the main bottleneck of such an approach. In this paper, the sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely noise filtering (also applied to grayscale images using Threshold Decomposition), thinning, and convex hulls. Various objective functions for driving the search were considered. Several modifications to the standard CA formulation were made (the B-rule and two-cycle CAs), which were found, in some cases, to improve performance.

  • training cellular automata for image processing
    IEEE Transactions on Image Processing, 2006
    Co-Authors: Paul L Rosin
    Abstract:

    Experiments were carried out to investigate the possibility of training cellular automata (CA) to perform several image processing tasks. Even if only binary images are considered, the space of all possible rule sets is still very large, and so the training process is the main bottleneck of such an approach. In this paper, the sequential floating forward search method for feature selection was used to select good rule sets for a range of tasks, namely noise filtering (also applied to grayscale images using Threshold Decomposition), thinning, and convex hulls. Various objective functions for driving the search were considered. Several modifications to the standard CA formulation were made (the B-rule and two-cycle CAs), which were found, in some cases, to improve performance.

Y Neuvo - One of the best experts on this subject based on the ideXlab platform.

  • Optimal parallel stack filtering under the mean absolute error criterion
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1994
    Co-Authors: Bing Zeng, Y Neuvo
    Abstract:

    The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block Threshold Decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean absolute error (MAE) criterion is shown to be similar to the minimum MAE stack filtering theory. The only difference is that one needs now to design more than one stack filter that together construct an optimal PSF. As a result, while reviewing briefly the optimal stack filtering theory, they will put more efforts to demonstrate, via several examples, the improvement by switching from stack filtering to parallel stack filtering for the task of image noise removal. >

  • adaptive stack filtering with application to image processing
    IEEE Transactions on Signal Processing, 1993
    Co-Authors: Lin Yia, Jaakko Astola, Y Neuvo
    Abstract:

    With the aid of Threshold Decomposition, it is shown that optimal stack filters under the mean absolute error (MAE) criterion are equal to optimal (or Bayesian) classifiers subject to stacking constraints under the mean classification error (MCE) criterion. Nonadaptive and adaptive constrained least mean absolute (LMA) algorithms are developed for the esti- mation of stack filters through the linearization of the unit step function in the objective function. The convergence of the al- gorithms is proven under certain conditions. Although the methods do not generally give optimal stack filters under the MAE criterion, these algorithms have several distinct merits compared to other stack filter optimization methods: 1) the op- timization problem has a unique solution that approximates the optimal stack filters in the least mean square sense; 2) the meth- ods can be implemented in the binary and real domains; and 3) for stack filters defined by linearly separable positive Boolean functions (PBF's) or weighted order statistic (WOS) filters, the number of the parameters to be estimated is reduced to the window width of the filter. A comparison between images re- stored by the new algorithms and the stack filtering algorithm optimal under the MAE criterion confirms the effectiveness of the proposed algorithms.

Bing Zeng - One of the best experts on this subject based on the ideXlab platform.

  • super resolution image by edge constrained curve fitting in the Threshold Decomposition domain
    European Signal Processing Conference, 2008
    Co-Authors: Bing Zeng
    Abstract:

    An edge-constrained curve-fitting method is proposed in this paper to produce super-resolution (SR) images from a low-resolution (LR) source image. The novelty of this method lies that the Threshold Decomposition is applied on the source image to obtain multiple binary images, and then an edge-constrained curve-fitting is applied on the resulting set of binary images. This allows us to focus on tiny objects and thin structures so as to achieve rather nice visual results even when a large zoom-in factor is used. Our results are compared with those achieved by using the bi-cubic interpolation, showing the ability of our algorithm to achieve much better visual quality in smooth areas as well as for sharp edges and small objects.

  • Optimal parallel stack filtering under the mean absolute error criterion
    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society, 1994
    Co-Authors: Bing Zeng, Y Neuvo
    Abstract:

    The authors extend the configuration of stack filtering to develop a new class of stack-type filters called parallel stack filters (PSFs). As a basis for the parallel stack filtering, the block Threshold Decomposition (BTD) is introduced, and its properties are investigated. The design of optimal PSHs under the mean absolute error (MAE) criterion is shown to be similar to the minimum MAE stack filtering theory. The only difference is that one needs now to design more than one stack filter that together construct an optimal PSF. As a result, while reviewing briefly the optimal stack filtering theory, they will put more efforts to demonstrate, via several examples, the improvement by switching from stack filtering to parallel stack filtering for the task of image noise removal. >

Josep Ramon Morros - One of the best experts on this subject based on the ideXlab platform.

  • gray scale erosion algorithm based on image bitwise Decomposition application to focal plane processors
    International Conference on Acoustics Speech and Signal Processing, 2009
    Co-Authors: Andres Friasvelazquez, Josep Ramon Morros
    Abstract:

    A novel approach to implement gray-scale morphological operations is presented in this work. This new technique is based on the bitwise Decomposition of the gray-scale image, yielding bitplanes disposed according to their bit of significance. It is of particular interest for implementations on Focal Plane Processors. Our approach relies on the binary search method to obtain either the maximum or minimum on a local neighborhood by manipulating the binary levels resulting from the bitwise Decomposition with simple logic functions. This contrasts significantly with the classical Threshold Decomposition (TD) approach, on which most of the current techniques are based on. Our method shows better efficiency than TD implementations. Further gains can be obtained because our method shows a strong dependency on the image dynamic range.

E.j. Coyle - One of the best experts on this subject based on the ideXlab platform.

  • Generalized stack filters and minimum mean absolute error estimation
    1988. IEEE International Symposium on Circuits and Systems, 2026
    Co-Authors: J.-h. Lin, E.j. Coyle
    Abstract:

    A class of sliding window operators called generalized stack filters is developed. This class of filters, which includes all rank order filters, stack filters, and digital morphological filters, is the set of all filters possessing the Threshold Decomposition architecture and a consistency property called the stacking property. A linear program is provided which determines a generalized stack filter which minimizes the mean absolute error (MAE) between the output of the filter and a desired input signal, given noisy observations of that signal. These results show that choosing the generalized stack filter that minimizes the MAE is equivalent to massively parallel Threshold-crossing decision-making when these decisions are consistent with each other. >