A 2-D Joint Process Lattice Filter for Adaptive Change Detection in Image Sequence

Youlal, H.; Janati, M’hamed El; Najim, M.

doi:10.1016/b978-0-444-89587-5.50068-2

Cited by 2 publications

(2 citation statements)

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“…This structure is the same as the one proposed in [1] with the exception that none of the reflection coefficients, , are assumed to be the same. The equations for each stage of the lattice are (2) through (5) can be rewritten as a single equation (6) where (the pixel index, , is left out of the matrix and vector for clarity) 7For a single lattice stage filter the subscript is equal to 1, and the input values to this first lattice stage are set equal to the image value itself (8) The goal of the algorithm is to minimize the sum of the square of the four prediction output values , and , described by the following equation:…”

Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

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Enhanced detectability of small objects in correlated clutter using an improved 2-D adaptive lattice algorithm

Ffrench¹,

Zeidler

1997

IEEE Trans. on Image Process.

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Two-dimensional (2-D) adaptive filtering is a technique that can be applied to many image processing applications. This paper will focus on the development of an improved 2-D adaptive lattice algorithm (2-D AL) and its application to the removal of correlated clutter to enhance the detectability of small objects in images. The two improvements proposed here are increased flexibility in the calculation of the reflection coefficients and a 2-D method to update the correlations used in the 2-D AL algorithm. The 2-D AL algorithm is shown to predict correlated clutter in image data and the resulting filter is compared with an ideal Wiener-Hopf filter. The results of the clutter removal will be compared to previously published ones for a 2-D least mean square (LMS) algorithm. 2-D AL is better able to predict spatially varying clutter than the 2-D LMS algorithm, since it converges faster to new image properties. Examples of these improvements are shown for a spatially varying 2-D sinusoid in white noise and simulated clouds. The 2-D LMS and 2-D AL algorithms are also shown to enhance a mammogram image for the detection of small microcalcifications and stellate lesions.

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Section: A Development Of a Single 2-d Al Lattice Stagementioning

confidence: 99%

“…The signal image is also assumed to be unknown but small. With known image signals, other more optimum structures can be used to enhance the detection, as shown in [8]. The 2-D AL algorithm will now be compared with the previously published results for the 2-D LMS algorithm in [16].…”

Section: -D Al Algorithm As a Prewhitenermentioning

confidence: 99%