Image detection through bipolar correlation

Arcese, A.; Mengert, Peter; Trombini, Elena

doi:10.1109/tit.1970.1054531

Cited by 37 publications

(7 citation statements)

References 9 publications

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“…In fact, our closed-form solution is found to give very accurate localization, and since it is not differentiable at the center, it overcomes the tradeoff between detection and localization (Section II-B) which is observed in [7]. It is also interesting that our edge operator is the second derivative of the smoothed edge profile, which is identical to one of the basis operators in [1].…”

Section: Optimal Edge Operatormentioning

confidence: 80%

“…Argyle [2] suggested the use of a split-Gaussian operator to alleviate the problem of discontinuities. An interesting paper by Arcese et al [1] shows that the (maximum-SNR sense) optimal filter for an intensity profile (not necessarily a step) in the presence of correlated noise with exponential correlation is a linear combination of the signal and the second derivative of the signal; the extreme case when the noise is independent corresponds to the signal only (the matched filter).…”

Section: Optimal Edge Operatormentioning

confidence: 99%

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Optimal edge-based shape detection

Moon

Chellappa

Rosenfeld

2002

IEEE Trans. on Image Process.

106

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Abstract-We propose an approach to accurately detecting twodimensional (2-D) shapes. The cross section of the shape boundary is modeled as a step function. We first derive a one-dimensional (1-D) optimal step edge operator, which minimizes both the noise power and the mean squared error between the input and the filter output. This operator is found to be the derivative of the double exponential (DODE) function, originally derived by Ben-Arie and Rao [5]. We define an operator for shape detection by extending the DODE filter along the shape's boundary contour. The responses are accumulated at the centroid of the operator to estimate the likelihood of the presence of the given shape. This method of detecting a shape is in fact a natural extension of the task of edge detection at the pixel level to the problem of global contour detection. This simple filtering scheme also provides a tool for a systematic analysis of edge-based shape detection. We investigate how the error is propagated by the shape geometry. We have found that, under general assumptions, the operator is locally linear at the peak of the response. We compute the expected shape of the response and derive some of its statistical properties. This enables us to predict both its localization and detection performance and adjust its parameters according to imaging conditions and given performance specifications. Applications to the problem of vehicle detection in aerial images, human facial feature detection, and contour tracking in video are presented.

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Section: Optimal Edge Operatormentioning

confidence: 80%

Section: Optimal Edge Operatormentioning

confidence: 99%

Optimal edge-based shape detection

Moon

Chellappa

Rosenfeld

2002

IEEE Trans. on Image Process.

106

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show abstract

“…In other words, due to A be known, which lead to reduction from the uncertainty of B , that is, the information amount of B were included of A [7]. Similarly, ) , ( B A I was also expressed that the information amount of A included from B , so it was called mutual information.…”

Section: Mutual Informationmentioning

confidence: 99%

Multi-sensor image automatic registration using mutual information

Jiang

2013

SPIE Proceedings

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A novel multi-sensor image registration algorithm was introduced, which was based on wavelet transform and the maximization of mutual information. Firstly, the region of interest was determined by search of wavelet magnitude maxima in the preprocessing step of images, and the affine transform was chosen as the image transform model. Secondly, the probability distribution of grey value was estimated by interpolation of pixel values to calculate image intensity histogram. Finally, the simulated annealing algorithm was used to approximate the global optimum. The experiment data show that the algorithm was effective and feasible.

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“…If the clutter is first-order Markov, then a pixel's intensity is only correlated with its adjacent pixels. In this case the clutter correlation is described by an exponential function that can be described analytically in terms of a correlation coefficient p. Arcess et al [96] show that the whitening filter can be expressed as a…”

Section: Clutter Whiteningmentioning

confidence: 99%

Physics-Based Radiometric Signature Modeling and Detection Algorithms of Landmines Using Electro-Optical Sensors

Liao¹

2005

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Image detection through bipolar correlation

Cited by 37 publications

References 9 publications

Optimal edge-based shape detection

Optimal edge-based shape detection

Multi-sensor image automatic registration using mutual information

Physics-Based Radiometric Signature Modeling and Detection Algorithms of Landmines Using Electro-Optical Sensors

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