A robust registration technique for multi-sensor images

Zamora, Gilberto; Dickens, Molly M.; Mitra, Sunanda

doi:10.1109/iai.1998.666865

Cited by 4 publications

(1 citation statement)

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“…Early attempts to apply cepstral analysis to image registration demonstrated that it was limited to image pairs exhibiting only translation and small rotations (<2 degrees) [ 9 ]. Most of the work that followed was limited to applications with translational disparities alone, such as optical flow analysis [ 10 ], the removal or enhancement of echoes in images [11], determining in conjunction with fuzzy clustering, the translational displacement for registration of multi-sensor images [12]. More recently, Sarnel [13] proposed registration of translated images by applying the cepstrum to the 1-D projections of images along the x and y-axis.…”

Section: Introductionmentioning

confidence: 99%

Robust Image Registration via Cepstral Analysis

González

2011

2011 International Conference on Digital Image Computing: Techniques and Applications

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Cepstrum based image registration has been largely overlooked over the past two decades. Initial investigation of the method was limited to alignment of images exhibiting only translational differences. Later attempts to extend the method to handle rotations proved disappointing. This paper presents a cepstrum-based registration method that provides performance comparable to the state of the art over a wide range of scale factors and rotation angles, with reduced computational load.

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Section: Introductionmentioning

confidence: 99%

Robust Image Registration via Cepstral Analysis

González

2011

2011 International Conference on Digital Image Computing: Techniques and Applications

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Adaptive clustering for segmentation of multi-sensor images

Mitra

Dickens²,

Pemmaraju³

1998 IEEE International Conference on Fuzzy Systems Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98C

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Automatic detection or recognition of specific objects from a sequence of images acquired under varying conditions and from different modalities requires careful segmentation. We present here the use of an adaptive neuro-fuzzy clustering technique for image segmentation and boundary detection for better extraction of the dominant features in images of the same scene acquired by synthetic aperture radar (SAR) and electro-optic (EO) sensors. The advantage of such adaptive segmentation is clearer identification of objects that appear differently due to different sensor characteristics.

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Cross-power spectrum phase for automated registration of multi/hyperspectral data-cubes for efficient information retrieval

Mandujano

Mitra

Proceedings Fifth IEEE Southwest Symposium on Image Analysis and Interpretation

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A robust registration technique for multi-sensor images

Cited by 4 publications

References 10 publications

Robust Image Registration via Cepstral Analysis

Robust Image Registration via Cepstral Analysis

Adaptive clustering for segmentation of multi-sensor images

Cross-power spectrum phase for automated registration of multi/hyperspectral data-cubes for efficient information retrieval

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