Phase Correlation Based Image Alignment with Subpixel Accuracy

Alba, Alfonso; Aguilar-Ponce, Ruth M.; Vigueras, Javier Flavio; Arce-Santana, Edgar R.

doi:10.1007/978-3-642-37807-2_15

Cited by 30 publications

(16 citation statements)

References 14 publications

(19 reference statements)

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“…(Figure 12). In this new space, the same model of Equation (19) leads to estimates of the scale and rotation parameters [34][35][36]. More details can be found in [34][35][36].…”

Section: I2mentioning

confidence: 99%

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Shadow-Based Hierarchical Matching for the Automatic Registration of Airborne LiDAR Data and Space Imagery

2016

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Abstract:The automatic registration of LiDAR data and optical images, which are heterogeneous data sources, has been a major research challenge in recent years. In this paper, a novel hierarchical method is proposed in which the least amount of interaction of a skilled operator is required. Thereby, two shadow extraction schemes, one from LiDAR and the other from high-resolution satellite images, were used, and the obtained 2D shadow maps were then considered as prospective matching entities. Taken as the base, the reconstructed LiDAR shadows were transformed to image shadows using a four-step hierarchical method starting from a coarse 2D registration model and leading to a fine 3D registration model. In the first step, a general matching was performed in the frequency domain that yielded a rough 2D similarity model that related the LiDAR and image shadow masks. This model was further improved by modeling and compensating for the local geometric distortions that existed between the two heterogeneous data sources. In the third step, shadow masks, which were organized as segmented matched patches, were the subjects of a coinciding procedure that resulted in a coarse 3D registration model. In the last hierarchical step, that model was ultimately reinforced via a precise matching between the LiDAR and image edges. The evaluation results, which were conducted on six datasets and from different relative and absolute aspects, demonstrated the efficiency of the proposed method, which had a very promising accuracy on the order of one pixel.

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“…(Figure 12). In this new space, the same model of Equation (19) leads to estimates of the scale and rotation parameters [34][35][36]. More details can be found in [34][35][36].…”

Section: I2mentioning

confidence: 99%

“…More details can be found in [34][35][36]. More details can be found in [34][35][36]. The estimated 2D-similarity parameters were finally implemented on the LSM to produce the Primary Transformed LiDAR Shadow Mask (PTLSM).…”

Section: I2mentioning

confidence: 99%

Shadow-Based Hierarchical Matching for the Automatic Registration of Airborne LiDAR Data and Space Imagery

2016

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“…The drawback was that the recovered translation has pixel accuracy only. Subpixel precision techniques, were later introduced for improving the peak estimation, using fitting functions [12,20], or finding approximate zeros of the gradient of the inverse Fourier transform of the normalized cross-power spectrum [1], which is more robust against border effect and multiple motions. Foorosh [12] suggested to prefilter the phase difference matrix to remove aliased components (generally at high spatial frequencies), but filtering must be adjusted to each image and sensor.…”

Section: Related Workmentioning

confidence: 99%

Image Alignment for Panorama Stitching in Sparsely Structured Environments

et al. 2015

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Panorama stitching of sparsely structured scenes is an open research problem. In this setting, feature-based image alignment methods often fail due to shortage of distinct image features. Instead, direct image alignment methods, such as those based on phase correlation, can be applied. In this paper we investigate correlation-based image alignment techniques for panorama stitching of sparsely structured scenes. We propose a novel image alignment approach based on discriminative correlation filters (DCF), which has recently been successfully applied to visual tracking. Two versions of the proposed DCF-based approach are evaluated on two real and one synthetic panorama dataset of sparsely structured indoor environments. All three datasets consist of images taken on a tripod rotating 360 degrees around the vertical axis through the optical center. We show that the proposed DCF-based methods outperform phase correlation based approaches on these datasets.

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“…We believed a solution to the rotation and scaling problem was to apply a widely-known phase-based registration (PR) technique [1,2], shown in Fig. 2, right.…”

Section: B Frequency-domain (Phase Registration) Approachmentioning

confidence: 99%