Hierarchical model-based motion estimation

Bergen, James R.; Anandan, P.; Hanna, Keith; Hingorani, R.

doi:10.1007/3-540-55426-2_27

Cited by 871 publications

(628 citation statements)

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“…However, high degrees of freedom cannot guarantee high precision. In fast coarse-to-fine alignment, homography is found to be not as stable as affine model [10]. By setting p 7 = p 8 = 0, we actually use affine inter-frame motion for most aerial videos and achieve real time alignment.…”

Section: Frame Alignmentmentioning

confidence: 99%

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Real Time Aerial Video Stitching via Sensor Refinement and Priority Scan

Liao

Wang

et al. 2012

IEICE Trans. Inf. & Syst.

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SUMMARYThe time efficiency of aerial video stitching is still an open problem due to the huge amount of input frames, which usually results in prohibitive complexities in both image registration and blending. In this paper, we propose an efficient framework aiming to stitch aerial videos in real time. Reasonable distortions are allowed as a tradeoff for acceleration. Instead of searching for globally optimized solutions, we directly refine frame positions with sensor data to compensate for the accumulative error in alignment. A priority scan method is proposed to select pixels within overlapping area into the final panorama for blending, which avoids complicated operations like weighting or averaging on pixels. Experiments show that our method can generate satisfying results at very competitive speed.

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Section: Frame Alignmentmentioning

confidence: 99%

“…We prefer the Lucas-Kanade method [9] and a hierarchical acceleration mechanism [10] for efficient alignment. This method iteratively searches for the model parameters p by minimizing the difference term E between two frames I and T , as shown in Eq.…”

Section: Pairwise Registrationmentioning

confidence: 99%

Real Time Aerial Video Stitching via Sensor Refinement and Priority Scan

Liao

Wang

et al. 2012

IEICE Trans. Inf. & Syst.

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“…Resolution [24] for the reconstruction stage of SR and the approach proposed by Keran et al [11] for the Motion estimation [2] in the RBSR. For EBSR, Freeman et al [7] approach is adopted.…”

Section: Introductionmentioning

confidence: 99%

Apparent Resolution Enhancement: Structural Similarity Perspective

Khan¹

2016

IJCA

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This paper presents a detailed investigation and comparative analysis of the two well-known classes of Super Resolution (SR) i.e. Reconstruction Based Super Resolution (RBSR) and the Example Based Super Resolution (EBSR), taking into account the seven variables: Resolution Factor, Aliasing, Stills, Motion, Compression, Noise and Detection scenario. The EBSR uses high and low frequency relationship and the RBSR is based on the frames sequence information. The EBSR and RBSR are tested on number of images to investigate which of the two classes of SR algorithms are best suited for preserving structural similarity to the original image and for visual analysis of the SR image. Experimental results show that over-all SSIM index for the EBSR is higher than RBSR and thus preserves the image quality better compared to the RBSR. In an evaluation of EBSR and RBSR for feature based detection scenario, it is observed that face detection in the EBSR resultant image has the same performance compared to that of the Viola-Jones approach [21] without SR; therefore, we do not gain any improvement in EBSR. In the case of RBSR, due to the registration errors, the over-all face detection performance after SR by the Viol-Jones algorithm is reduced by 3%. As such, it is an extension of the author's conference work [12].

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“…It is to track an object through a video sequence by extracting the template in the first frame and finding the region which matches the chosen template as closely as possible in the following frames. Based on the affine image alignment (AIA) algorithm, template tracking has been extended in a variety of ways, which include: (1) allowing for arbitrary parametric transformations of the template (Bergen et al, 1992); (2) allowing for linear appearance variation (Black and Jepson, 1998;Hager and Belhumeur, 1998); and (3) dealing with special cases, such as occlusion and containing background pixels (Ishikawa et al, 2002). Based on the combination of these extensions, some non-rigid appearance models for template tracking are proposed, such as active appearance models (AAM) (Cootes et al, 2001;Gross et al, 2006) and active blobs (Sclaroff and Isidoro, 1998).…”

Section: Introductionmentioning

confidence: 99%