Key frames selection into panoramic mosaics

Fadaeieslam, Mohammad Javad; Soryani, Mohsen

doi:10.1109/icics.2009.5397678

Cited by 6 publications

(2 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We use the Singer model for corners' motion modeling. 34,35 In the previous work, 36 we used a Wiener process acceleration model for this purpose. The Singer model assumes that the target acceleration is a zero-mean first-order stationary Markov process, whereas the Wiener model is referred to as a nearly constant acceleration model.…”

Section: Kalman Filtermentioning

confidence: 99%

Efficient key frames selection for panorama generation from video

Fadaeieslam

Soryani

2011

J. Electron. Imaging

Self Cite

View full text Add to dashboard Cite

A video sequence consists of several hundred frames, and as a result, creating a panoramic image from these frames is a very time-consuming process. Consecutive frames have large overlap areas that do not provide much information. Therefore, some key frames must be extracted for better performance. There are a number of methods for key-frame selection that match all frames in a video sequence. We present a novel and efficient method to select key frames from video for creating a large panoramic mosaic without matching all frames. Consecutive frames are transformed and projected onto the common mosaic surface and the position of each corner of the next frame is predicted with a distinct Kalman filter on this surface. The overlap area between each predicted frame and its previous key frame is used as the criterion to select the next key frame. This method uses video information to reduce features and align frames with repeated structures more accurately. We show that this approach is an efficient preprocessing step and substantially reduces the time required to construct panorama from a video sequence. © 2011 SPIE and IS&T.

show abstract

Section: Kalman Filtermentioning

confidence: 99%

Efficient key frames selection for panorama generation from video

Fadaeieslam

Soryani

2011

J. Electron. Imaging

Self Cite

View full text Add to dashboard Cite

show abstract

“…Some recent studies have used image-to-mosaic registration [20][21][22] to discard some images and select key frames [23][24][25] with the aim of real-time mosaicing (known as online mosaicing). These approaches are well suited to create panoramic mosaics from a video sequence in cases where the images have large overlaps, especially cases where camera translation is not allowed.…”

Section: Related Work On Feature-based Image Mosaicingmentioning

confidence: 99%

Graph theory approach for match reduction in image mosaicing

et al. 2014

View full text Add to dashboard Cite

One of the crucial steps in image mosaicing is global alignment, which requires finding the best image registration parameters by employing nonlinear minimization methods over correspondences between overlapping image pairs for a dataset. Based on graph theory, we propose a simple but efficient method to reduce the number of overlapping image pairs without any noticeable effect on the final mosaic quality. This reduction significantly lowers the computational cost of the image mosaicing process. The proposed method can be applied in a topology estimation process to reduce the number of image matching attempts. The method has been validated through experiments on challenging underwater image sequences obtained during sea trials with different unmanned underwater vehicles.

show abstract