Tonatiuh Tecuan | (Benemérita) Universidad Autónoma de Puebla (original) (raw)
Related Authors
Government College of Engineering,Salem
Uploads
Papers by Tonatiuh Tecuan
This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms ... more This tutorial reviews image alignment and image stitching algorithms. Image alignment
algorithms can discover the correspondence relationships among images with
varying degrees of overlap. They are ideally suited for applications such as video
stabilization, summarization, and the creation of panoramic mosaics. Image stitching
algorithms take the alignment estimates produced by such registration algorithms
and blend the images in a seamless manner, taking care to deal with potential problems
such as blurring or ghosting caused by parallax and scene movement as well as
varying image exposures. This tutorial reviews the basic motion models underlying
alignment and stitching algorithms, describes effective direct (pixel-based) and feature-
based alignment algorithms, and describes blending algorithms used to produce
seamless mosaics. It closes with a discussion of open research problems in the area.
This paper covers an implementation of fully automated HDR panorama stitching algorithm. This pro... more This paper covers an implementation of fully automated
HDR panorama stitching algorithm. This problem involves
image recognition as we need to know which part of
panorama to stitch. We used the SIFT algorithm for recognition
of the corresponding points; this method is invariant
to changes in image scaling, rotation and in illumination.
The SIFT algorithm was modified for work with HDR images.
Perspective transformations were used to set up new
positions of images in the panorama and normalization of
luminance was made in order to remove seams from pictures.
Results of implementation of the HDR panorama
stitching algorithm are presented and discussed.
Keywords: HDR panoramas, HDR images, SIFT, local
features, perspective transformations.
Image stitching is used to combine several individual images having some overlap into a composite... more Image stitching is used to combine several individual images having
some overlap into a composite image. The quality of image stitching is measured
by the similarity of the stitched image to each of the input images, and by the
visibility of the seam between the stitched images.
In order to define and get the best possible stitching, we introduce several formal
cost functions for the evaluation of the quality of stitching. In these cost functions,
the similarity to the input images and the visibility of the seam are defined in
the gradient domain, minimizing the disturbing edges along the seam. A good
image stitching will optimize these cost functions, overcoming both photometric
inconsistencies and geometric misalignments between the stitched images.
This approach is demonstrated in the generation of panoramic images and in object
blending. Comparisons with existing methods show the benefits of optimizing
the measures in the gradient domain.
This tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms ... more This tutorial reviews image alignment and image stitching algorithms. Image alignment
algorithms can discover the correspondence relationships among images with
varying degrees of overlap. They are ideally suited for applications such as video
stabilization, summarization, and the creation of panoramic mosaics. Image stitching
algorithms take the alignment estimates produced by such registration algorithms
and blend the images in a seamless manner, taking care to deal with potential problems
such as blurring or ghosting caused by parallax and scene movement as well as
varying image exposures. This tutorial reviews the basic motion models underlying
alignment and stitching algorithms, describes effective direct (pixel-based) and feature-
based alignment algorithms, and describes blending algorithms used to produce
seamless mosaics. It closes with a discussion of open research problems in the area.
This paper covers an implementation of fully automated HDR panorama stitching algorithm. This pro... more This paper covers an implementation of fully automated
HDR panorama stitching algorithm. This problem involves
image recognition as we need to know which part of
panorama to stitch. We used the SIFT algorithm for recognition
of the corresponding points; this method is invariant
to changes in image scaling, rotation and in illumination.
The SIFT algorithm was modified for work with HDR images.
Perspective transformations were used to set up new
positions of images in the panorama and normalization of
luminance was made in order to remove seams from pictures.
Results of implementation of the HDR panorama
stitching algorithm are presented and discussed.
Keywords: HDR panoramas, HDR images, SIFT, local
features, perspective transformations.
Image stitching is used to combine several individual images having some overlap into a composite... more Image stitching is used to combine several individual images having
some overlap into a composite image. The quality of image stitching is measured
by the similarity of the stitched image to each of the input images, and by the
visibility of the seam between the stitched images.
In order to define and get the best possible stitching, we introduce several formal
cost functions for the evaluation of the quality of stitching. In these cost functions,
the similarity to the input images and the visibility of the seam are defined in
the gradient domain, minimizing the disturbing edges along the seam. A good
image stitching will optimize these cost functions, overcoming both photometric
inconsistencies and geometric misalignments between the stitched images.
This approach is demonstrated in the generation of panoramic images and in object
blending. Comparisons with existing methods show the benefits of optimizing
the measures in the gradient domain.