Image mosaicing using voronoi diagram

Laraqui, Abderrahmane; Baataoui, Aziz; Saaidi, Abderrahim; Jarrar, A.; Masrar, Med.; Satori, Khalid

doi:10.1007/s11042-016-3478-z

Cited by 24 publications

(12 citation statements)

References 35 publications

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“…Affine-SIFT (ASIFT) algorithm [18] achieves the full affine performance and four parameters invariants of an affine transform of SIFT by simulating zooms out and normalizing translation and rotation, but the algorithm complexity is more than twice that of the original SIFT algorithm. Literature [19] constrains image matching by Voronoi diagram regions to implement fast and accurate image stitching using the SIFT algorithm. In general, the performance and complexity of these feature-based image registration methods are not satisfactory at the same time.…”

Section: Related Workmentioning

confidence: 99%

Sequence Image Registration for Large Depth of Microscopic Focus Stacking

Shi

et al. 2020

IEEE Access

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The large depth of images for microscopic measurement can be achieved by using focus stacking techniques with a small depth of field of objective lens. It is implemented by fusing the image sequences of short depth images. However, the non-linear movement of the objective imaging system or the measured object caused by the moving stage straightness error brings the misalignment of the image sequences, such as transversal translation, rotation, and tilting. All of these interferences, as well as the image brightness variation must be corrected by image registration before fusing the image sequences. In this paper, a fast-automatic registration method based on the scale invariant feature transform (SIFT) is proposed. It is achieved by firstly segmenting the focal regions of the image sequences through fast edge detection. Then the image features are extracted within the small segmented focal areas. It greatly reduces the computational cost of feature extraction and the following steps of image correction, and alignment. In the process, the random sampling consistency (RANSAC) algorithm is also used to remove the mistake features. The Laplacian pyramid method is adopted for the large depth of image fusion. The experimental results show that the proposed method is more efficient than the traditional SIFT algorithm. Its registration efficiency is improved by about 60%. This method facilitates the high-precision and real-time imaging of a monocular three-dimensional focus stacking.

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Section: Related Workmentioning

confidence: 99%

Sequence Image Registration for Large Depth of Microscopic Focus Stacking

Shi

et al. 2020

IEEE Access

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“…Laraqui yaptığı çalıĢmada özellik çıkarma ve eĢleĢtirme iĢlemini SIFT algoritmasını kullanarak gerçekleĢtirmiĢtir. Laraqui'nin çalıĢmasında farklı olarak hatalı eĢleĢmelerin elenmesinde voronoi diyagramından yararlanılmıĢtır [13].…”

Section: Birleştirilecek Görüntüler Görüntüleri çıKarılan öZellikler unclassified

Optimizasyon Tabanlı Adaptif Görüntü Mozaikleme Algoritması

Karaköse

Yetiş

Müezzinoğlu

2017

Bilişim Teknolojileri Dergisi

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Özet-Görüntü iĢleme, dijital ortamdaki görüntüler üzerinde bir amaç doğrultusunda gerekli analizler veya değiĢikliklerin yapılmasına yönelik çalıĢmaları kapsamaktadır. Son zamanlarda görüntü iĢleme tekniklerinden en çok kullanılanlardan biri de ortak sahneleri içeren resimler üzerinde iĢlem yapan ve bunları çakıĢtırarak birleĢtiren görüntü mozaikleme iĢlemidir. Bu çalıĢmada kendi kameramızdan elde ettiğimiz görüntüler üzerinde MATLAB ortamında görüntü mozaikleme iĢlemi gerçekleĢtirilmiĢtir. Görüntü mozaikleme için, özellik tabanlı görüntü mozaiklemenin temel adımları olan anahtar noktaların tespiti, bu noktaların eĢleĢtirilmesi, hatalı eĢleĢmelerin elenmesi ve homografi tahmini uygulanmıĢtır. Uygulamada özellik çıkarımı için SIFT algoritması seçilmiĢ, elde edilen homografi matrisi yardımı ile gerekli dönüĢüm iĢlemleri uygulanarak görüntü mozaikleme iĢlemi gerçekleĢtirilmiĢtir. Mozaikleme iĢleminin kalitesini artırmak adına optimizasyon yöntemi olan genetik algoritmadan yararlanılmıĢtır. Uygulamada giriĢ görüntü sayıları ve mozaikleme parametreleri farklı seçilerek çeĢitli karĢılaĢtırma iĢlemleri yapılmıĢtır.Anahtar Kelimeler-Genetik algoritma, Görüntü iĢleme, Görüntü mozaikleme, Optimizasyon. Optimization Based Adaptive Image Mosaicing AlgorithmAbstract-Image processing involves studies to make necessary analyzes or changes in an image aimed at digital images. One of the most recent use of image processing techniques is image mosaicking, which processes and combines images containing common scenes. In this study, we performed image mosaicing in the MATLAB environment on the images we acquired from our own camera. The key steps for feature-based image mosaicking which are detection of key points, matching of these points, elimination of mismatches and homography estimation are applied for image mosaicing. In the application, the SIFT algorithm was selected for feature extraction and image transformation was performed with the help of the obtained homography matrix. In order to increase the quality of the mosaic process, genetic algorithm, which is an optimization method, has been used. Various comparisons were performed by taking different input counts and by choosing different image mosaicing parameters.

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“…These mosaics are called static mosaics. Although many algorithms [9–13] and software treat the case of strict scenes, it is not enough in the case of more complex scenes, and as such the failure of the mosaic is unavoidable. Indeed, complex scenes contain objects in motion and creating a mosaic with images that have objects that change either in time or space, leads to the appearance of unwanted artefacts.…”

Section: Introductionmentioning

confidence: 99%

“…There are many methods in recent years that have treated this problem in many ways. Although in static mosaicking, many approaches in [9–13] have achieved great results, these results are still lacking when it comes to dynamic mosaicking.…”

Section: Introductionmentioning

confidence: 99%