Automatic mosaicing is an important image processing application and we propose several improvements and simplifications to the image registration pipeline used in microscopy to automatically construct large images of whole specimen samples from a series of images. First of all we propose a feature descriptor based on the amplitude of a few elements of the Fourier transform, which makes it fast to compute and that can be used for any image matching and registration applications where scale and rotation invariance is not needed. Secondly, we propose a cascade matching approach that will reduce the time for the nearest neighbour search considerably, making it almost independent on feature vector length. Moreover, several improvements are proposed that will speed up the whole matching process. These are: faster interest point detection, a regular sampling strategy and a deterministic false positive removal procedure that finds the transformation. All steps of the improved pipeline are explained and the results comparative experiments are presented.
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Anders Hast was born in 1966. In 1996 he graduated from the University of Gävle and obtained the Bachelor of Science in Computer Science degree. In 2004 he defended the PhD thesis on topic “Improved Algorithms for Fast Shading and Lighting” in the Centre for Image Analysis at the Uppsala University. Besides computer graphics and mathematics, also parallel programming and visualisation were important parts of his PhD studies. For nine years he was an application expert in scientific visualisation at UPPMAX. In 2011 he spent one year at IIT, CNR, Pisa in Italy as an ERCIM fellow and after that he received a full time position as associate professor at Uppsala University (e-mail: firstname.lastname@example.org). Since then the research has focused on computer vision and image processing, especially for cultural heritage applications. He published 15 journal papers and 18 short papers, took park in 29 conferences and wrote 11 book chapters. He is a member of the Swedish Society for automated image analysis, the International Association for Pattern Recognition and also a member of Eurographics.
Victoria Alexandrovna Sablina was born in 1983. In 2006 she graduated with honors from the Ryazan State Radio Engineering Academy. In 2009 she defended the thesis on topic “The Development and the Investigation of Image Restoration Algorithms by the Sequency Theory Methods” in specialty “System Analysis, Control and Information Processing (in Engineering Systems)” and obtained the degree of a Candidate of Engineering Sciences. At present, she works at the Electronic computers department of the Ryazan State Radio Engineering University as an Associate Professor (e-mail: email@example.com). The area of her scientific interests includes computer vision systems, mathematical image processing methods, threevalued logic. After defending her thesis she continued to research image processing and analysis algorithms by the sequency analysis methods. At present, she does research in image matching and image superimposition in computer vision systems by the multiple view geometry methods. In all 69 scientific works were published and of them there are 56 papers (2 papers in the international peer-review journals and 19 papers in international conference proceedings) and 1 monograph “Image Processing in the Aviation Computer Vision Systems”. She is a member of the International Society for Optics and Photonics (SPIE).
Ida-Maria Sintorn was born in 1976. In 2000 she graduated from the Uppsala University and obtained the MSc degree in Molecular Biotechnology Engineering. In 2005 she also obtained the PhD degree in computerized image analysis and remote sensing at the Swedish University of Agricultural Science. Since 2012 Ida-Maria has a Docent degree from Uppsala University. She works at the Division of Visual Information and Interaction of the Uppsala University as an Associate Professor (e-mail: firstname.lastname@example.org). Her fields of research are segmentation, texture analysis, automated electron and fluorescence microscopy. She published 16 journal papers and took part in 23 conferences. She is a member of the Swedish Society for Automated Image Analysis and the International Association for Pattern Recognition.
Bengt Gustaf Kylberg was born in 1983. In 2008 he obtained the MSc degree at the Uppsala University. In 2014 he also obtained the PhD degree in at the Centre for Image Analysis at the Uppsala University. At present, he works at the Vironova AB company in Stockholm at the position of the Technical Product Owner (e-mail: gustaf.kylberg@vironova. com). His fields of research are automation within electron microscopy, object segmentation, description and classification. He has 12 scientific publications. He is a member of the Swedish Society for Automated Image Analysis and the International Association for Pattern Recognition.
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Hast, A., Sablina, V.A., Sintorn, IM. et al. A Fast Fourier based Feature Descriptor and a Cascade Nearest Neighbour Search with an Efficient Matching Pipeline for Mosaicing of Microscopy Images. Pattern Recognit. Image Anal. 28, 261–272 (2018). https://doi.org/10.1134/S1054661818020050
- Image mosaicing
- matching pipeline
- feature descriptor
- microscopy images
- nearest neighbour search
- Fourier transform
- interest points