Posudky závěrečné kvalifikační práce

Posudek vedoucího

Španěl, Michal

Jako vedoucí práce nemám co vytknout. Pan Šilling proniknul do problematiky, výborně zadání uchopil a komplexně zpracoval, odvedl velké množství svědomité a promyšlené práce.

Dílčí hodnocení
Kritérium	Známka	Body	Slovní hodnocení
Informace k zadání			Zadání vzniklo na základě partnerství FIT s firmou, pro kterou jde o velmi aktuální téma. Zadání vyžadovalo nastudovat nejnovější metody hlubokého učení vhodné pro úlohu zarovnání obrazu, navrhnout kompletní postup sešití většího počtu obrázků a prozkoumat vlastnosti metod založených na hlubokém učení v porovnání s postupy využívajícími klasické detektory klíčových bodů jako SIFT. Jako problematická se ukázala dostupnost vhodných trénovacích a validačních dat. Firma pro testování poskytla pouze dva ukázkové datasety z elektronového mikroskopu, což je při variabilitě dat žalostně málo. Student tak značné úsilí věnoval vytvoření vlastního datasetu. Všechny požadované body zadání byly splněny.
Práce s literaturou			Student strávil hodně času detailní analýzou existujících přístupů a základními experimenty pro ověření jejich vlastností. Vyhledal a prostudoval množství literatury, většinou ve formě publikovaných vědeckých článků.
Aktivita během řešení, konzultace, komunikace			Student byl aktivní a iniciativní. Pravidelné konzultace často sloužily jen pro ujištění, že další navrhovaný postup je vhodný. A nutno podotknout, že byl. Student sám postupoval při řešení velmi promyšleně a systematicky.
Aktivita při dokončování			Práce byla dokončena včas, případné připomínky k technické zprávě byly průběžně zapracovávány.
Publikační činnost, ocenění			Práce byla prezentována na studentské konferenci Excel@FIT.

Navrhovaná známka: A

Body: 100

Posudek oponenta

Beran, Vítězslav

Based on a very careful study of relevant scientific papers, the author has selected suitable existing modern methods for image analysis and adapted them for stitching of images taken by electron microscope. From the existing datasets, he created a new dataset that reflects the needs of the image stitching task in the electron microscopy domain. He has developed a high-quality software solution integrating existing libraries and his own methods, including a number of necessary tools for dataset generation and experiment execution. The technical report, written in English, has an excellent presentation and technical level. The resulting work contains new insights.

Dílčí hodnocení
Kritérium	Body	Slovní hodnocení
Náročnost zadání		Although the assignment does not explicitly require a solution in the electron microscopy domain, the work focuses on this domain. The development of a method for image fusion in a less common domain is novel and may present unexpected problems.
Rozsah splnění požadavků zadání
Rozsah technické zprávy
Prezentační úroveň technické zprávy	85	The technical report has a clear and logical structure and the text follows each other well. The author expresses himself factually, clearly and professionally, the individual parts of the text are well-justified and relevant to the solution of the assignment. The chapter ranges or structure could be slightly revised to make the chapter scope a little more balanced. There are a few minor flaws in the text. In the introduction to the solution proposal (Ch. 6), the term OpenCV is used somewhat inappropriately to refer to a method that computes a transformation between two images. Although the author refers to the literature on the use of metrics to evaluate the quality of stitched images (Ch. 8.2), at least a brief description of these metrics and the essence of these metrics (what they measure and what conclusions can be drawn from these values) is missing. The author mentions the size of the CNN architecture of the LoFTR module for the first time only at the end, which has not been discussed anywhere before. Also, the conclusion about the need for a larger dataset is not well justified and does not make much sense. It can be assumed that more data may slightly improve the quality of the LoFTR network, but it is not clear how it will help to improve the final stitched images, since the author does not know the reason for the worse final results even with this better new architecture. The description of the implemented modules describes their functionality and integration, but not more detailed technical aspects such as the data structures and formats used, the integration of external tools and how to implement their specific use, etc. Despite the minor shortcomings, the overall presentation level of the technical report is very good.
Formální úprava technické zprávy	100	The formal and linguistic level of the technical report is excellent. The text is written in good professional English, is perfectly understandable and free of errors. All formatting is precise and the figures are in relevant formats and are of high quality.
Práce s literaturou	100	The selection of study sources is of high quality, extremely extensive and relevant. The summaries of the different approaches show a careful study of the various articles, and the author summarizes the key aspects of the different approaches in a concise, factual and erudite manner.
Realizační výstup	90	The author has selected appropriate actual methods for solving the sub-steps on the basis of a good study. The selection of these methods is well described, as well as the overall structure of the SW solution. A new dataset is of high quality and relevance. However, the key parts of the solution are not described in sufficient detail either in the design or in the implementation. The specific parameters and the procedure for retraining the CNN architecture of LoFTR on EM data are not known. For the global optimization, where it uses the SLAM method, again neither the theoretical part nor the implementation explains the optimization principle - in short, it somehow optimizes the local transformations to the global objective. The experiments in Ch. 8.3 are presented only by images (Figs. 8.1 and 8.2), where the behaviour of the different methods can be clearly seen, but of course, quantitative results cannot be read from them. Further, it is not clear what the properties of the used datasets are. The experiment on the robustness of different variants of the methods is very relevant and crucial, but the results presented in this way are not very useful. Technically, the software is of very good quality, the source files contain authorship information, are logically structured and well annotated. The solution contains all the necessary tools for working with datasets, retraining the LoFTR model, running experiments, etc.
Využitelnost výsledků		The work brings new insights by applying relevant and modern methods in a new domain. It appropriately selects modern techniques from the field of image stitching and adapts them to the electron microscopy domain. Thus, the partial results of the work can be evaluated as new. The use of the results is still questionable, as experiments have shown improvements in sub-steps, but have not yet been able to improve the overall result over classical methods.

Navrhovaná známka: A

Body: 90

Otázky

Why is the overlap a fixed 20%? Why isn't it random to find out the robustness to the amount of the overlap as well? How does it fit the expected data if Fig. 2.3d shows an overlap of 5%?
What are the transformations between images in EM sensing methods?
The final stitched image using the new method shows slightly worse quality than the conventional methods. You mention in the text that you don't know why yet. Nevertheless, try to briefly present how you explain this and where you would look for the source of the deterioration.