Přednášet bude Prof. Heikki Kälviäinen, který vede Laboratoř počítačového vidění a rozpoznávání obrazů na Katedře výpočetního inženýrství při Lappeenranta-Lahti univerzitě technologie ve Finsku. Ve svém výzkumu se zabývá problémy počítačového a strojového vidění, rozpoznáváním obrazů a strojovým učením.
Prof. Kälviäinen si pro nás připravil velmi zajímavou přednášku kombinující řadu výzkumných oblastí umělé inteligence. V prvé části, určené hlavně těm, kteří mají zájem se seznámit s principy AI, uvede základní přístupy ke zpracování digitálních obrazů a analýzy strojového učení. Druhou část věnuje vybraným ukázkovým aplikacím strojového vidění: automatizovaném rozpoznávání jedinců Saima tuleňů, hodnocení planktonu Baltického moře, a predikci kvality budoucích prken na základě pozorování vnějšího povrchu kmenů.
Title: Computer vision using machine learning: challenges and solutions
Abstract:
The presentation considers computer vision, especially a point of view of machine learning applications. Digital image processing and analysis with machine learning methods enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with image acquisition, data annotation with expert knowledge, and clustering and classification, including deep learning method training are discussed. Different applications are given as examples based on the fresh novel data available: planktons in the Baltic Sea, Saimaa ringed seals in Lake Saimaa, and logs in the sawmill industry. In the first application the motivation is that distributions of plankton types give much information about the condition of the sea water system, e.g., about the climate change. An imaging flow cytometer can produce a lot of plankton images which should be classified into different plankton types. Manual classification of these images is very laborious, and thus, a CNN-based method has been developed to automatically recognize the plankton types in the Baltic Sea. In the second application the Saimaa ringed seals are automatically identified individually using camera trap images for assisting this very small population to survive in nature. CNN-based re-identification methods are based on pelage patterns of the seals. The third application is related to the sawmill industry. The digitalization of the sawmill industry is important for optimizing material flows and the quality. The research is focused on seeing inside the log to be able to predict which kinds of sawn boards are produced after cutting the log.
Více informací viz http://www2.it.lut.fi/cvprl/visual
Title: Computer vision using machine learning: challenges and solutions
Abstract:
The presentation considers computer vision, especially a point of view of machine learning applications. Digital image processing and analysis with machine learning methods enable efficient solutions for various areas of useful data-centric engineering applications. Challenges with image acquisition, data annotation with expert knowledge, and clustering and classification, including deep learning method training are discussed. Different applications are given as examples based on the fresh novel data available: planktons in the Baltic Sea, Saimaa ringed seals in Lake Saimaa, and logs in the sawmill industry. In the first application the motivation is that distributions of plankton types give much information about the condition of the sea water system, e.g., about the climate change. An imaging flow cytometer can produce a lot of plankton images which should be classified into different plankton types. Manual classification of these images is very laborious, and thus, a CNN-based method has been developed to automatically recognize the plankton types in the Baltic Sea. In the second application the Saimaa ringed seals are automatically identified individually using camera trap images for assisting this very small population to survive in nature. CNN-based re-identification methods are based on pelage patterns of the seals. The third application is related to the sawmill industry. The digitalization of the sawmill industry is important for optimizing material flows and the quality. The research is focused on seeing inside the log to be able to predict which kinds of sawn boards are produced after cutting the log.
Více informací viz http://www2.it.lut.fi/cvprl/visual