An Approach for Segmentation of Colored Images with Seeded Spatial Enhancement

In the image analysis, image segmentation is the operation that divides image into set of different segments. The work deals about common color image segmentation techniques and methods. Image enhancement is done using four connected approach for seed selection of the image. An algorithm is implemented on the basis of manual seed selection. It select a seed point in an image an then check for its four neighbor pixels connected to that particular seed point. And segment that image in foreground and background framing. At the end, the evaluation criterion will be introduced and applied on the algorithms results. Five most used image segmentation algorithms, namely, efficient graph based, K means, Mean shift, Expectation maximization and hybrid method are compared with implemented algorithm

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Automatische bildbasierte Segmentierung organischer Objekte einer gleichartigen Gruppe: Abgeleitet vom Problem der Stammschnittflächensegmentierung

Diese Arbeit adressiert die automatische bildbasierte Segmentierung von organo-Gruppen, Gruppen gleichartiger organischer Objekte. Die Segmentierung einer organo-Gruppe ermöglicht Anwendungen zur automatischen Vermessung, Inspektion oder Sortierung. In dieser Arbeit werden, ausgehend vom Problem der Stammschnittflächen, drei Segmentierungskonzepte entwickelt und quantitativ evaluiert. Ausgehend von den Konzepten wird eine allgemeinere Lösung für organo-Gruppen entwickelt und am Beispiel von Plattfischen, Kartoffeln und Äpfeln evaluiert, wobei gute bis sehr gute Ergebnisse erzielt werden