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Active partition based medical image understanding with self-organised competitive spatch eduction
(Wydawnictwo Politechniki Łódzkiej, 2010) Pryczek, Michał; Tomczyk, Arkadiusz; Szczepaniak, Piotr
Medical Image Understanding is a recently defined semantic oriented image recognition task. Its specific requirements, highlighting complex characteristics of recognised objects as well as indispensable use of human-level expert knowledge almost every step of data processing sets new requirements for implemented algorithms. This paper focuses on linguistic image description method, designed to segment low level, semantically coherent image regions and mine adjacency relations among them. Example method results on medical images are presented to specify some methods properties.
Geometric Transformations Embedded into Convolutional Neural Networks
(Wydawnictwo Politechniki Łódzkiej, 2016) Tarasiuk, Paweł; Pryczek, Michał
This paper presents a novel extension to convolutional neural networks. While CNNs are known for invariance to object translation, changes to the other parameters could make the image recognition tasks diffcult – that includes rotations and scaling. Some improvement in this area could be achieved with embedded geometric transformations used inside the CNNs. In order to provide a practical solution, which allows fast propagation and learning of the modified networks, “fast geometric transformations” are introduced.
A Review on Point Cloud Semantic Segmentation Methods
(Wydawnictwo Politechniki Łódzkiej, 2018) Lazarek, Jagoda; Pryczek, Michał
Semantic segmentation of 3D point clouds is an open research problem and remains crucial for autonomous driving, robot navigation, human-computer interaction, 3D reconstruction and many others. The large scale of the data and lack of regular data organization make it a very complex task. Research in this ﬁeld focuses on point cloud representation (e.g., 2D images, 3D voxels grid, graph) and segmentation techniques. In the paper, state-of-the-art approaches related to these tasks are presented.
Spatch based active partitions with linguistically formulated energy
(Wydawnictwo Politechniki Łódzkiej, 2010) Tomczyk, Arkadiusz; Pryczek, Michał; Walczak, Stanisław; Jojczyk, Konrad; Szczepaniak, Piotr
The present paper shows the method of cognitive hierarchical active partitions that can be applied to creation of automatic image understanding systems. The approach, which stems from active contours techniques, allows one to use not only the knowledge contained in an image, but also any additional expert knowledge. Special emphasis is put on the effcient way of knowledge retrieval, which could minimise the necessity to render information expressed in a natural language into a description convenient for recognition algorithms and machine learning.