Improved Digital Image Segmentation Based on Stereo Vision and Mean Shift Algorithm

Grycuk, Rafał; Gabryel, Marcin; Korytkowski, Marcin; Romanowski, Jakub; Scherer, Rafał

doi:10.1007/978-3-642-55224-3_41

Cited by 14 publications

(4 citation statements)

References 14 publications

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“…It was presented for the first time in [10], but currently is used in various systems e.g. image recognition, 3D reconstruction, image description, segmentation [11], image analysis [12], content based image retrieval [13], object tracking, image databases [14], and many others. SURF is based on SIFT, and it uses Integral Images instead of DOG (Difference of Gaussian), which allows it to work much faster than SIFT.…”

Section: Surfmentioning

confidence: 99%

Novel visual object descriptor using SURF and clustering algorithms

Grycuk

2016

J. Appl. Math. Comput. Mech.

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Abstract. In this paper we propose a method for object description based on two wellknown clustering algorithms (k-means and mean shift) and the SURF method for keypoints detection. We also perform a comparison of these clustering methods in object description area. Both of these algorithms require one input parameter; k-means (k, number of objects) and mean shift (h, window). Our approach is suitable for images with a non-homogeneous background thus, the algorithm can be used not only on trivial images. In the future we will try to remove non-important keypoints detected by the SURF algorithm. Our method is a part of a larger CBIR system and it is used as a preprocessing stage.

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Section: Surfmentioning

confidence: 99%

Novel visual object descriptor using SURF and clustering algorithms

Grycuk

2016

J. Appl. Math. Comput. Mech.

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“…Concrete Engine -implements user logic based on previous the layer (in this paper we implemented the SURF descriptors for feature extraction and k-means clustering [26] for indexing), 4. CBIR Service (for more about CBIR see [23] [22]) -which is WCF service that allows to invoke engine methods as Service Oriented Applications (SOA). 5.…”

Section: Proposed Architecture For Storing Visual Datamentioning

confidence: 99%

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

Grycuk

Gabryel

Scherer

et al. 2015

Artificial Intelligence and Soft Computing

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In this paper we present a novel architecture for storing visual data. Effective storing, browsing and searching collections of images is one of the most important challenges of computer science. The design of architecture for storing such data requires a set of tools and frameworks such as SQL database management systems and service-oriented frameworks. The proposed solution is based on a multi-layer architecture, which allows to replace any component without recompilation of other components. The approach contains five components, i.e. Model, Base Engine, Concrete Engine, CBIR service and Presentation. They were based on two well-known design patterns: Dependency Injection and Inverse of Control. For experimental purposes we implemented the SURF local interest point detector as a feature extractor and K-means clustering as indexer. The presented architecture is intended for content-based retrieval systems simulation purposes as well as for real-world CBIR tasks.

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“…Describing image by set of features is a common approach in computer vision [15], [18], [19], [13], [9]. The feature vector is constructed to obtain the relevant information from the image data.…”

Section: Proposed Feature Descriptormentioning

confidence: 99%

Image Segmentation in Liquid Argon Time Projection Chamber Detector

Płoński

Stefan

Sulej

et al. 2015

Artificial Intelligence and Soft Computing

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Abstract. The Liquid Argon Time Projection Chamber (LAr-TPC) detectors provide excellent imaging and particle identification ability for studying neutrinos. An efficient and automatic reconstruction procedures are required to exploit potential of this imaging technology. Herein, a novel method for segmentation of images from LAr-TPC detectors is presented. The proposed approach computes a feature descriptor for each pixel in the image, which characterizes amplitude distribution in pixel and its neighbourhood. The supervised classifier is employed to distinguish between pixels representing particle's track and noise. The classifier is trained and evaluated on the hand-labeled dataset. The proposed approach can be a preprocessing step for reconstructing algorithms working directly on detector images.

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Improved Digital Image Segmentation Based on Stereo Vision and Mean Shift Algorithm

Cited by 14 publications

References 14 publications

Novel visual object descriptor using SURF and clustering algorithms

Novel visual object descriptor using SURF and clustering algorithms

Multi-layer Architecture For Storing Visual Data Based on WCF and Microsoft SQL Server Database

Image Segmentation in Liquid Argon Time Projection Chamber Detector

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