The Data Exploration System for Image Processing Based on Server-Side Operations

Ładniak, Magdalena; Piórkowski, Adam; Młynarczuk, M.

doi:10.1007/978-3-642-40925-7_17

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Such the approach, presented in Fig. 3, is similar to the proposal presented in [25,29] and allows us to operate in the entire PACS database without data transferring. Such organization reduces both the size of the data stored in the memory and CPU load.…”

Section: Methods Of Review and Evaluation Of Ct Datamentioning

confidence: 92%

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Automatized fuzzy evaluation of CT scan heart slices for creating 3D/4D heart model

Bielecka

Piórkowski

2015

Applied Soft Computing

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Section: Methods Of Review and Evaluation Of Ct Datamentioning

confidence: 92%

“…Thus, the fast automatic analysis of set quality is required. The used approach is based on the method proposed for rock images analysis introduced in [25,29]. Similarly, the analyzed CT images are stored in PACS databases.…”

Section: Computed Tomography Imagesmentioning

confidence: 99%

Automatized fuzzy evaluation of CT scan heart slices for creating 3D/4D heart model

Bielecka

Piórkowski

2015

Applied Soft Computing

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“…Verification of the usability of the proposed approach for a wider set of data as well as comparison with state-of-art methods based on genetic algorithms remain open issues for further research. Another interesting topic for future studies is to apply the proposed method in a data exploration system for CA-based image processing [7,11].…”

Section: Identification Of Probabilistic Cellular Automatamentioning

confidence: 99%

Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach

Płaczek

2014

Parallel Processing and Applied Mathematics

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Abstract. In this paper a method is proposed which uses data mining techniques based on rough sets theory to select neighborhood and determine update rule for cellular automata (CA). According to the proposed approach, neighborhood is detected by reducts calculations and a rulelearning algorithm is applied to induce a set of decision rules that define the evolution of CA. Experiments were performed with use of synthetic as well as real-world data sets. The results show that the introduced method allows identification of both deterministic and probabilistic CAbased models of real-world phenomena.

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“…A recent application of similar imaging methods includes the novel technology "the Fingerprint of Things", which is based on recording characteristic images of materials within databases and, subsequently, identifying the particular materials by comparing new images with visual databases [4,5]. Such innovations affect the development of methods for image and video storage and exploration [6][7][8][9][10]. Geological image content is difficult to automatically standardize due to the irregular distribution of grains relating to colour and texture.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Local Matching Methods in Image Analysis for Mineral Grain Tracking in Microscope Images of Rock Sections

Habrat

Młynarczuk

2018

Minerals

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Modern geological techniques have resulted in vast and growing databases of digital images and video sequences of rocks, which are available for the use of researchers. The number of database images continues to increase exponentially, creating a need for techniques that will enable the automation of data set management. Desired techniques include query by image, a topic that has been extensively elaborated on in the literature recently. Unfortunately, using such techniques in the geological sciences has been very sporadic and insufficient. This paper presents the evaluation of characteristic local features within rock images for tracking objects on images or video sequences. It also discusses the possibilities for using selected local feature descriptors for content-based image retrieval (CBIR) in the area of geological sciences. The evaluation was performed for the Speeded Up Robust Features (SURF), Binary Robust Invariant Scalable Keypoints (BRISK), Harris-Stephens Algorithm (HSA), Minimum Eigenvalue Algorithm (MEA), and Features from Accelerated Segment Test algorithm (FAST) methods, which are widely known and appreciated in the computer vision field. These methods were analysed for their application to microscopic images of rocks. Five functional cases of geological grain tracking were investigated, based on a selected non-transformed query image, as well as a computer-rotated, acquisitive-rotated, computer-magnified, and an acquisitive-magnified query image. The results demonstrated that these methods can be successfully used for geological applications.

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The Data Exploration System for Image Processing Based on Server-Side Operations

Cited by 6 publications

References 7 publications

Automatized fuzzy evaluation of CT scan heart slices for creating 3D/4D heart model

Automatized fuzzy evaluation of CT scan heart slices for creating 3D/4D heart model

Neighborhood Selection and Rules Identification for Cellular Automata: A Rough Sets Approach

Evaluation of Local Matching Methods in Image Analysis for Mineral Grain Tracking in Microscope Images of Rock Sections

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