Wyniki wyszukiwania - BazTech

1

Iceberg detection and tracking using two-level feature extraction methodology on Antarctica Ocean

Krishnan Rajakumar, Thangavelu Arunkumar, Panneer Prabhavathy, Devulapalli Sudheer, Misra Arundhati, Putrevu Deepak

Acta Geophysica

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2022

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Vol. 70, no 6

2953--2963

EN

This paper aims to develop an automatic feature extraction system for detecting icebergs in Antarctica. Extracting suitable features to discriminate an iceberg from sea ice and land melting based on its content is tedious. Especially in Synthetic Aperture Radar data, high image content is highly affected by speckle noise. Establishing the appropriate spatial relationship between pixels is not producing much accuracy with the standard low-level features. The proposed method introduces the two-level iceberg detection and tracking algorithm. The available samples were used to train the first-level convolution neural network-based features. False-positive predictions have been removed using the multiscale contourlet-based Haralick texture features in the second level. The final detected iceberg movement has been tracked using the temporal image data. The distance moved in both temporal images is computed with the help of latitude and longitude information. The proposed methodology exhibited the best performance over state-of-the-art methods and acquired 79.1% precision and 83.8 F1 score.

2

Automated and effective content-based mammogram retrieval using wavelet based CS-LBP feature and self-organizing map

Singh V. P., Srivastava R.

Biocybernetics and Biomedical Engineering

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2018

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Vol. 38, no. 1

90--105

EN

In this paper, automated, fast and effective content based-mammogram image retrieval system is proposed. The proposed pre-processing steps include automatic labelling-scratches suppression, automatic pectoral muscle removal and image enhancement. Further, for segmentation selective thresholds based seeded region growing algorithm is introduced. Furthermore, we apply 2-level discrete wavelet transform (DWT) on the segmented region and wavelet based centre symmetric-local binary pattern (WCS-LBP) features are extracted. Then, extracted features are fed to self-organizing map (SOM) which generates clusters of images, having similar visual content. SOM produces different clusters with their centres and query image features are matched with all cluster representatives to find closest cluster. Finally, images are retrieved from this closest cluster using Euclidean distance similarity measure. So, at the searching time the query image is searched only in small subset depending upon cluster size and is not compared with all the images in the database, reflects a superior response time with good retrieval performances. Descriptive experimental and empirical discussions confirm the effectiveness of this paper.

3

Varish N., Kumar S., Pal A. K.

Fundamenta Informaticae

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2017

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Vol. 156, nr 2

209--235

EN

Content-based image retrieval (CBIR) scheme has gained popularity in the field of information retrieval for retrieving some relevant images from the image database based on the visual descriptors such as color, texture and/or shape of a given query image. In this paper, color features have been exploited from each color component of an RGB color image by using multiresolution approach since most of the information of an image is undetected at one resolution level while some other undetectable information is visualized in other multi-resolution levels. Initially, Gaussian image pyramid is employed on each color component of the color image and subsequent DCT is computed directly on the obtained multi-resolution image planes. Then some significant DCT coefficients are selected according to the zigzag scanning order. For formation of the feature vector, we have derived some statistical values from AC coefficients and all other DC coefficients are included entirely. Finally, a similarity measure is suggested during image retrieval process and it is found that the overall computation overhead is reduced due to consideration of the proposed similarity measure. The proposed CBIR scheme is validated on a two standard Corel-1K and GHIM-10K image databases and satisfactory results are achieved in terms of precision, recall and F-score. The retrieved results show that the proposed scheme outperforms significantly over other related CBIR schemes.

4

New image descriptor from edge detector and blob extractor

Grycuk R., Scherer R., Gabryel M.

Journal of Applied Mathematics and Computational Mechanics

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2015

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Vol. 14, nr 4

31--39

EN

In this paper we present a novel approach for image description. The method is based on two well-known algorithms: edge detection and blob extraction. In the edge detection step we use the Canny detector. Our method provides a mathematical description of each object in the input image. On the output of the presented algorithm we obtain a histogram, which can be used in various fields of computer vision. In this paper we applied it in the content-based image retrieval system. The simulations proved the effectiveness of our method.

5

Image Retrieval Based on Text and Visual Content Using Neural Networks

Castro D. A., Seijas L. M.

Journal of Applied Computer Science Methods

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2010

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Vol. 2 No. 1

21--39

EN

In the last few years there has been a dramatic increase in the amount of visual data to be searched and retrieved. Typically, images are described by their textual content (TBIR) or by their visual features (CBIR). However, these approaches still present many problems. The hybrid approach was recently introduced, combining both characteristics to improve the benefits of using text and visual content separately. In this work we examine the use of the Self Organizing Maps for content-based image indexing and retrieval. We propose a scoring function which eliminates irrelevant images from the results and we also introduce a SOM variant (ParBSOM) that reduces training and retrieval times. The application of these techniques to the hybrid approach improved computational results.

6

Reprezentacja kształtu 3D oparta na deskryptorze EGI na potrzeby indeksowania

Frejlichowski D.

Metody Informatyki Stosowanej

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2009

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nr 3 (20)

83-89

PL

W niniejszym artykule przedstawiono wyniki wstępnych badań nad problemem opisu kształtów trójwymiarowych z użyciem deskryptora EGI (Extended Gaussian Image). Jest to jeden z popularniejszych algorytmów, którego największą zaletą jest intuicyjność i skuteczność w podstawowych zastosowaniach. W artykule skoncentrowano się na ozpoznawaniu kształtów w kontekście indeksowania tego typu obiektów w multimedialnych bazach danych (CBIR – Content Based Image Retrieval).

EN

The paper presents experimental results on three dimensional shape description and recognition using EGI (Extended Gaussian Image) 3D shape desciptor. The method uses the Gaussian image, which is obtained through mapping of areas and normal vectors for all polygons in an object into the Gaussian sphere. The descriptor is invariant to translation of an object in a scene. The algorithm will be applied to the problem of CBIR (Content Based Image Retrieval). More precisely, the 3D model retrieval is explored.