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

Efficient image retrieval by fuzzy rules from boosting and metaheuristic

Korytkowski Marcin, Senkerik Roman, Scherer Magdalena M., Angryk Rafal A., Kordos Miroslaw, Siwocha Agnieszka

Journal of Artificial Intelligence and Soft Computing Research

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2020

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Vol. 10, No. 1

57--69

EN

Fast content-based image retrieval is still a challenge for computer systems. We present a novel method aimed at classifying images by fuzzy rules and local image features. The fuzzy rule base is generated in the first stage by a boosting procedure. Boosting meta-learning is used to find the most representative local features. We briefly explore the utilization of metaheuristic algorithms for the various tasks of fuzzy systems optimization. We also provide a comprehensive description of the current best-performing DISH algorithm, which represents a powerful version of the differential evolution algorithm with effective embedded mechanisms for stronger exploration and preservation of the population diversity, designed for higher dimensional and complex optimization tasks. The algorithm is used to fine-tune the fuzzy rule base. The fuzzy rules can also be used to create a database index to retrieve images similar to the query image fast. The proposed approach is tested on a state-of-the-art image dataset and compared with the bag-of-features image representation model combined with the Support Vector Machine classification. The novel method gives a better classification accuracy, and the time of the training and testing process is significantly shorter.

3

Yarn-Dyed Fabric Image Retrieval Using Colour Moments and the Perceptual Hash Algorithm

Li Zhongjian, Xiang Jun, Wang Lei, Zhang Ning, Pan Ruru, Gao Weidong

Fibres & Textiles in Eastern Europe

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2019

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Vol. 27, no. 5 (137)

39--46

EN

Due to the variety of yarn colours and arrangement, it is a challenging problem to retrieve a yarn-dyed fabric image. In this paper, yarn-dyed fabric samples are captured by the DigiEye system first, and then pattern images of the fabric images captured are simulated by pattern design software based on extracted structure parameters of the yarn-dyed fabric. For the simulated pattern image, an effective algorithm is proposed to retrieve these kinds of images by combining the colour moments method and perceptual hash algorithm. Then the pattern images retrieved are mapped back to the yarn-dyed fabric image so as to realise the yarn-dyed fabric image retrieval. In the algorithm proposed, the colour moments method is adopted to extract the colour features, and the perceptual hash algorithm is utilised to calculate the spatial features of the simulated pattern images. Then the two kinds of image features are used to compute the similarity between the input original image and each target image based on the Euclidean distance and Hamming distance. Relevant images can be retrieved in dependence on the similarity value, which is determined by calculating the optimum weighted value of the colour features’ similarity and spatial features’ similarity. In order to measure the retrieval efficiency of the method proposed, the accuracy rate and retrieval rate of image retrieval were computed in experiments using a PATTERN image database with 300 images. The experimental results show that the average accuracy rate of the method proposed is 85.30% and the retrieval rate - 53.51% when the weighted value of the colour feature similarity is fixed at 0.45 and the spatial feature similarity is 0.55. It is shown that the method presented is effective to retrieve pattern images of yarn-dyed fabric.

PL

Ze względu na różnorodność kolorów i rozmieszczenia przędz otrzymanie obrazu tkaniny wytworzonej z barwionych przędz jest trudnym zadaniem. W artykule próbki tkanin z barwionych przędz były najpierw analizowane przez system DigiEye, a następnie wykonane zostały symulacje obrazów z zastosowaniem oprogramowania do projektowania wzorów oparte na wyodrębnionych parametrach struktury tkaniny. W przypadku symulacji obrazu wzoru zaproponowano skuteczny algorytm do odzyskiwania tego rodzaju obrazów poprzez połączenie metody momentów koloru i percepcyjnego algorytmu z mieszaniem. W zaproponowanym algorytmie do wyodrębniania cech kolorów zastosowano metodę momentów barwnych, a do obliczenia cech przestrzennych symulowanych obrazów został wykorzystywany percepcyjny algorytm mieszania. Następnie użyto dwóch rodzajów cech obrazu do obliczenia podobieństwa między oryginalnym obrazem wejściowym a każdym obrazem docelowym w oparciu o odległość euklidesową i odległość Hamminga. Odpowiednie obrazy można odzyskać w zależności od wartości podobieństwa, która jest określana przez obliczenie optymalnej ważonej wartości podobieństwa cech koloru i podobieństwa cech przestrzennych. Aby zmierzyć skuteczność proponowanej metody w eksperymentach obliczono wskaźnik dokładności i szybkość pobierania obrazów, wykorzystując bazę danych obrazów PATTERN z 300 obrazami. Wyniki eksperymentalne pokazały, że średni współczynnik dokładności proponowanej metody wynosi 85,30%, a szybkość pobierania 53,51%, wartość podobieństwa cech kolorów wynosiła 0,45, a podobieństwo cech przestrzennych wynosiło 0,55. Wykazano, że prezentowana metoda jest skuteczna w przypadku otrzymywania obrazów wzorów tkanin z przędz barwionych.

4

Reconstruction of high-dimensional data using the method of probabilistic features combination

Jakóbczak D. J.

Zeszyty Naukowe Wydziału Elektroniki i Informatyki Politechniki Koszalińskiej

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2016

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Nr 9

37--50

EN

Proposed method, called Probabilistic Features Combination (PFC), is the method of multi-dimensional data modeling, extrapolation and interpolation using the set of high-dimensional feature vectors. This method is a hybridization of numerical methods and probabilistic methods. Identification of faces or fingerprints need modeling and each model of the pattern is built by a choice of multi-dimensional probability distribution function and feature combination. PFC modeling via nodes combination and parameter γ as N-dimensional probability distribution function enables data parameterization and interpolation for feature vectors. Multidimensional data is modeled and interpolated via nodes combination and different functions as probability distribution functions for each feature treated as random variable: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.

PL

Autorska metoda Probabilistycznej Kombinacji Cech - Probabilistic Features Combination (PFC) jest wykorzystywana do interpolacji i modelowania wielowymiarowych danych. Węzły traktowane są jako punkty charakterystyczne N-wymiarowej informacji, która ma być odtwarzana (np. obraz). Wielowymiarowe dane są interpolowane lub rekonstruowane z wykorzystaniem funkcji rozkładu prawdopodobieństwa: potęgowych, wielomianowych, wykładniczych, logarytmicznych, trygonometrycznych, cyklometrycznych.

5

Content-based image retrieval using a signature graph and a self-organizing map

Van T. T., Le T. M.

International Journal of Applied Mathematics and Computer Science

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2016

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Vol. 26, no. 2

423--438

EN

In order to effectively retrieve a large database of images, a method of creating an image retrieval system CBIR (content-based image retrieval) is applied based on a binary index which aims to describe features of an image object of interest. This index is called the binary signature and builds input data for the problem of matching similar images. To extract the object of interest, we propose an image segmentation method on the basis of low-level visual features including the color and texture of the image. These features are extracted at each block of the image by the discrete wavelet frame transform and the appropriate color space. On the basis of a segmented image, we create a binary signature to describe the location, color and shape of the objects of interest. In order to match similar images, we provide a similarity measure between the images based on binary signatures. Then, we present a CBIR model which combines a signature graph and a self-organizing map to cluster and store similar images. To illustrate the proposed method, experiments on image databases are reported, including COREL, Wang and MSRDI.

6

An autonomous monitoring system based on object shape detection

Deniziak S., Michno T., Pięta P.

Measurement Automation Monitoring

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2015

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Vol. 61, No. 7

349--351

EN

In traditional monitoring systems, stationary cameras are supervised only by a human operator, who may easily miss some events recorded by a camera. Because it is imperative for a surveillance system to be reliable, its autonomy can be extended by applying computer vision algorithms to a video signal and also by the use of mobile robots capable of monitoring tight and occluded areas. In this paper, we present an overview of the concept of an autonomous monitoring system based on object shape detection. Our goal is to develop a real-time system which robustly and efficiently identifies objects on the basis of their approximate shape. For monitoring the environment we use active and smart cameras capable of remote position control, as well as mobots equipped with video sensors. After performing the object extraction from individual video frames, each new detected object is decomposed into simple graphical primitives like lines, circles, rectangles etc. and then identified in a database using the Query by Shape (QS) method.

7

Content-based Image Retrieval using Visual Attention Point Features

Wang X.-Y., Li Y.-W., Niu P.-P., Yang H.-Y., Li D.-M.

Fundamenta Informaticae

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2014

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Vol. 135, nr 3

309--329

EN

One of the challenges in the development of a content-based image indexing and retrieval application is to achieve an efficient and robust indexing scheme. Color is a fundamental image feature used in content-based image retrieval (CBIR) systems. This paper proposes a robust and effective image retrieval scheme, which is based on the weighed color histogram of visual attention points. Firstly, the fully affine invariant visual attention points are extracted from the origin color image by using the Affine-SIFT (scale-invariant feature transform) detector. Secondly, according to the color complexity measure (CCM) theory, the visual weight values for the significant visual attention points are calculated to reflect the image local variation. Then, the weighed color histogram of visual attention points is constructed. Finally, the similarity between color images is computed by using the weighed color histogram of visual attention points. Experimental results show that the proposed image retrieval is not only more accurate and efficient in retrieving the user-interested images, but also yields higher retrieval accuracy than some state-of-the-art image retrieval schemes for various test DBs.

8

Fuzzy Sugeno integral interpretation with application in histopathology image analysis

Tabakov M., Kwaśnicka H., Możejko K.

Systems Science

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2010

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Vol. 36, no 4

71-82

EN

In this article, we present the specification of a histopathology image retrieval system, based on Sugeno fuzzy integral, applied to the breast cancer diagnosis. The system proposed can be used as a medical decision support tool. The decision problem under consideration is related to the problem of recognition of histopathology images with respect to the degree of HER2/neu receptor overexpression. The proposed solution is based on Sugeno fuzzy integral with well-defined interpretation of the problem. Experiments showed usefulness of our approach.

9

Przegląd podstawowych metod reprezentacji kształtów 3 D

Frejlichowski Dariusz, Nuszkiewicz Patrycja

Studia i Materiały Informatyki Stosowanej

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2010

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nr 2

15--21

PL

W artykule przedstawiono przegląd istniejących rozwiązań w dziedzinie reprezentowania kształtów trójwymiarowych, stosunkowo nowym podejściu do opisywania obiektów w przetwarzaniu, rozpoznawaniu i indeksowaniu obrazów. Deskryptory kształtu 3D stają się coraz potrzebniejsze i coraz powszechniej stosowane. Wynika to z rozwoju sprzętu komputerowego, a co za tym idzie możliwości szybkiego przetwarzania skomplikowanych scen trójwymiarowych. Znajduje się przy tym kolejne obszary zastosowań trójwymiarowego opisu obiektów, m.in. w biometrii, systemach CAD i indeksowaniu.

EN

In the paper a brief survey on existing approaches to the representation of three-dimensional shapes was presented. It is a new way of describing objects in image processing, recognition and indexing. The 3D shape descriptors become more and more useful and widely used. Rapid development of computer hardware and the possibilities of fast processing of three-dimensional scenes cause it. Many new applications of 3D shape description can be easily found, e.g. in biometrics, CAD systems and image retrieval.

10

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.

11

Detekcja kopii obrazu metodą cech lokalnych

Wnukowicz K., Skarbek W.

Elektronika : konstrukcje, technologie, zastosowania

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2009

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Vol. 50, nr 4

72-76

PL

W artykule przedstawiono metodę detekcji kopii obrazów na podstawie treści wizualnej. W metodzie tej wyznacza się zwięzłą sygnaturę reprezentującą unikalną treść obrazu. Sygnatura ta jest odporna na wiele popularnych technik modyfikacji obrazów, które nie powodują istotnej utraty informacji, takich jak kompresja stratna, zmiana rozmiaru, poprawa kolorów, czy proste obroty. Wykrywanie kopii obrazów wykonuje się za pomocą szybkiego algorytmu porównywania ich sygnatur. Właściwości prezentowanej sygnatury, takie jak mały rozmiar, szybka metoda obliczania i detekcji oraz wysoka skuteczność wykrywania kopii, pozwalają na jej zastosowanie w aplikacjach zarządzania dużymi zbiorami obrazów, w tym również do wykrywania kopii obrazów w zasobach Internetowych.

EN

The paper presents a method for image copy detection based on visual content. In this method a compact image signature is extracted, which represents unique image content. Te signature is robust to many common image processing techniques, which do not lead to significant loss of information, such as lossy compression, resizing, color enhancements and simple rotations. The detection of image copies is performed by a fast algorithm of signature matching. The properties of the presented signature, such as small size, fast extraction, fast matching, and high detection rate of image copies, allow the signature to be used in big image databases, including image resources on the Web.

12

Testing dimension reduction methods for image retrieval

Moravec P., Snášel V.

Image Processing & Communications

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2006

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Vol. 11, no 2

51-61

EN

In this paper, we compare performance of several dimension reduction techniques, namely LSI, NMF, SDD and FastMap. The qualitative comparison is based on rank lists and evaluated on a collection of faces from the Olivetti Research Lab. We compare the quality of these methods from several standpoints: the visual impact, quality of generated "eigenfaces", size of reduced matrices and retrieval performance.

13

An Image Retrieval System Based on the Color, Areas, and Perimeters of Objects

Wang C-L., Hwang R-H., Chan Y-K, Chen C-Y., Cheng C-C.

Fundamenta Informaticae

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2006

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Vol. 69, nr 3

319-330

EN

Two different objects can be generally distinguished by their colors, areas, and perimeters. This paper hence proposes an image retrieval system which uses the colors, areas, and perimeters of objects in an image as the features of the image. The system is insensitive to the shift and rotation variations of objects in images, as well as to the scale and noise variations of images. The experimental results show that this system is capable of recognizing different images very well.

14

Image retrieval based on hierarchical Gabor filters

Andrysiak T., Choraś M.

International Journal of Applied Mathematics and Computer Science

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2005

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Vol. 15, no 4

471-480

EN

Content Based Image Retrieval (CBIR) is now a widely investigated issue that aims at allowing users of multimedia information systems to automatically retrieve images coherent with a sample image. A way to achieve this goal is the computation of image features such as the color, texture, shape, and position of objects within images, and the use of those features as query terms. We propose to use Gabor filtration properties in order to find such appropriate features. The article presents multichannel Gabor filtering and a hierarchical image representation. Then a salient (characteristic) point detection algorithm is presented so that texture parameters are computed only in a neighborhood of salient points. We use Gabor texture features as image content descriptors and efficiently emply them to retrieve images.

15

Colour temperature estimation algorithm for digital images-properties and convergence

Wnukowicz K., Skarbek W.

Opto-Electronics Review

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2003

|

Vol. 11, No. 3

193-196

EN

The paper presents an algorithm for estimation of temperature of image. Colour temperature is important, perceptual feature describing colour and content of images. The main idea of the algorithm is to average pixel values of image, omitting the values which have meaningless influence on perception of colour temperature. It is done in an interactive procedure. The convergence of the procedure is discussed. The algorithm can be applied in image search/retrieval tasks and is proposed in the MPEG-7 colour temperature descriptor for estimation of colour temperature of images.

16

Improvement and invariance analysis of Zernike moments using as a region-based shape descriptor

Ye B., Peng J.

Image Processing & Communications

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2002

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Vol. 8, no 3

11-20

EN

In order to retrieve an image from a large image database, the descriptor should be invariant to scale and rotation. It must, also have enough discriminating power and immunity to noise for retrieval from a large image database. The Zernike moment descriptor has manv desirable properties such as rotation invariance, robustness to noise, expression efficiency, fast computation and multi-level representation for describing the shapes of patterns, but it does not possess scale invariance. In this paper, we present an improved Zernike moment descriptor that not only has rotation invariance, but also has scale invariance. We apply the improved Zernike moments to image recognition using as an elective descriptor of global shape of an image in a large image database. The experimemtal results show that the improved Zernike moment has better invariant properties than unimproved Zernike moment using as region-based shape descriptor.

17

Similarity analysis in medical image databases

Rumiński J.

TASK Quarterly : scientific bulletin of Academic Computer Centre in Gdansk

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1999

|

Vol. 3, No 4

419-444

EN

The review of methods of similarity analysis of medical images is presented. Feature extraction, feature representation and different concepts of image query algebra problems are described and discussed from the medical application point of view. New algorithms based on medical image regularity description and intensity description are proposed. As a conclusion a Java application "ObrazMed" for content based medical image analysis is presented.