Search results

1 – 10 of over 2000
Article
Publication date: 27 March 2009

Ntogas Nikolaos and Ventzas Dimitrios

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition…

Abstract

Purpose

The purpose of this paper is to introduce an innovative procedure for digital historical documents image binarization based on image pre‐processing and image condition classification. The estimated results for each class of images and each method have shown improved image quality for the six categories of document images described by their separate characteristics.

Design/methodology/approach

The applied technique consists of five stages, i.e. text image acquisition, image preparation, denoising, image type classification in six categories according to image condition, image thresholding and final refinement, a very effective approach to binarize document images. The results achieved by the authors' method require minimal pre‐processing steps for best quality of the image and increased text readability. This methodology performs better compared to current state‐of‐the‐art adaptive thresholding techniques.

Findings

An innovative procedure for digital historical documents image binarization based on image pre‐processing, image type classification in categories according to image condition and further enhancement. This methodology is robust and simple, with minimal pre‐processing steps for best quality of the image, increased text readability and it performs better compared to available thresholding techniques.

Research limitations/implications

The technique consists of limited but optimized pre‐processing sequential steps, and attention should be given in document image preparation and denoising, and on image condition classification for thresholding and refinement, since bad results in a single stage corrupt the final document image quality and text readability.

Originality/value

The paper contributes in digital image binarization of text images suggesting a procedure based on image preparation, image type classification and thresholding and image refinement with applicability on Byzantine historical documents.

Details

International Journal of Intelligent Computing and Cybernetics, vol. 2 no. 1
Type: Research Article
ISSN: 1756-378X

Keywords

Article
Publication date: 8 April 2016

Tarek Bentahar, Djamel Benatia and Mohamed Boulila

In this paper, a new efficient method to de-noise the interferometric Synthetic Aperture Radar interferogram, also called wrapped phase image, is proposed with the aim to reduce…

66

Abstract

Purpose

In this paper, a new efficient method to de-noise the interferometric Synthetic Aperture Radar interferogram, also called wrapped phase image, is proposed with the aim to reduce the residue number and make the phase unwrapping process easy.

Design/methodology/approach

This method is based on two statistics functions, the former is the phase derivative variance (PDV) defined as a quality map to select the badness areas, the second one is the phase derivative variance (PAD) for a local 3 × 3 pixels filtering which allows to assign an estimated phase for each bad area selected by PDV function. Our filter was tested with a simulated interferograms and compared to other most used filters.

Findings

With this proposed method, the residues in the interferogram are minimized better than using a conventional filters, and the phase unwrapping process gives a better estimation.

Originality/value

Combining two statistical functions (PDV and PAD) is efficient in terms of minimizing the noise in the interferogram; this is very helpful to minimize the processing time of the InSAR image particularly the phase unwrapping treatment and have a good quality of the image.

Details

World Journal of Engineering, vol. 13 no. 2
Type: Research Article
ISSN: 1708-5284

Keywords

Article
Publication date: 7 December 2021

Sreelakshmi D. and Syed Inthiyaz

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this…

Abstract

Purpose

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

Design/methodology/approach

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

Findings

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

Originality/value

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

Details

International Journal of Pervasive Computing and Communications, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 1 June 1998

Jinung An

X‐ray laminography, a tomographic technique that can examine individual planes of focus within a 3‐D structure, promises to be an excellent method of inspecting complicated…

Abstract

X‐ray laminography, a tomographic technique that can examine individual planes of focus within a 3‐D structure, promises to be an excellent method of inspecting complicated circuit boards. The technique has accuracy appropriate for circuit board inspection, but the application has been limited by the requirements of synchronized motion of the source and detector, a sophisticated X‐ray device and a huge image acquiring system. A new translational laminography system is presented. The X‐ray source and detector described are stationary. Translation of the XY table is only the mechanical motion required to generate the laminographic image. Based on this system, a new image separation algorithm is also explained. This algorithm uses a recursive process with a simple mathematical function, which is derived analytically by the X‐ray projection geometry. To evaluate the proposed method, an X‐ray imaging system has been constructed. From the test sample experiments, it is confirmed that the proposed algorithm allows cleanly separated images with fewer artifacts than the one obtained by conventional laminography.

Details

Circuit World, vol. 24 no. 2
Type: Research Article
ISSN: 0305-6120

Keywords

Article
Publication date: 27 January 2012

Jinshuai Zhao, Sujin Yang and Liu Xin

The purpose of this paper is to construct a novel grey filter model for image denoising and to solve the problems which exist in the image denoising filter method, in which the…

333

Abstract

Purpose

The purpose of this paper is to construct a novel grey filter model for image denoising and to solve the problems which exist in the image denoising filter method, in which the true intensity value of each noisy pixel cannot be predicted better.

Design/methodology/approach

Based on the definition of stepwise, the defects of traditional grey prediction models are found. A new grey filter model, named grey stepwise prediction model, is proposed. The new filter model for the image denoising is based on each noisy pixel's neighborhoods stepwise, which is the eight pixels around the noisy pixel, to predict its intensity value and to solve the problems which exist in the image denoising filter method.

Findings

The experiment results show that the improved filter model can effectively eliminate image noise, preserve the image's details and edges, increase SNR (signal‐to‐noise ratio) as well as PSNR (peak signal‐to‐noise ratio), reduce MSE (mean square error) and MAE (mean absolute error), and significantly improve the image's visual effect.

Practical implications

The new filter method exposed in the paper can be used to 8‐bit gray‐scale image denoising. The method can also be used to binary image denoising.

Originality/value

The paper succeeds in constructing a novel filter method for image denoding, and it is undoubtedly a new development in image recovery algorithm and grey systems theory.

Details

Grey Systems: Theory and Application, vol. 2 no. 1
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 28 October 2014

Ke Ren and Yinghan Hong

– The purpose of this paper is to make the test of flexible printed circuit (FPC) board be more efficient and accurate.

1902

Abstract

Purpose

The purpose of this paper is to make the test of flexible printed circuit (FPC) board be more efficient and accurate.

Design/methodology/approach

This paper aims to segment an image through applying the technology of computer pattern recognition.

Findings

The results indicate that the automatic detection system of FPC board’s defect has high efficiency and accuracy in accordance with the requirements of detection.

Originality/value

This paper managed image segmentation through applying the technology of computer pattern recognition.

Details

Circuit World, vol. 40 no. 4
Type: Research Article
ISSN: 0305-6120

Keywords

Article
Publication date: 22 March 2013

Zhen Ye, Gu Fang, Shanben Chen and Mitchell Dinham

This paper aims to develop a method to extract the weld seam from the welding image.

Abstract

Purpose

This paper aims to develop a method to extract the weld seam from the welding image.

Design/methodology/approach

The initial step is to set the window for the region of the weld seam. Filter and edge‐operator are then applied to acquire edges of images. Based on the prior knowledge about characteristics of the weld seam, a series of routines is proposed to recognize the seam edges and calculate the seam representation.

Findings

The proposed method can be used to extract seams of different deviations from noise‐polluted images efficiently. Besides, the method is low time‐consuming and quick enough for real time processing.

Practical implications

Weld seam extraction is the key problem in passive vision based seam tracking technology. The proposed method can extract the weld seam even when the image is noisy, and it is quick enough to be applied in seam tracking technology. The method is expected to improve seam tracking results.

Originality/value

A useful method is developed for weld seam extraction from the noise‐polluted image based on prior knowledge of weld seam. The method is robust and quick enough for real time processing.

Details

Sensor Review, vol. 33 no. 2
Type: Research Article
ISSN: 0260-2288

Keywords

Article
Publication date: 10 March 2022

Jayaram Boga and Dhilip Kumar V.

For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning…

94

Abstract

Purpose

For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed.

Design/methodology/approach

The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification.

Findings

The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively.

Originality/value

The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.

Details

International Journal of Pervasive Computing and Communications, vol. 19 no. 4
Type: Research Article
ISSN: 1742-7371

Keywords

Article
Publication date: 18 July 2019

Mohamed Marzouk and Mahmoud Hassouna

This paper aims to propose a system for defect detection in constructed elements that is able to indicate deformity positions. It also evaluates the defects in finishing materials…

Abstract

Purpose

This paper aims to propose a system for defect detection in constructed elements that is able to indicate deformity positions. It also evaluates the defects in finishing materials of constructed building elements to support the subjective visual quality investigation of the aesthetics of an architectural work.

Design/methodology/approach

This strategy depends on defect features analysis that evaluates the defect value in digital images using digital image processing methods. The research uses the three-dimensional (3D) modeling techniques and image processing algorithms to generate a system that is able to perform some of the monitoring activities by computers. Based on the collected site scans, a 3D model is created for the building. Then, several images can be exported from the 3D model to investigate a specific element. Different image denoizing techniques are compared such as mean filter, median filter, Wiener filter and Split–Bregman iterations. The most efficient technique is implemented in the system. Then, the following six different methods are used for image segmentation to separate the concerned object from the background; color segmentation, region growing segmentation, histogram segmentation, local standard deviation segmentation, adaptive threshold segmentation and mean-shift cluster segmentation.

Findings

The proposed system is able to detect the cracks and defected areas in finishing works and calculate the percentage of the defected area compared to the total captured area in the photo with high accuracy.

Originality/value

The proposed system increases the precision of decision-making by decreasing the contribution of human subjective judgment. Investigation of different finishing surfaces is applied to validate the proposed system.

Article
Publication date: 13 September 2021

Naresh Kattekola, Amol Jawale, Pallab Kumar Nath and Shubhankar Majumdar

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

Abstract

Purpose

This paper aims to improve the performance of approximate multiplier in terms of peak signal to noise ratio (PSNR) and quality of the image.

Design/methodology/approach

The paper proposes an approximate circuit for 4:2 compressor, which shows a significant amount of improvement in performance metrics than that of the existing designs. This paper also reports a hybrid architecture for the Dadda multiplier, which incorporates proposed 4:2 compressor circuit as a basic building block.

Findings

Hybrid Dadda multiplier architecture is used in a median filter for image de-noising application and achieved 20% more PSNR than that of the best available designs.

Originality/value

The proposed 4:2 compressor improves the error metrics of a Hybrid Dadda multiplier.

Details

Circuit World, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0305-6120

Keywords

1 – 10 of over 2000