scholarly journals Simple, Flexible Method to Extract Digital Image Features

2021 ◽  
Vol 38 (5) ◽  
pp. 1509-1514
Author(s):  
Mohammad S. Khrisat ◽  
Hatim Ghazi Zaini ◽  
Ziad A. Alqadi
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Extraction Process ◽  
Image Features ◽  
Features Extraction ◽  
Experimental Results ◽  
Speed Up ◽  
Classification Prediction

The process of digital image features extraction is very important and it is required in many applications such as classification, prediction and regression. The extracted features for each image must be unique and capable to be used as an image identifier. In this paper we will introduce a method of image features extraction; it will be shown that this method will enhance the efficiency of the features extraction process. The proposed method will be experimentally tested using various images; the obtained experimental results will be compared with other existing methods of feature extraction to show the advantages of the proposed method and to show how to increase the speed up of the method.

scholarly journals Classification of Sambas Traditional Fabric “Kain Lunggi” Using Texture Feature

2019 ◽  
Vol 13 (4) ◽  
pp. 389
Author(s):  
Alda Cendekia Siregar ◽  
Barry Ceasar Octariadi
Keyword(s):  
Image Processing ◽  
Feature Extraction ◽  
Feature Selection ◽  
Digital Image Processing ◽  
Digital Image ◽  
Classification Accuracy ◽  
Selection Process ◽  
Image Features ◽  
K Value ◽  
Feature Extraction Method

Traditional fabric is a cultural heritage that has to be preserved. Kain Lunggi is Sambas traditional fabric that saw a decline in its crafter. To introduce Kain Lunggi in a broader national and global society in order to preserve it, a digital image processing based system to perform Kain Lunggi pattern recognition need to be built. Feature extraction is an important part of digital image processing. The visual feature that does not represent the character of an object will affect the accuracy of a recognition system. The purposes of this research are to perform feature selection on sets of feature to determine the best feature that can increase recognition accuracy. This research conducted in several steps which are image acquisition of Kain Lunggi pattern, preprocessing to reduce image noise, feature extraction to obtain image features, and feature selection. GLCM is implemented as a feature extraction method.  Feature extraction result will be used in a feature selection process using CFS (Correlation-based Feature Selection) methods. Selected features from CFS process are Angular Second Moment, Contrast, and Correlation. Selected features evaluation is conducted by calculating classification accuracy with the KNN method. Classification accuracy prior to feature extraction is 85.18% with K values K=1 ; meanwhile, the accuracy increases to 88.89% after feature selection. The highest accuracy improvement of 20.74% in KNN occurred when using K value K= 4.

Review on Singular-Value-Decomposition-Based Digital Watermarking Algorithm in Combination with Image Features

2012 ◽  
Vol 424-425 ◽  
pp. 452-463
Author(s):  
Tong Tong Liu ◽  
Cheng Yang
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Comparative Analysis ◽  
Singular Value Decomposition ◽  
Digital Watermarking ◽  
Image Features ◽  
Singular Value ◽  
Experimental Results ◽  
Processing Effects ◽  
Value Decomposition

The combination of image features with singular value decomposition algorithm and digital watermarking algorithm based on wavelet transform respectively makes possible the advancement from pixel watermarking to content watermarking, which effectively solves the contradiction between perceptibility and robustness of conventional watermarking algorithm. Therefore, stronger robustness and lower perceptibility of watermarking are achieved. This paper first gives a review on conventional singular-value-decomposition-based digital watermarking algorithm, and then makes a thorough analysis of its respective features, advantages and defects. Improved feature extraction schemes of digital watermark which combine image features with SVD algorithm as well as the application algorithm in feature extraction based on improved SVD algorithm are put forward for the comparative analysis of corresponding principles and processing effects. The experimental results indicate that the content-based (instead of pixel-based) watermarking algorithm can better satisfy the perceptibility and robustness of digital watermarking, with huge application potentials and more development space

scholarly journals Identification and Classification of Lung Nodules Using Neural Networks

2021 ◽  
Vol 12 (6) ◽  
pp. 1956-1961
Author(s):  
Dr. S. Gnanavel Et al.
Keyword(s):  
Lung Cancer ◽  
Image Processing ◽  
Feature Extraction ◽  
Digital Image Processing ◽  
Digital Image ◽  
Extraction Process ◽  
Health Concern ◽  
Image Processing Techniques ◽  
Processing Techniques

Lung cancer is a serious health concern, which is also one of the major types of cancer that has a profound impact on the overall cancer mortality rates. The detection of lung cancer nodules is quite a challenge as the major challenge is the structure of the cancer nodules; here the cells are imbricated with each other. The prediction and classification of lung cancer is done by applying digital image processing techniques to the acquired input images of the nodules. This methodology also aids early detection which in turns reduces the criticality of the condition and provides scope for early intervention and treatment. The prediction methodology involves extracting several features of the lung cancer cell and then applying pattern-based prediction techniques. In recent times, owing to the fact that the time and execution parameters are very important aspects to detect the abnormality of the fast-spreading cancer cells, digital image processing techniques are being widely deployed. The fundamental factors of this research are the quality of image assessment and the precision of feature extraction. Following our proposed methodology, a clear picture of the region of interest is obtained which acts as a basis for the feature extraction process. Here an overall evaluation of the digital image processing techniques used by previous scholars for the finding and classification of lung cancer nodules have also been emphasised.

Recognizing Substitution Steganography of Spatial Domain Based on the Characteristics of Pixels Correlation

2017 ◽  
Vol 9 (4) ◽  
pp. 48-61 ◽  
Author(s):  
Zhe Chen ◽  
Jicang Lu ◽  
Pengfei Yang ◽  
Xiangyang Luo
Keyword(s):  
Feature Extraction ◽  
Digital Image ◽  
Spatial Domain ◽  
Recognition Algorithm ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Image Steganalysis ◽  
Statistical Feature ◽  
Better Than

Steganographic algorithm recognition is currently a key issue in digital image steganalysis. For the typical substitution steganographic algorithm in spatial domain, we analyze the modification way and construct the feature extraction source based on the adjacent pixels correlation; extract the special statistical feature which could distinguish the substitution steganography from other types of steganographic algorithms. Finally, a substitution steganography recognition algorithm is presented and tested by experiments. The experimental results show that, the proposed algorithm could recognize the substitution steganography in spatial domain efficiently, and the detection accuracy is better than existing algorithms.

The Face Detection Research Based on Multi-Scale and Rectangle Feature

2012 ◽  
Vol 198-199 ◽  
pp. 1383-1388
Author(s):  
Hong Hai Liu ◽  
Xiang Hua Hou
Keyword(s):  
Feature Extraction ◽  
Image Features ◽  
Face Image ◽  
High Dimensions ◽  
Feature Extraction Method ◽  
Face Features ◽  
Haar Feature ◽  
The Face ◽  
Speed Up ◽  
Feature Expression

When extracting the face image features based on pixel distribution in face image, there always exist large amount of calculation and high dimensions of feature sector generated after feature extraction. This paper puts forward a feature extraction method based on prior knowledge of face and Haar feature. Firstly, the Haar feature expressions of face images are classified and the face features are decomposed into edge feature, line feature and center-surround feature, which are further concluded into the expressions of two rectangles, three rectangles and four rectangles. In addition, each rectangle varies in size. However, for face image combination, there exist too much redundancy and large calculation amount in this kind of expression. In order to solve the problem of large amount of calculation, the integral image is adopted to speed up the rectangle feature calculation. In addition, the thought based on classified trainer is adopted to reduce the redundancy expression. The results show that using face image of Haar feature expression can improve the speed and efficiency of recognition.

scholarly journals A Study of Digital Image Enlargement and Enhancement

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Hsueh-Yi Lin ◽  
Chi-Yuan Lin ◽  
Cheng-Jian Lin ◽  
Sheng-Chih Yang ◽  
Cheng-Yi Yu
Keyword(s):  
Digital Image ◽  
Image Features ◽  
Experimental Results ◽  
Hyperbolic Tangent ◽  
High Quality ◽  
Image Enlargement

Most image enlargement techniques suffer the problem of zigzagged edges and jagged images following enlargement. Humans are sensitive to the edges of objects; if the edges in the image are sharp, the visual is considered to be high quality. To solve this problem, this paper presents a new and effective method for image enlargement and enhancement based on adaptive inverse hyperbolic tangent (AIHT) algorithm. Conventional image enlargement and enhancement methods enlarge the image using interpolation, and subsequently enhance the image without considering image features. However, this study presents the method based on Adaptive Inverse Hyperbolic Tangent algorithm to enhance images according to image features before enlarging the image. Experimental results indicate that the proposed algorithm is capable of adaptively enhancing the image and extruding object details, thereby improving enlargements by smoothing the edge of the objects in the image.

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

2018 ◽  
Author(s):  
I Wayan Agus Surya Darma
Keyword(s):  
Feature Extraction ◽  
Success Rate ◽  
Character Recognition ◽  
Nearest Neighbor ◽  
Recognition System ◽  
Extraction Process ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm ◽  
Character Feature

Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

scholarly journals Thermochemical Heat Storage in a Lab-Scale Indirectly Operated CaO/Ca(OH)2 Reactor—Numerical Modeling and Model Validation through Inverse Parameter Estimation

2021 ◽  
Vol 11 (2) ◽  
pp. 682
Author(s):  
Gabriele Seitz ◽  
Farid Mohammadi ◽  
Holger Class
Keyword(s):  
Numerical Model ◽  
Gas Flow ◽  
Reaction Rate ◽  
Heat Storage ◽  
Bulk Material ◽  
Fixed Bed ◽  
Experimental Results ◽  
Fixed Bed Reactor ◽  
Thermochemical Heat Storage ◽  
Speed Up

Calcium oxide/Calcium hydroxide can be utilized as a reaction system for thermochemical heat storage. It features a high storage capacity, is cheap, and does not involve major environmental concerns. Operationally, different fixed-bed reactor concepts can be distinguished; direct reactor are characterized by gas flow through the reactive bulk material, while in indirect reactors, the heat-carrying gas flow is separated from the bulk material. This study puts a focus on the indirectly operated fixed-bed reactor setup. The fluxes of the reaction fluid and the heat-carrying flow are decoupled in order to overcome limitations due to heat conduction in the reactive bulk material. The fixed bed represents a porous medium where Darcy-type flow conditions can be assumed. Here, a numerical model for such a reactor concept is presented, which has been implemented in the software DuMux. An attempt to calibrate and validate it with experimental results from the literature is discussed in detail. This allows for the identification of a deficient insulation of the experimental setup. Accordingly, heat-loss mechanisms are included in the model. However, it can be shown that heat losses alone are not sufficient to explain the experimental results. It is evident that another effect plays a role here. Using Bayesian inference, this effect is identified as the reaction rate decreasing with progressing conversion of reactive material. The calibrated model reveals that more heat is lost over the reactor surface than transported in the heat transfer channel, which causes a considerable speed-up of the discharge reaction. An observed deceleration of the reaction rate at progressed conversion is attributed to the presence of agglomerates of the bulk material in the fixed bed. This retardation is represented phenomenologically by mofifying the reaction kinetics. After the calibration, the model is validated with a second set of experimental results. To speed up the calculations for the calibration, the numerical model is replaced by a surrogate model based on Polynomial Chaos Expansion and Principal Component Analysis.

scholarly journals Categorisation of EEG suppression using enhanced feature extraction for SUDEP risk assessment

2020 ◽  
Vol 20 (S12) ◽  
Author(s):  
Juan C. Mier ◽  
Yejin Kim ◽  
Xiaoqian Jiang ◽  
Guo-Qiang Zhang ◽  
Samden Lhatoo
Keyword(s):  
Sensitivity Analysis ◽  
Feature Extraction ◽  
Random Forest ◽  
Window Size ◽  
Extraction Process ◽  
Unexpected Death ◽  
Power Spectral ◽  
Eeg Data ◽  
Boosted Decision Trees ◽  
Future Work

Abstract Background Sudden Unexpected Death in Epilepsy (SUDEP) has increased in awareness considerably over the last two decades and is acknowledged as a serious problem in epilepsy. However, the scientific community remains unclear on the reason or possible bio markers that can discern potentially fatal seizures from other non-fatal seizures. The duration of postictal generalized EEG suppression (PGES) is a promising candidate to aid in identifying SUDEP risk. The length of time a patient experiences PGES after a seizure may be used to infer the risk a patient may have of SUDEP later in life. However, the problem becomes identifying the duration, or marking the end, of PGES (Tomson et al. in Lancet Neurol 7(11):1021–1031, 2008; Nashef in Epilepsia 38:6–8, 1997). Methods This work addresses the problem of marking the end to PGES in EEG data, extracted from patients during a clinically supervised seizure. This work proposes a sensitivity analysis on EEG window size/delay, feature extraction and classifiers along with associated hyperparameters. The resulting sensitivity analysis includes the Gradient Boosted Decision Trees and Random Forest classifiers trained on 10 extracted features rooted in fundamental EEG behavior using an EEG specific feature extraction process (pyEEG) and 5 different window sizes or delays (Bao et al. in Comput Intell Neurosci 2011:1687–5265, 2011). Results The machine learning architecture described above scored a maximum AUC score of 76.02% with the Random Forest classifier trained on all extracted features. The highest performing features included SVD Entropy, Petrosan Fractal Dimension and Power Spectral Intensity. Conclusion The methods described are effective in automatically marking the end to PGES. Future work should include integration of these methods into the clinical setting and using the results to be able to predict a patient’s SUDEP risk.

Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-18 ◽  
Author(s):  
Huimin Lu ◽  
Rui Yang ◽  
Zhenrong Deng ◽  
Yonglin Zhang ◽  
Guangwei Gao ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Contextual Information ◽  
Image Features ◽  
Context Information ◽  
Global Information ◽  
Image Description ◽  
Image Captioning ◽  
Image Content ◽  
Single Feature ◽  
Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

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