scholarly journals Identifikasi Karakteristik Citra Berdasarkan pada Nilai Entropi dan Kontras

2021 ◽  
Vol 2 (1) ◽  
pp. 18-23
Author(s):  
Bheta Agus Wardijono ◽  
Lussiana ETP ◽  
Rozi
Keyword(s):  
Edge Detection ◽  
Good Condition ◽  
The Other ◽  
Detection Methods ◽  
Test Results ◽  
Image Size ◽  
Image Region ◽  
Image Characteristics ◽  
Different Characteristics ◽  
Region Size

Abstract Determining the object boundaries in an image is a necessary process, to identify the boundaries of an object with other objects as well as to define an object in the image. The acquired image is not always in good condition, on the other hand there is a lot of noise and blur. Various edge detection methods have been developed by providing noise parameters to reduce noise, and adding a blur parameter but because these parameters apply to the entire image, but lossing some edges due to these parameters. This study aims to identify the characteristics of the image region, whether the region condition is noise, blurry or otherwise sharp (clear). The step is done by dividing the four regions from the image size, then calculating the entropy value and contrast value of each formed region. The test results show that changes in region size can produce different characteristics, this is indicated by entropy and contrast values ​​of each formed region. Thus it can be concluded that entropy and contrast can be used as a way to identify image characteristics, and dividing the image into regions provides more detailed image characteristics.  

A Steganalytic Scheme Based on Classifier Selection Using Joint Image Characteristics

2020 ◽  
pp. 362-376
Author(s):  
Jie Zhu ◽  
Qingxiao Guan ◽  
Xianfeng Zhao ◽  
Yun Cao ◽  
Gong Chen
Keyword(s):  
Image Size ◽  
Size Quantization ◽  
Classification Techniques ◽  
Appreciable Difference ◽  
Classifier Selection ◽  
Image Characteristics ◽  
Improve Accuracy ◽  
Different Characteristics ◽  
Selection Of

Steganalysis relies on steganalytic features and classification techniques. Because of the complexity and different characteristics of cover images, to make steganalysis more applicable toward detecting stego images in real applications, we need to train different classifiers so as to match different images according to their characteristics. Selection of classifiers according to characteristics of images is the key point to improve accuracy of steganalysis. In our work, we study the methods of classifier selection based on characteristics of images including image size, quantization factor, or matrix. Besides, we also discuss other characteristics, such as texture, cover source, which makes an appreciable difference to steganalysis.

A Steganalytic Scheme Based on Classifier Selection Using Joint Image Characteristics

2017 ◽  
Vol 9 (4) ◽  
pp. 1-14 ◽  
Author(s):  
Jie Zhu ◽  
Qingxiao Guan ◽  
Xianfeng Zhao ◽  
Yun Cao ◽  
Gong Chen
Keyword(s):  
Image Size ◽  
Size Quantization ◽  
Classification Techniques ◽  
Appreciable Difference ◽  
Classifier Selection ◽  
Image Characteristics ◽  
Improve Accuracy ◽  
Different Characteristics ◽  
Selection Of

Steganalysis relies on steganalytic features and classification techniques. Because of the complexity and different characteristics of cover images, to make steganalysis more applicable toward detecting stego images in real applications, we need to train different classifiers so as to match different images according to their characteristics. Selection of classifiers according to characteristics of images is the key point to improve accuracy of steganalysis. In our work, we study the methods of classifier selection based on characteristics of images including image size, quantization factor, or matrix. Besides, we also discuss other characteristics, such as texture, cover source, which makes an appreciable difference to steganalysis.

scholarly journals Analisis Performa Pre-Trained Model Convolutional Neural Network dalam Mendeteksi Penyakit Tuberkulosis

2021 ◽  
Vol 8 (4) ◽  
pp. 805
Author(s):  
Ovy Rochmawanti ◽  
Fitri Utaminingrum ◽  
Fitra A. Bachtiar
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Computation Time ◽  
The Other ◽  
X Rays ◽  
Test Results ◽  
Image Size ◽  
The Difference ◽  
The Right ◽  
Disease Testing

<p>Tuberkulosis (TB) merupakan salah satu penyakit berbahaya yang dapat menular lewat udara dan sering menyebabkan kematian apabila tidak cepat ditangani. Penyakit TB bisa disembuhkan dengan deteksi dini sehingga penderita dapat segera mendapatkan pengobatan yang tepat. Metode Convolutional Neural Network (CNN) digunakan untuk mendeteksi penyakit TB melalui foto rontgen dada. Penelitian ini bertujuan untuk menentukan model CNN yang mampu menghasilkan performa paling baik dalam mendeteksi penyakit TB. Pengujian dilakukan dengan menggunakan lima pre-trained model yang telah disediakan oleh Keras yaitu <em>ResNet50</em>, <em>DenseNet121</em>, <em>MobileNet</em>, <em>Xception</em>, <em>InceptionV3</em>, dan <em>InceptionResNetV2</em>. Perbedaan ukuran gambar yag digunakan pada saat pelatihan dan pengujian juga akan dianalisis pengaruhnya terhadap nilai akurasi yang dihasilkan dan waktu komputasinya. Hasil pengujian menunjukkan bahwa model <em>DenseNet121</em> mampu menghasilkan nilai akurasi tertinggi dalam mendeteksi penyakit TB, yaitu 91,57%. Sedangkan model <em>MobileNet</em> merupakan model dengan waktu komputasi tercepat untuk semua ukuran gambar yang diuji. Semakin besar ukuran citra maka semakin tinggi nilai akurasinya, namun di sisi lain waktu komputasi juga akan semakin lama. </p><p> </p><p><strong>Abstract</strong></p><p><strong> </strong></p><p class="Abstract"><em>Tuberculosis (TB) is one of the dangerous disease that can be transmitted through the air and often causes death if not treated quickly. This illness can be cured with early detection, so that sufferers can immediately get the right treatment. The Convolutional Neural Network (CNN) method is used to detect TB disease through chest X-rays. This study aims to determine which CNN model is able to produce the best performance in detecting TB disease. Testing was carried out using five pre-trained models provided by Keras namely ResNet50, DenseNet121, MobileNet, Xception, InceptionV3, and InceptionResNetV2. The difference in image size used during training and testing will also be analyzed for its effect on the resulting accuracy value and its computation time. The test results showed that the DenseNet121 model was able to produce the highest accuracy value in detecting TB disease, namely 91.57%. Meanwhile, the MobileNet model is the model with the fastest computation time for all image sizes tested. The bigger the image size, the higher the accuracy value, but on the other hand the computation time will also be longer.</em></p>

scholarly journals Comparison between Edge Detection Methods on UTeM Unmanned Arial Vehicles Images

2018 ◽  
Vol 150 ◽  
pp. 06029
Author(s):  
Zuraini Othman ◽  
Asmala Ahmad ◽  
Fauziah Kasmin ◽  
Sharifah Sakinah Syed Ahmad ◽  
Mohd Yazid Abu Sari ◽  
...  
Keyword(s):  
Machine Vision ◽  
Edge Detection ◽  
Unmanned Aerial Vehicles ◽  
Ground Truth ◽  
The Other ◽  
Detection Methods ◽  
Aerial Vehicles ◽  
The Many ◽  
F Measure

Machine vision calls for the use of detectors to ascertain the features and type of object portrayed in the image. The employment of unmanned aerial vehicles (UAVs), which can function freely in active and precarious settings, is currently gaining momentum. These vehicles are mainly used for the detecting, classifying and tracking of an object. However, the achievement of these objectives necessitates the involvement of an effective edge detection procedure. Sobel, Canny, Prewitt and LoG are among the many edge detection procedures presently available. In this endeavour, we opted for the utilization of UTeM UAVs images for an evaluation of these edge detection procedures. During our investigations, the ground truth edge images were corroborated by a specialist in this field. The results obtained from these investigations revealed that in terms of accuracy, precision, sensitivity and f-measure, the Prewitt procedure outperforms the other methods mentioned.

Adding an expansive agent to increase the toughness of steel fibre reinforced concrete

2019 ◽  
Vol 26 (4) ◽  
pp. 197-208
Author(s):  
Leo Gu Li ◽  
Albert Kwok Hung Kwan
Keyword(s):  
Reinforced Concrete ◽  
Steel Fibre ◽  
The Other ◽  
Fibre Reinforced Concrete ◽  
Test Results ◽  
Compressive Properties ◽  
Shrinkage Cracking ◽  
Steel Fibre Reinforced Concrete ◽  
Expansive Agent ◽  
Concrete Matrix

Previous research studies have indicated that using fibres to improve crack resistance and applying expansive agent (EA) to compensate shrinkage are both effective methods to mitigate shrinkage cracking of concrete, and the additions of both fibres and EA can enhance the other performance attributes of concrete. In this study, an EA was added to fibre reinforced concrete (FRC) to produce concrete mixes with various water/binder (W/B) ratios, steel fibre (SF) contents and EA contents for testing of their workability and compressive properties. The test results showed that adding EA would slightly increase the superplasticiser (SP) demand and decrease the compressive strength, Young’s modulus and Poisson’s ratio, but significantly improve the toughness and specific toughness of the steel FRC produced. Such improvement in toughness may be attributed to the pre-stress of the concrete matrix and the confinement effect of the SFs due to the expansion of the concrete and the restraint of the SFs against such expansion.

Comparative Analysis of Edge Detection Methods and its Implementation using UTLP Kit

2016 ◽  
Vol 3 (2) ◽  
pp. 26
Author(s):  
HEMALATHA R. ◽  
SANTHIYAKUMARI N. ◽  
MADHESWARAN M. ◽  
SURESH S. ◽  
◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Edge Detection ◽  
Detection Methods

The Internationalization Of Human Rights And The Importance Of Normative Dialogues Between International And National Courts

2017 ◽  
Vol 17 (2) ◽  
pp. 137
Author(s):  
Mariane Morato Stival ◽  
Marcos André Ribeiro ◽  
Daniel Gonçalves Mendes da Costa
Keyword(s):  
Human Rights ◽  
The Other ◽  
Human Rights Protection ◽  
Rights Protection ◽  
National Courts ◽  
European Court ◽  
Differences Of Opinion ◽  
Rights Discourse ◽  
Different Characteristics ◽  
Human Rights Discourse

This article intends to analyze in the context of the complexity of the process of internationalization of human rights, the definitions and tensions between cultural universalism and relativism, the essence of human rights discourse, its basic norms and an analysis of the normative dialogues in case decisions involving violations of human rights in international tribunals such as the European Court of Human Rights, the Inter-American Court of Human Rights and national courts. The well-established dialogue between courts can bring convergences closer together and remove differences of opinion on human rights protection. A new dynamic can occur through a complementarity of one court with respect to the other, even with the different characteristics between the legal orders.

scholarly journals Evaluating Presence/Absence of Target Microbes in Microbiological Tests

2000 ◽  
Vol 83 (6) ◽  
pp. 1429-1434
Author(s):  
Robert J Blodgett ◽  
Anthony D Hitchins
Keyword(s):  
Performance Studies ◽  
Collaborative Study ◽  
False Negative ◽  
The Other ◽  
Test Results ◽  
Logistic Curve ◽  
Microbiological Method ◽  
Test Portion ◽  
Method Performance ◽  
Data Set

Abstract A typical qualitative microbiological method performance (collaborative) study gathers a data set of responses about a test for the presence or absence of a target microbe. We developed 2 models that estimate false-positive and false-negative rates. One model assumes a constant probability that the tests will indicate the target microbe is present for any positive concentration in the test portion. The other model assumes that this probability follows a logistic curve. Test results from several method performance studies illustrate these estimates.

scholarly journals Low Complexity Lane Detection Methods for Light Photometry System

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1665
Author(s):  
Jakub Suder ◽  
Kacper Podbucki ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Edge Detection ◽  
Optimal Path ◽  
Color Space ◽  
Low Complexity ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Detection Methods ◽  
Raspberry Pi ◽  
Line Detection ◽  
Hsv Color Space

The aim of the paper was to analyze effective solutions for accurate lane detection on the roads. We focused on effective detection of airport runways and taxiways in order to drive a light-measurement trailer correctly. Three techniques for video-based line extracting were used for specific detection of environment conditions: (i) line detection using edge detection, Scharr mask and Hough transform, (ii) finding the optimal path using the hyperbola fitting line detection algorithm based on edge detection and (iii) detection of horizontal markings using image segmentation in the HSV color space. The developed solutions were tuned and tested with the use of embedded devices such as Raspberry Pi 4B or NVIDIA Jetson Nano.

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