Text Localization and Extraction from Background with Texture and Noise in Digital Images Using Adaptive Thresholding and Convolutional Neural Network

2019 ◽  
Author(s):  
Pukjira Pattaranuprawat ◽  
Rajalida Lipikorn
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Images ◽  
Adaptive Thresholding ◽  
Text Localization

scholarly journals Convolutional Neural Network-Based Digital Image Watermarking Adaptive to the Resolution of Image and Watermark

2020 ◽  
Vol 10 (19) ◽  
pp. 6854 ◽  
Author(s):  
Jae-Eun Lee ◽  
Young-Ho Seo ◽  
Dong-Wook Kim
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Data ◽  
Digital Images ◽  
Image Watermarking ◽  
Value Added ◽  
Absolute Error ◽  
Geometric Attacks ◽  
Host Image ◽  
Processing Network

Digital watermarking has been widely studied as a method of protecting the intellectual property rights of digital images, which are high value-added contents. Recently, studies implementing these techniques with neural networks have been conducted. This paper also proposes a neural network to perform a robust, invisible blind watermarking for digital images. It is a convolutional neural network (CNN)-based scheme that consists of pre-processing networks for both host image and watermark, a watermark embedding network, an attack simulation for training, and a watermark extraction network to extract watermark whenever necessary. It has three peculiarities for the application aspect: The first is the host image resolution’s adaptability. This is to apply the proposed method to any resolution of the host image and is performed by composing the network without using any resolution-dependent layer or component. The second peculiarity is the adaptability of the watermark information. This is to provide usability of any user-defined watermark data. It is conducted by using random binary data as the watermark and is changed each iteration during training. The last peculiarity is the controllability of the trade-off relationship between watermark invisibility and robustness against attacks, which provides applicability for different applications requiring different invisibility and robustness. For this, a strength scaling factor for watermark information is applied. Besides, it has the following structural or in-training peculiarities. First, the proposed network is as simple as the most profound path consists of only 13 CNN layers, which is through the pre-processing network, embedding network, and extraction network. The second is that it maintains the host’s resolution by increasing the resolution of a watermark in the watermark pre-processing network, which is to increases the invisibility of the watermark. Also, the average pooling is used in the watermark pre-processing network to properly combine the binary value of the watermark data with the host image, and it also increases the invisibility of the watermark. Finally, as the loss function, the extractor uses mean absolute error (MAE), while the embedding network uses mean square error (MSE). Because the extracted watermark information consists of binary values, the MAE between the extracted watermark and the original one is more suitable for balanced training between the embedder and the extractor. The proposed network’s performance is confirmed through training and evaluation that the proposed method has high invisibility for the watermark (WM) and high robustness against various pixel-value change attacks and geometric attacks. Each of the three peculiarities of this scheme is shown to work well with the experimental results. Besides, it is exhibited that the proposed scheme shows good performance compared to the previous methods.

scholarly journals Coffee Flower Identification Using Binarization Algorithm Based on Convolutional Neural Network for Digital Images

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Pengliang Wei ◽  
Ting Jiang ◽  
Huaiyue Peng ◽  
Hongwei Jin ◽  
Han Sun ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Digital Images ◽  
Time Lapse ◽  
Identification Accuracy ◽  
Support Vector ◽  
Small Scale ◽  
Binarization Algorithm

Crop-type identification is one of the most significant applications of agricultural remote sensing, and it is important for yield estimation prediction and field management. At present, crop identification using datasets from unmanned aerial vehicle (UAV) and satellite platforms have achieved state-of-the-art performances. However, accurate monitoring of small plants, such as the coffee flower, cannot be achieved using datasets from these platforms. With the development of time-lapse image acquisition technology based on ground-based remote sensing, a large number of small-scale plantation datasets with high spatial-temporal resolution are being generated, which can provide great opportunities for small target monitoring of a specific region. The main contribution of this paper is to combine the binarization algorithm based on OTSU and the convolutional neural network (CNN) model to improve coffee flower identification accuracy using the time-lapse images (i.e., digital images). A certain number of positive and negative samples are selected from the original digital images for the network model training. Then, the pretrained network model is initialized using the VGGNet and trained using the constructed training datasets. Based on the well-trained CNN model, the coffee flower is initially extracted, and its boundary information can be further optimized by using the extracted coffee flower result of the binarization algorithm. Based on the digital images with different depression angles and illumination conditions, the performance of the proposed method is investigated by comparison of the performances of support vector machine (SVM) and CNN model. Hence, the experimental results show that the proposed method has the ability to improve coffee flower classification accuracy. The results of the image with a 52.5° angle of depression under soft lighting conditions are the highest, and the corresponding Dice (F1) and intersection over union (IoU) have reached 0.80 and 0.67, respectively.

scholarly journals Deteksi Penyakit Pada Tanaman Mangga Dengan Citra Digital : Tinjauan Literatur Sistematis (SLR)

2020 ◽  
Vol 7 (1) ◽  
pp. 63
Author(s):  
Solikin Solikin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Literature Review ◽  
Decision Tree ◽  
Convolutional Neural Network ◽  
Systematic Literature Review ◽  
Digital Image ◽  
Digital Images ◽  
Disease Identification ◽  
Mango Leaves

Abstrak: Penelitian dengan melakukan tinjauan literatur sistematis (Sistematic Literatur Review-SLR) dilakukan untuk mempelajari berbagai teknik identifikasi penyakit pada daun dengan citra digital  sebagai tahapan untuk mendapatkan pemahaman mengenai teknik identifikasi penyakit pada daun mangga dengan citra digital. Produksi Mangga di Indonesia dari tahun 2014 – 2018 secara fluktuatif selalu mengalami peningkatan dan di tahun 2018 produksi mangga di Indonesia mencapai 2.624.783 ton, proses budidaya tanaman mangga tidak selamanya dapat terlepas dari serangan penyakit. Penyakit pada tanaman mangga disebabkan oleh jamur atau bakteri yang biasanya menyerang pada bagian akar, batang, kulit batang, ranting atau buah mangga. Jenis penyakit pada tanaman mangga adalah : Penyakit mangga (Jamur Gloesoporium), Penyakit Diplodia, Cendawan jelaga, Bercak karat merah, Kudis buah, Penyakit Blendok. Penyakit pada mangga memiliki berbagai gejala dan kadang sulit didiagnosis oleh petani dan untuk itu diperlukan keahlian untuk mendiagnosis penyakit pada tanaman mangga dan bagaimana cara penanggulangannya yang biasanya keahlian tersebut terdapat pada ahli patologi tanaman professional. Sehingga dibutuhkan suatu Teknologi IT dengan Sistem Cerdas yang dirancang untuk dapat mengidentifikasi secara otomatis penyakit tanaman mangga dan cara penanggulangannya berdasarkan gejala visual dengan menggunakan metode citra digital. Metode literatur review yang digunakan yaitu Compare, Contrast, Criticize, Synthesize dan Summarize. Metode Citra Digital yang dapat digunakan dalam identifikasi penyakit pada daun mangga adalah tahapan Image Acquisition, Preprocessing , Segmentation, Ekstraksi Fitur, Seleksi Fitur. Metode Klasifikasi yang dapat digunakan adalah SVM, Artificial Neural Network, Decision Tree, Convolutional Neural Network.   Kata kunci: citra digital, daun, penyakit mangga, tinjauan literatur sistematis     Abstract: Research by conducting a systematic literature review (Systematic Literature Review-SLR) was conducted to study various techniques of disease identification in leaves with digital images as a stage to gain an understanding of the techniques for disease identification on mango leaves with digital images. Mango production in Indonesia from 2014 - 2018 fluctuations has always increased and in 2018 mango production in Indonesia reached 2,624,783 tons, the process of mango cultivation is not always free from disease. Diseases of mango plants are caused by fungi or bacteria that usually attack the roots, stems, bark, twigs or mangoes. Types of diseases in mango plants are: Mango disease (Gloesoporium Fungus), Diplodia disease, sooty fungus, red rust spots, fruit scabies, Blendok disease. Diseases of mangoes have a variety of symptoms and are sometimes difficult to diagnose by farmers and expertise is needed to diagnose diseases on mango plants and how to overcome them which are usually found in professional plant pathologists. So that we need an IT Technology with an Intelligent System that is designed to be able to automatically identify mango plant diseases and how to overcome them based on visual symptoms using digital image methods. The literature review method used is Compare, Contrast, Criticize, Synthesize and Summarize. Digital image methods that can be used in the identification of diseases on mango leaves are the stages of Image Acquisition, Preprocessing, Segmentation, Feature Extraction, Feature Selection. Classification methods that can be used are SVM, Artificial Neural Network, Decision Tree, Convolutional Neural Network.

scholarly journals Automatic Rain Detection System Based on Digital Images of CCTV Cameras Using Convolutional Neural Network Method

2021 ◽  
Vol 893 (1) ◽  
pp. 012048
Author(s):  
Arif Luqman Hakim ◽  
Ristiana Dewi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Images ◽  
Detection System ◽  
Observation Data ◽  
Detection Process ◽  
Weather Information ◽  
Network Method ◽  
Testing Accuracy ◽  
Evaluation Accuracy

Abstract The Meteorology, Climatology and Geophysics Agency (BMKG) has a duty to provide weather information including rainfall. BMKG has several types of rainfall gauges, but these are not evenly distributed across regions. The solution to increase the density of rainfall observations is to use existing sources to obtain weather information. This research uses Closed Circuit Television (CCTV) that is spread across the Jakarta area to produce information on rainy conditions. The method used is the Convolutional Neural Network (CNN). The image from CCTV will be used for the training and testing process, so as to get the best accuracy model. The results of this model will be used for rain detection on CCTV digital images. The rain detection process is carried out automatically and in real time. The results of the rain detection process will be displayed on the map according to the location where the CCTV was installed. This research has succeeded in making a CNN model for rain detection with a training accuracy of 98.8% and a testing accuracy of 96.4%, as well as evaluating the BMKG observation data, so it has an evaluation accuracy of 96.7%.

Taylor-rider-based deep convolutional neural network for image forgery detection in 3D lighting environment

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
V. Vinolin ◽  
M. Sucharitha
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optimization Algorithm ◽  
Digital Images ◽  
Deep Convolutional Neural Network ◽  
Distance Measures ◽  
Forgery Detection ◽  
Content Type ◽  
Detection Techniques ◽  
Human Faces

PurposeWith the advancements in photo editing software, it is possible to generate fake images, degrading the trust in digital images. Forged images, which appear like authentic images, can be created without leaving any visual clues about the alteration in the image. Image forensic field has introduced several forgery detection techniques, which effectively distinguish fake images from the original ones, to restore the trust in digital images. Among several forgery images, spliced images involving human faces are more unsafe. Hence, there is a need for a forgery detection approach to detect the spliced images.Design/methodology/approachThis paper proposes a Taylor-rider optimization algorithm-based deep convolutional neural network (Taylor-ROA-based DeepCNN) for detecting spliced images. Initially, the human faces in the spliced images are detected using the Viola–Jones algorithm, from which the 3-dimensional (3D) shape of the face is established using landmark-based 3D morphable model (L3DMM), which estimates the light coefficients. Then, the distance measures, such as Bhattacharya, Seuclidean, Euclidean, Hamming, Chebyshev and correlation coefficients are determined from the light coefficients of the faces. These form the feature vector to the proposed Taylor-ROA-based DeepCNN, which determines the spliced images.FindingsExperimental analysis using DSO-1, DSI-1, real dataset and hybrid dataset reveal that the proposed approach acquired the maximal accuracy, true positive rate (TPR) and true negative rate (TNR) of 99%, 98.88% and 96.03%, respectively, for DSO-1 dataset. The proposed method reached the performance improvement of 24.49%, 8.92%, 6.72%, 4.17%, 0.25%, 0.13%, 0.06%, and 0.06% in comparison to the existing methods, such as Kee and Farid's, shape from shading (SFS), random guess, Bo Peng et al., neural network, FOA-SVNN, CNN-based MBK, and Manoj Kumar et al., respectively, in terms of accuracy.Originality/valueThe Taylor-ROA is developed by integrating the Taylor series in rider optimization algorithm (ROA) for optimally tuning the DeepCNN.

scholarly journals Content-Based Image Copy Detection Using Convolutional Neural Network

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2029
Author(s):  
Xiaolong Liu ◽  
Jinchao Liang ◽  
Zi-Yi Wang ◽  
Yi-Te Tsai ◽  
Chia-Chen Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Digital Images ◽  
Rapid Development ◽  
Network Technology ◽  
Copy Detection ◽  
Detection Scheme ◽  
The Past ◽  
Image Copy Detection ◽  
Feature Values

With the rapid development of network technology, concerns pertaining to the enhancement of security and protection against violations of digital images have become critical over the past decade. In this paper, an image copy detection scheme based on the Inception convolutional neural network (CNN) model in deep learning is proposed. The image dataset is transferred by a number of image processing manipulations and the feature values in images are automatically extracted for learning and detecting the suspected unauthorized digital images. The experimental results show that the proposed scheme takes on an extraordinary role in the process of detecting duplicated images with rotation, scaling, and other content manipulations. Moreover, the mechanism of detecting duplicate images via a convolutional neural network model with different combinations of original images and manipulated images can improve the accuracy and efficiency of image copy detection compared with existing schemes.

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