scholarly journals METODE PENCUPLIKAN NILAI ECHO CITRA RADAR *.PNG DENGAN REFERENSI SPASIAL DAN KOMBINASI WARNA RGB

2017 ◽  
Vol 18 (1) ◽  
pp. 25
Author(s):  
Purwadi Purwadi ◽  
Lutfi Fitriano
Keyword(s):  
Image Processing ◽  
Image Data ◽  
Numerical Data ◽  
Original Data ◽  
Gray Area ◽  
Radar Echo ◽  
Mapping Process ◽  
Color Equation ◽  
Color Data ◽  
Echo Radar

IntisariData meteorologi yang berupa citra/gambar sulit dianalisis dan dikombinasikan dengan data lain. Dalam karya tulis ini akan dijelaskan metode pencuplikan citra/gambar radar yang dipublikasikan oleh BMKG menjadi data teks. Proses pengolahan terdiri dari dua tahap yaitu proses pemetaan setiap pixel dalam citra radar menjadi koordinat bumi (latitude dan longitude) dan penentuan nilai echo radar (dBZ). Dari legenda pada citra radar didapatkan 9 pola warna RGB yang digunakan sebagai penentu nilai dBZ setiap pixel dalam citra radar. Hasil pengolahan citra radar berupa data teks yang terdiri dari longitude, latitude, dan nilai dBZ. Untuk membandingkan dengan data asli, data radar teks hasil pengolahan ditampilkan pada Website Global Informasion System (WebGIS). Warna data radar pada WebGIS ditentukan dengan persamaan warna sebagai fungsi dari nilai dBZ. Secara kualitatif, hasil perbandingan gambar radar asli dengan data numerik yang ditampilkan pada WebGIS menunjukkan bahwa hasil data numerik cukup presisi pada posisi longitude dan latitude. Namun, dari segi nilai numerik echo radar (dBZ) yang dihasilkan terdeteksi kurang akurat pada batas awan karena latar belakang gambar yang berwarna hitam. Selain itu, pada keterangan nama kota dan batas wilayah yang berwarna abu-abu menujukkan pixel yang kosong walaupun disekelilingnya terlihat adanya awan, sehingga diperlukan penelitian lebih lanjut untuk mengatasi permasalahan tersebut dengan menambahkan filter untuk mengoreksi nilai pixel pada batas gambar dan teknik interpolasi untuk mengisi kekosongan nilai pada area pixel. AbstractMeteorological data in the form of image has difficulty in further analysis such as to combine the data with other data sources. In this research, the proposed method for converting image data into texts using image processing for BMKG data provided is presented. The processing scenarios consist of two main steps; mapping process of every pixel of the images into the earth coordinate (latitude and longitude) step and radar echo values estimation in dBZ step. From the analysis, the 9 color patterns of RGB are obtained and used as the dBZ justification tool for the pixel color of radar image. The results of this image processing step are the texts data of latitude, longitude and the radar echo values in dBZ. In order to compare the analysis results with the original data, the processing data are then reshown to global information system website (WebGIS). The radar color data on WebGIS is determined based on color equation as a function of the echo radar. Qualitatively, the results of this comparison show that the numerical data results are precise in terms of longitude and latitude positions. However, in terms of numerical values echo radar (dBZ), the results perform less accurate especially on the boundary of the cloud due to the black color of background image. In addition, the description of the city name and the border of the gray area show the data are empty although by visual inspection there are surrounding clouds. Thus, further research is needed to solve the problem by adding filters to correct the pixel value at the image boundary and applying the interpolation technique to fill the void value in the pixel area. 

Digital differential hysteresis iimage processing displays what the microscope acquires but the eye can't see

1995 ◽  
Vol 53 ◽  
pp. 642-643
Author(s):  
Klaus-Ruediger Peters
Keyword(s):  
Image Processing ◽  
Visual System ◽  
High Precision ◽  
Image Data ◽  
Processing Technology ◽  
Output Data ◽  
Contrast Resolution ◽  
Pixel Intensity ◽  
Data Reading ◽  
Hysteresis Properties

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.

Information and estimation in image processing

1987 ◽  
Vol 45 ◽  
pp. 14-17
Author(s):  
B. Roy Frieden
Keyword(s):  
Image Processing ◽  
Optical System ◽  
Large Amplitude ◽  
Communication Theory ◽  
Image Data ◽  
Direct Approach ◽  
Ill Posed

Despite the skill and determination of electro-optical system designers, the images acquired using their best designs often suffer from blur and noise. The aim of an “image enhancer” such as myself is to improve these poor images, usually by digital means, such that they better resemble the true, “optical object,” input to the system. This problem is notoriously “ill-posed,” i.e. any direct approach at inversion of the image data suffers strongly from the presence of even a small amount of noise in the data. In fact, the fluctuations engendered in neighboring output values tend to be strongly negative-correlated, so that the output spatially oscillates up and down, with large amplitude, about the true object. What can be done about this situation? As we shall see, various concepts taken from statistical communication theory have proven to be of real use in attacking this problem. We offer below a brief summary of these concepts.

scholarly journals Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 816
Author(s):  
Pingping Liu ◽  
Xiaokang Yang ◽  
Baixin Jin ◽  
Qiuzhan Zhou
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Diabetic Retinopathy ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Network Model ◽  
Rapid Development ◽  
Image Data ◽  
Lesion Detection ◽  
Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

scholarly journals InstantDL: an easy-to-use deep learning pipeline for image segmentation and classification

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Dominik Jens Elias Waibel ◽  
Sayedali Shetab Boushehri ◽  
Carsten Marr
Keyword(s):  
Image Processing ◽  
Deep Learning ◽  
Specific Problem ◽  
State Of The Art ◽  
Image Data ◽  
Semantic Segmentation ◽  
Parameter Tuning ◽  
Cellular Processes ◽  
Minimal Effort ◽  
Instance Segmentation

Abstract Background Deep learning contributes to uncovering molecular and cellular processes with highly performant algorithms. Convolutional neural networks have become the state-of-the-art tool to provide accurate and fast image data processing. However, published algorithms mostly solve only one specific problem and they typically require a considerable coding effort and machine learning background for their application. Results We have thus developed InstantDL, a deep learning pipeline for four common image processing tasks: semantic segmentation, instance segmentation, pixel-wise regression and classification. InstantDL enables researchers with a basic computational background to apply debugged and benchmarked state-of-the-art deep learning algorithms to their own data with minimal effort. To make the pipeline robust, we have automated and standardized workflows and extensively tested it in different scenarios. Moreover, it allows assessing the uncertainty of predictions. We have benchmarked InstantDL on seven publicly available datasets achieving competitive performance without any parameter tuning. For customization of the pipeline to specific tasks, all code is easily accessible and well documented. Conclusions With InstantDL, we hope to empower biomedical researchers to conduct reproducible image processing with a convenient and easy-to-use pipeline.

Off-line classification of Polish vowel spectra using artificial neural networks

2004 ◽  
Vol 34 (1) ◽  
pp. 37-52
Author(s):  
Wiktor Jassem ◽  
Waldemar Grygiel
Keyword(s):  
Neural Networks ◽  
Numerical Data ◽  
Word List ◽  
Original Data ◽  
Data Matrix ◽  
Probabilistic Neural Networks ◽  
Independent Variable ◽  
The Individual ◽  
Vowel Phoneme

The mid-frequencies and bandwidths of formants 1–5 were measured at targets, at plus 0.01 s and at minus 0.01 s off the targets of vowels in a 100-word list read by five male and five female speakers, for a total of 3390 10-variable spectrum specifications. Each of the six Polish vowel phonemes was represented approximately the same number of times. The 3390* 10 original-data matrix was processed by probabilistic neural networks to produce a classification of the spectra with respect to (a) vowel phoneme, (b) identity of the speaker, and (c) speaker gender. For (a) and (b), networks with added input information from another independent variable were also used, as well as matrices of the numerical data appropriately normalized. Mean scores for classification with respect to phonemes in a multi-speaker design in the testing sets were around 95%, and mean speaker-dependent scores for the phonemes varied between 86% and 100%, with two speakers scoring 100% correct. The individual voices were identified between 95% and 96% of the time, and classifications of the spectra for speaker gender were practically 100% correct.

TEK KATLI DUVAR DEFORMASYONUNUN GÖRÜNTÜ İŞLEME TEKNİKLERİ KULLANILARAK BELİRLENMESİ

2021 ◽  
Vol 8 (17) ◽  
pp. 21-29
Author(s):  
Abdulkerim İLGÜN ◽  
Ahmad Javid ZIA ◽  
Vahdettin DEMİR ◽  
Abdullah MÜSEVİTOĞLU ◽  
Sadrettin SANCIOĞLU
Keyword(s):  
Image Processing ◽  
Single Layer ◽  
Numerical Data ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Initial State ◽  
Image Digitization ◽  
Sliding Test ◽  
Natural Stones ◽  
Experimental Values

Image processing technique has been used frequently in the solution of engineering problems recently. In engineering studies, photographs are taken at certain intervals between the initial state of the material and the state after the change, and changes during the study are observed with the Image processing technique. Based on these photos, the change is transferred to numerical data and the change of the material is observed thanks to these data. Package program systems are used in Image processing technique applications. But these systems are quite expensive systems. In this study, a simpler and feasible system has been developed. The initial sliding test was carried out on 9 single-layer wall systems with natural stones in 20 * 30 * 10 cm dimensions. The displacement values formed on the walls under load during the experiments were measured with the help of potentiometric linear rulers. At the same time, photographs were taken at certain intervals from the baseline to the conclusion of the experiment. The photographs were digitized in the ArcGIS program and the changes on the wall were converted into numerical data. Experimental data and data obtained by photographs were compared. As a result of this comparison, 84% similarity is observed between experimental values and analytical values. It is observed that the image digitization application performed as a result of the study yielded very successful results. In this context, it is believed that the use of this system will be both fast and economically beneficial in larger scale experiments and the number of data.

AUTOMATIC SEGMENTATION OF BREAST TISSUE THERMAL IMAGES

2018 ◽  
Vol 30 (03) ◽  
pp. 1850024 ◽  
Author(s):  
Zeinab Heidari ◽  
Mehrdad Dadgostar ◽  
Zahra Einalou
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Imaging ◽  
Breast Tissue ◽  
Early Stage ◽  
Automatic Segmentation ◽  
Image Data ◽  
Thermal Images ◽  
Early Stages ◽  
Invasive Method

Breast cancer is one of the main causes of women’s death. Thermal breast imaging is one the non-invasive method for cancer at early stage diagnosis. In contrast to mammography this method is cheap and painless and it can be used during pregnancy while ionized beams are not used. Specialists are seeking new ways to diagnose the cancer in early stages. Segmentation of the breast tissue is one of the most indispensable stages in most of the cancer diagnosis methods. By the advancement of infrared precise cameras, new and fast computers and nouvelle image processing approaches, it is feasible to use thermal imaging for diagnosis of breast cancer at early stages. Since the breast form is different in individuals, image segmentation is a hard task and semi-automatic or manual methods are usual in investigations. In this research the image data base of DMR-IR has been utilized and a now automatic approach has been proposed which does not need learning. Data were included 159 gray images used by dynamic protocol (132 healthy and 27 patients). In this study, by combination of different image processing methods, the segmentation of thermal images of the breast tissues have been completed automatically and results show the proper performance of recommended method.

scholarly journals Introducing Zoomify Image

2007 ◽  
Vol 26 (1) ◽  
pp. 48 ◽  
Author(s):  
Adam Smith
Keyword(s):  
Image Processing ◽  
Response Times ◽  
Image Data ◽  
Web Browser ◽  
Cornell University ◽  
High Resolution Images ◽  
Cross Platform ◽  
Processing Software ◽  
Image Processing Software ◽  
The Web

Zoomify Image is a mature product for easily publishing large, high-resolution images on the Web. End users view these images with existing Webbrowser software as quickly as they do normal, downsampled images. A Flash-based Zoomifyer client asynchronously streams image data to the Web browser as needed, resulting in response times approaching those of desktop applications using minimal bandwidth. The author, a librarian at Cornell University and the principal architect of a small, open-source company, worked closely with Zoomify to produce a cross-platform, opensource implementation of that company’s image-processing software and discusses how to easily deploy the product into a widely used Webpublishing environment. Limitations are also discussed as are areas of improvement and alternatives.

scholarly journals PlantCV v2: Image analysis software for high-throughput plant phenotyping

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4088 ◽  
Author(s):  
Malia A. Gehan ◽  
Noah Fahlgren ◽  
Arash Abbasi ◽  
Jeffrey C. Berry ◽  
Steven T. Callen ◽  
...  
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Image Analysis ◽  
Image Data ◽  
Plant Phenotyping ◽  
Analysis Software ◽  
Computational Tools ◽  
Landmark Identification ◽  
Plant Image ◽  
Leaf Segmentation

Systems for collecting image data in conjunction with computer vision techniques are a powerful tool for increasing the temporal resolution at which plant phenotypes can be measured non-destructively. Computational tools that are flexible and extendable are needed to address the diversity of plant phenotyping problems. We previously described the Plant Computer Vision (PlantCV) software package, which is an image processing toolkit for plant phenotyping analysis. The goal of the PlantCV project is to develop a set of modular, reusable, and repurposable tools for plant image analysis that are open-source and community-developed. Here we present the details and rationale for major developments in the second major release of PlantCV. In addition to overall improvements in the organization of the PlantCV project, new functionality includes a set of new image processing and normalization tools, support for analyzing images that include multiple plants, leaf segmentation, landmark identification tools for morphometrics, and modules for machine learning.

scholarly journals Unsupervised Text Feature Learning via Deep Variational Auto-encoder

2020 ◽  
Vol 49 (3) ◽  
pp. 421-437
Author(s):  
Genggeng Liu ◽  
Lin Xie ◽  
Chi-Hua Chen
Keyword(s):  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Image Data ◽  
Original Data ◽  
Feature Representation ◽  
High Dimensional ◽  
Learning To Learn ◽  
Text Feature ◽  
Reduction Methods ◽  
Low Dimensional

Dimensionality reduction plays an important role in the data processing of machine learning and data mining, which makes the processing of high-dimensional data more efficient. Dimensionality reduction can extract the low-dimensional feature representation of high-dimensional data, and an effective dimensionality reduction method can not only extract most of the useful information of the original data, but also realize the function of removing useless noise. The dimensionality reduction methods can be applied to all types of data, especially image data. Although the supervised learning method has achieved good results in the application of dimensionality reduction, its performance depends on the number of labeled training samples. With the growing of information from internet, marking the data requires more resources and is more difficult. Therefore, using unsupervised learning to learn the feature of data has extremely important research value. In this paper, an unsupervised multilayered variational auto-encoder model is studied in the text data, so that the high-dimensional feature to the low-dimensional feature becomes efficient and the low-dimensional feature can retain mainly information as much as possible. Low-dimensional feature obtained by different dimensionality reduction methods are used to compare with the dimensionality reduction results of variational auto-encoder (VAE), and the method can be significantly improved over other comparison methods.

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