Character Reorganization and Categorization using Hybrid Algorithms by Scanned Documents

2019 ◽  
Vol 8 (6S2) ◽  
pp. 943-948
Keyword(s):  
Research Work ◽  
Hybrid Algorithms ◽  
Research Topic ◽  
Image Processing Techniques ◽  
Digital Books ◽  
Otsu Method ◽  
Challenging Research ◽  
Processing Techniques ◽  
Scanned Documents ◽  
Day By Day

Detection and reorganization of text may save a lot of time while reproducing old books text and its chapters. This is really challenging research topic as different books may have different font types and styles. The digital books and eBooks reading habit is increasing day by day and new documents are producing every day. So in order to boost the process the text reorganization using digital image processing techniques can be used. This research work is using hybrid algorithms and morphological algorithms. For sample we have taken an letter pad where the text and images are separated using algorithms. The another objective of this research is to increase the accuracy of recognized text and produce accurate results. This research worked on two different concepts, first is concept of Pixel-level thresholding processing and another one is Otsu Method thresholding.

HUMAN FACE IMAGE PROCESSING TECHNIQUES

2001 ◽  
Vol 01 (02) ◽  
pp. 197-215 ◽  
Author(s):  
HONG YAN
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Facial Feature ◽  
Face Image ◽  
Computer Algorithms ◽  
Human Face ◽  
Feature Location ◽  
Image Processing Techniques ◽  
Face Image Processing ◽  
Processing Techniques

Human face image processing techniques have many applications, such as in security operations, entertainment, medical imaging and telecommunications. In this paper, we provide an overview of existing computer algorithms for face detection and facial feature location, face recognition, image compression and animation. We also discuss limitations of current methods and research work needed in the future.

scholarly journals Analysis of Buffer Zones of Limestone Mines Areas using Digital Image Processing Techniques

2020 ◽  
Vol 8 (6) ◽  
pp. 5431-5437
Keyword(s):  
Image Processing ◽  
Land Use ◽  
Digital Image Processing ◽  
Digital Image ◽  
Land Use Changes ◽  
Research Work ◽  
Mining Activities ◽  
Buffer Zones ◽  
Image Processing Techniques ◽  
Processing Techniques

The economic growth of any country crucially depends on the mining activity of that country. The mining activities require huge land for the extraction of mineral from the earth. The recent government policy imposing the systematic mapping of the land use and land cover in and around the mines. In the present study, work, the analysis of land used and land covered was carried out at Malkapur limestone mines. This study discussed the brief mapping of the buffer zones buffer zones areas in by using digital image processing techniques. This research work demonstrated the changes happened in and around mines for the buffer radius of 1 km, 5 km and 10 km. In this study it was found that there were no significant changes observed in land use which intern implies that mining activities are not having any impact in land use changes. Further, in this study, not much variation was reported against the forest land and water bodies situated in and around the mines

scholarly journals Quantum-Assisted Retinal Drusen Detection Algorithm using Entropy-Based Image Processing Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 2251-2256
Keyword(s):  
Image Processing ◽  
Composite System ◽  
Detection Method ◽  
Research Work ◽  
Detection Algorithm ◽  
Image Processing Techniques ◽  
Sensitivity Specificity ◽  
Processing Techniques ◽  
Retinal Fundus ◽  
Retinal Drusen

Drusen identification is the fundamental operation in the automated diagnosis of eye diseases. Manual and automatic detection of the drusen in the retinal fundus images has been developed recently in the classical manner only. This work provides the quantum-based retinal drusen detection method using entropy-based image processing techniques. This algorithm is the composite system of two channels, classical and quantum channels for the preprocessing and drusen detection respectively. This research work has been evaluated with the databases of DRIVE, STARE, MESSIDOR, E-Optha-Ex and ONH-Hunter. This quantum-based approach will be analyzed with the results of the existing classical methods and proves its efficiency from the calculations of sensitivity, specificity, accuracy and execution time.

scholarly journals Detection and Classification of Rice Diseases: An Automated Approach Using Textural Features

2019 ◽  
Vol 38 (1) ◽  
pp. 239-250 ◽  
Author(s):  
Komal Bashir ◽  
Mariam Rehman ◽  
Mehwish Bari
Keyword(s):  
Image Processing ◽  
Research Work ◽  
Recall Rate ◽  
Misclassification Rate ◽  
Maximum Efficiency ◽  
Support Vector ◽  
Image Processing Techniques ◽  
Effective Decision Making ◽  
Processing Techniques

Image processing techniques are widely used for the detection and classification of diseases for various plants. The structure of the plant and appearance of the disease on the plant pose a challenge for image processing. This research implements SVM (Support Vector Machine) based image-processing approach to analyze and classify three of the rice crop diseases. The process consists of two phases, i.e. training phase and disease prediction phase. The approach identifies disease on the leaf using trained classifier. The proposed research work optimizes SVM parameters (gamma, nu) for maximum efficiency. The results show that the proposed approach achieved 94.16% accuracy with 5.83% misclassification rate, 91.6% recall rate and 90.9% precision. These findings were compared with image processing techniques discussed in review of literature. The results of comparison conclude that the proposed methodology yields high accuracy percentage as compared to the other techniques. The results obtained can help the development of an effective software solution by incorporating image processing and collaboration features. This may facilitate the farmers and other bodies in effective decision making to efficiently protect the rice crops from substantial damage. While considering the findings of this research, the presented technique may be considered as a potential solution for adding image processing techniques to KM (Knowledge Management) systems.

scholarly journals Applications of Human Biometrics in Digital Image Processing

2020 ◽  
Vol 5 (7) ◽  
pp. 1273-1276
Author(s):  
Anshul Kumar Singh ◽  
Brajesh Kumar Singh
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Research Work ◽  
Security Systems ◽  
Image Processing Techniques ◽  
Work Related ◽  
Microscope Imaging ◽  
Processing Techniques ◽  
Biometric Features

Digital image processing is the trending topic of research in recent time and big amount of research work related to Biometric features is done and currently it achieved good amount of accuracy. Biometric features is used for security, verification and recognition purpose. This paper is a showcase of how security systems can be developed by using biometric features of human like face, fingerprint and iris, etc. It can be used for the purpose of identification, recognition and Authentication and it is also applicable for making software for image preparation in bioscience laboratories that make use of scanned or digitally photographed images. The widespread use of such image processing techniques using photography and microscope imaging across the natural science with particular attention being paid to research in cell and molecular bioscience. This paper is a review of various methods trending to the field of biometric applications on biotechnologies.

Abrasion Measurement of Spun Yarns by Image Analysis and Artificial Intelligence Techniques

2014 ◽  
Vol 18 (3) ◽  
pp. 61-68
Author(s):  
D. Semnani
Keyword(s):  
Research Work ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
The Body ◽  
Neural Nets ◽  
Image Processing Techniques ◽  
New Methods ◽  
Spun Yarns ◽  
Processing Techniques ◽  
First Time

Previously, to evaluate the abrasion of spun yarns, ASTM standard D1379-64 (1970) was applied and valid until 1975. After that, much research work has been carried out to study the abrasion resistance of yarns by using different methods. Recently, new methods based on image processing techniques have been developed. In this research, first, to calculate the abrasion indexes for an image of yarns that are wrapped side by side, the inputs for a back propagation neural network are provided and abrasion destruction indexes are the output. The training of the net is done with data from model images. Moreover, the network has been tested with those model images. To design the model images, attempts are made to simulate various types of defects which are made by abrasion on the body of yarn. After that, groups of spun and filament yarns are tested with both a standard and the new intelligent method and the results are compared. The results prove that trained neural nets have the ability to evaluate the images of yarns trained to the net before; in addition, they can evaluate the images which are inserted into the net for the first time.

Detection crack in image using Otsu method and multiple filtering in image processing techniques

Optik ◽  
2016 ◽  
Vol 127 (3) ◽  
pp. 1030-1033 ◽  
Author(s):  
Ahmed Mahgoub Ahmed Talab ◽  
Zhangcan Huang ◽  
Fan Xi ◽  
Liu HaiMing
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Otsu Method ◽  
Processing Techniques

On Sonar Image Processing Techniques for Detection and Localization of Underwater Objects

2012 ◽  
Vol 236-237 ◽  
pp. 509-514 ◽  
Author(s):  
Li Yuan Weng ◽  
Min Li ◽  
Zhen Bang Gong
Keyword(s):  
Image Processing ◽  
Median Filter ◽  
Localization System ◽  
Image Processing Techniques ◽  
Sonar Image ◽  
Otsu Method ◽  
Tracing Algorithm ◽  
Underwater Object ◽  
Processing Techniques ◽  
Detection And Localization

This paper presents an underwater object detection and localization system based on multi-beam sonar image processing techniques. Firstly, sonar data flow collected by multi-beam sonar is processed by median filter to reduce noise. Secondly, an improved adaptive thresholding method based on Otsu method is proposed to extract foreground objects from sonar image. Finally, the object’s contour is calculated by Moore-Neighbor Tracing algorithm to locate the object. Experiments show that the proposed system can detect underwater objects quickly and the figure out the position of objects accurately.

scholarly journals Categorization of Vehicle and Motion Analysis Using Vehicle Features

2018 ◽  
Vol 7 (3.10) ◽  
pp. 184
Author(s):  
Ms S.Vanithamai ◽  
Dr S.Purushothaman
Keyword(s):  
Image Processing ◽  
Object Detection ◽  
Motion Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Research Work ◽  
Vehicle Classification ◽  
Image Processing Techniques ◽  
Video Frames ◽  
Processing Techniques

This research work can identify the vehicle and classify the vehicle using the vehicle features such as shape, color etc., The parameters extracted from the vehicle classification are based on movement of the vehicle are classified as static, movement variation in the successive video frames are used to identify the hazardness of the vehicle. Digital Image processing techniques are used in the object detection. 

scholarly journals Image To Speech Synthesizer with Reference to Assamese Numerals

2019 ◽  
Vol 9 (1) ◽  
pp. 900-905
Keyword(s):  
Image Processing ◽  
Template Matching ◽  
Research Work ◽  
Second Phase ◽  
Digital Form ◽  
Speech Synthesizer ◽  
Image Processing Techniques ◽  
Digital World ◽  
Matching Technique ◽  
Processing Techniques

In this research work we have shown the methodology for converting printed Assamese numerals to its corresponding utterance. We have implemented as an initial effort which will read only four digit numerals. We are using Image processing techniques to convert an image of Assamese numerals into textual/digital form. In the second phase the numerals will be pronounced as a number by Google speaker. In this system, images are stored in a dataset and then inputted data is compared with the dataset image using template matching technique. After recognition of the text output will be displayed as a speech waveform. This work has many applications in today’s digital world

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