Detection of Microconidia in Microscopy Images of Fusarium oxysporum f. sp. cubense Using Image Processing Techniques and Neural Networks

2020 ◽  
Author(s):  
Erinn Giannice T. Abigan ◽  
Luis Gabriel A. Cajucom ◽  
Josh Daniel L. Ong ◽  
Patricia Angela R. Abu ◽  
Ma. Regina Justina E. Estuar
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Fusarium Oxysporum ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Microscopy Images

scholarly journals Clay-Based Brick Porosity Estimation Using Image Processing Techniques

2019 ◽  
Vol 29 (1) ◽  
pp. 1226-1234
Author(s):  
Safa Jida ◽  
Hassan Ouallal ◽  
Brahim Aksasse ◽  
Mohammed Ouanan ◽  
Mohamed El Amraoui ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
Scanning Electron Microscopy ◽  
Computer Vision ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Porosity Estimation ◽  
Microscopy Images ◽  
Scanning Electron

Abstract This work intends to apprehend and emphasize the contribution of image-processing techniques and computer vision in the treatment of clay-based material known in Meknes region. One of the various characteristics used to describe clay in a qualitative manner is porosity, as it is considered one of the properties that with “kill or cure” effectiveness. For this purpose, we use scanning electron microscopy images, as they are considered the most powerful tool for characterising the quality of the microscopic pore structure of porous materials. We present various existing methods of segmentation, as we are interested only in pore regions. The results show good matching between physical estimation and Voronoi diagram-based porosity estimation.

The Application of Image Recognition Based on BP Neural Networks in Automatic Steel Rolling

2013 ◽  
Vol 764 ◽  
pp. 161-164
Author(s):  
Wei Jiang
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Image Recognition ◽  
Character Recognition ◽  
Video Image ◽  
Bp Neural Networks ◽  
Steel Rolling ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Frame Grabber

A BP neural networks is presented for billet character recognition. Firstly, by a series of image processing techniques, the character’feature in the billet character region of the video image gathered by frame grabber is abstracted. Secondly, the BP neural networks algorithm is employed for character recognition. Application results show that the image recognition based BP neural networks can performs well in billet character recognition, and the method presented is speedy, efficient and of high value in practice.

scholarly journals Fingerprints detection using neural networks suitable to physical changes of fingerprint

2017 ◽  
Vol 22 (2) ◽  
pp. 35-50
Author(s):  
Mabel Rocio Diaz Pineda ◽  
Maria Alejandra Dueñas ◽  
Karen Dayanna Acevedo
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Gabor Filter ◽  
Image Processing Techniques ◽  
Working Paper ◽  
Minutiae Extraction ◽  
Processing Techniques ◽  
Physical Changes ◽  
Segment Orientation

This working paper shows the results of finished research, using image processing techniques to improve the fingerprint obtained from a database, where the image is normalized and segmented to get only the section of the image with the fingerprint. Then, the Gabor filter is applied, and it corrects defects in ridges and valleys, allowing continuity. That way, if the fingerprint has a physical defect, the filter can correct it as long as the segment orientation to be correct. Once improved, the fingerprint, it is binarized and thinned for minutiae extraction. The false minutiae are filtered and eliminated in order to ensure the operation of the algorithm. Finally, it is necessary training with the minutiae of all fingerprints in the database, to individually determine which user belongs the fingerprint entered. The system has a reliability of 81% of the process, with the pre-processing part being crucial to guarantee the correct extraction of the characteristics of fingerprints.

Algorithms for Extraction of Nanowire Lengths and Positions From Optical Section Microscopy Image Sequence

2009 ◽  
Vol 9 (4) ◽  
Author(s):  
Tao Peng ◽  
Arvind Balijepalli ◽  
Satyandra K. Gupta ◽  
Thomas W. LeBrun
Keyword(s):  
Image Processing ◽  
Optical Tweezers ◽  
Image Sequence ◽  
Optical Section ◽  
Image Frame ◽  
Image Processing Techniques ◽  
Microscopy Image ◽  
Processing Techniques ◽  
Areas Of Interest ◽  
Microscopy Images

This paper presents algorithms for estimating length, location, and orientation of nanowires in a fluidic workspace using images obtained by optical section microscopy. Images containing multiple nanowires are first segmented to locate general areas of interest, which are then analyzed to determine discrete nanowire parameters. We use a set of image processing techniques to extract features of nanowire image patterns, e.g., boundary of nanowire, linear edges, and the intensity profile of nanowire’s diffraction fringes. The parameters of the features are then used to estimate length, 3D position, and 3D orientation of nanowires. A scene representing the workspace is reconstructed using the estimated attributes of nanowires, and it is constantly updated upon the capture of every image frame. We believe that the work described in this paper will be useful for assembly of nanowires using optical tweezers.

Efficient Image Denoising for Effective Digitization using Image Processing Techniques and Neural Networks

2016 ◽  
Vol 7 (4) ◽  
pp. 77-93 ◽  
Author(s):  
K.G. Srinivasa ◽  
B.J. Sowmya ◽  
D. Pradeep Kumar ◽  
Chetan Shetty
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Network Models ◽  
Morphological Operations ◽  
Neural Network Models ◽  
Image Processing Techniques ◽  
Optical Character ◽  
Processing Techniques

Vast reserves of information are found in ancient texts, scripts, stone tablets etc. However due to difficulty in creating new physical copies of such texts, knowledge to be obtained from them is limited to those few who have access to such resources. With the advent of Optical Character Recognition (OCR) efforts have been made to digitize such information. This increases their availability by making it easier to share, search and edit. Many documents are held back due to being damaged. This gives rise to an interesting problem of removing the noise from such documents so it becomes easier to apply OCR on them. Here the authors aim to develop a model that helps denoise images of such documents retaining on the text. The primary goal of their project is to help ease document digitization. They intend to study the effects of combining image processing techniques and neural networks. Image processing techniques like thresholding, filtering, edge detection, morphological operations, etc. will be applied to pre-process images to yield higher accuracy of neural network models.

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