scholarly journals Depth image correction for intel realsense depth camera

2020 ◽  
Vol 19 (2) ◽  
pp. 1021
Author(s):  
Hyun Jun Park ◽  
Kwang Baek Kim
Keyword(s):  
Image Processing ◽  
Infrared Radiation ◽  
Infrared Camera ◽  
High Accuracy ◽  
Depth Image ◽  
Depth Information ◽  
Depth Camera ◽  
Image Correction ◽  
Image Processing Techniques ◽  
Processing Techniques

<p><span>Intel RealSense depth camera provides depth image using infrared projector and infrared camera. Using infrared radiation makes it possible to measure the depth with high accuracy, but the shadow of infrared radiation makes depth unmeasured regions. Intel RealSense SDK provides a postprocessing algorithm to correct it. However, this algorithm is not enough to be used and needs to be improved. Therefore, we propose a method to correct the depth image using image processing techniques. The proposed method corrects the depth using the adjacent depth information. Experimental results showed that the proposed method corrects the depth image more accurately than the Intel RealSense SDK.</span></p>

scholarly journals Smart Dye Inspection System in Textile Industry

2020 ◽  
Vol 8 (6) ◽  
pp. 5061-5063
Keyword(s):  
Image Processing ◽  
Reference Material ◽  
Textile Industry ◽  
High Accuracy ◽  
Inspection System ◽  
Manual Operation ◽  
Image Processing Techniques ◽  
Manual Inspection ◽  
Processing Techniques

Inspection on the dyed material in the textile industry is facing a challenging task owing to the accurate measurement of the dye concentration added. Currently manual inspection is done. It consumes more time and less accurate. The proposed work provides a solution to above problem. The image of reference material (cloth) is captured and the features are extracted using image processing techniques. The color concentration of both the reference material and the test fabric is compared. If the dye concentration of the test fabric matches with the reference material, then it is a perfect dyed cloth whereas for mismatched samples, the concentration is to be adjusted is displayed. This smart dyeing inspection system reduces the manual operation and saves time and results in high accuracy.

scholarly journals Detection and Contouring of BAU-Kul using Image Processing Techniques

2019 ◽  
Vol 23 (2) ◽  
pp. 15-25
Author(s):  
MM Rahman ◽  
MMH Oliver
Keyword(s):  
Image Processing ◽  
High Accuracy ◽  
Contour Detection ◽  
Average Error ◽  
Market Prices ◽  
Image Processing Techniques ◽  
Random Samples ◽  
Automated Grading ◽  
Processing Techniques ◽  
Locally Grown

Automated grading and sorting of fruits during harvesting period are needed for securing better market prices. In order to introduce such automation facilities in Bangladesh, edging and contouring information of the locally grown fruits is important. This study reports the first endeavor towards the use of image processing techniques for a popular jujube variety (BAU-Kul) in Bangladesh. Image processing techniques were used for segmentation, and contouring on the basis of color Thresholding, edge detection and contour detection in Python-OpenCV software. Six random samples of BAU-Kul fruit were used for the research. Perimeter lengths obtained from the image analysis of the six samples ranged from 17.9 cm to 20.20 cm with an average of 19.29 (±1.02) cm. The measured lengths on the other hand, varied from 16.2 cm to 19.1 cm with an average of 17.75 (±1.3) cm. Consequently, the average error in calculation was limited to only 7.98%. This indicates the fact that images captured through mobile devices can be used for detection and contouring of BAU-Kul samples with fairly high accuracy (92.02%). These information provides a foreground basis of automation for the grading and sorting systems of BAU-Kul fruits in Bangladesh. Ann. Bangladesh Agric. (2019) 23(2) : 15-25

Three-Dimensional Structure of Rotavirus-Fab Complex

1990 ◽  
Vol 48 (1) ◽  
pp. 238-239
Author(s):  
B.V.V. Prasad ◽  
E. Marietta ◽  
J.W. Burns ◽  
M.K. Estes ◽  
W. Chiu
Keyword(s):  
Image Processing ◽  
Smooth Surface ◽  
Three Dimensional ◽  
Outer Shell ◽  
Dimensional Structure ◽  
Structural Studies ◽  
Cell Penetration ◽  
Electron Cryomicroscopy ◽  
Image Processing Techniques ◽  
Processing Techniques

Rotaviruses are spherical, double-shelled particles. They have been identified as a major cause of infantile gastroenteritis worldwide. In our earlier studies we determined the three-dimensional structures of double-and single-shelled simian rotavirus embedded in vitreous ice using electron cryomicroscopy and image processing techniques to a resolution of 40Å. A distinctive feature of the rotavirus structure is the presence of 132 large channels spanning across both the shells at all 5- and 6-coordinated positions of a T=13ℓ icosahedral lattice. The outer shell has 60 spikes emanating from its relatively smooth surface. The inner shell, in contrast, exhibits a bristly surface made of 260 morphological units at all local and strict 3-fold axes (Fig.l).The outer shell of rotavirus is made up of two proteins, VP4 and VP7. VP7, a glycoprotein and a neutralization antigen, is the major component. VP4 has been implicated in several important functions such as cell penetration, hemagglutination, neutralization and virulence. From our earlier studies we had proposed that the spikes correspond to VP4 and the rest of the surface is composed of VP7. Our recent structural studies, using the same techniques, with monoclonal antibodies specific to VP4 have established that surface spikes are made up of VP4.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Rice Panicle Blast Detection and Grading Based on Image Processing Techniques

2019 ◽  
Vol 7 (5) ◽  
pp. 165-168 ◽  
Author(s):  
Prabira Kumar Sethy ◽  
Swaraj Kumar Sahu ◽  
Nalini Kanta Barpanda ◽  
Amiya Kumar Rath
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Panicle Blast ◽  
Processing Techniques

Identification of Cucumber Leaf Disease using Image Processing Techniques

2018 ◽  
Vol 6 (6) ◽  
pp. 1493-1499
Author(s):  
Shrutika.C.Rampure . ◽  
Dr. Vindhya .P. Malagi ◽  
Dr. Ramesh Babu D.R
Keyword(s):  
Image Processing ◽  
Image Processing Techniques ◽  
Leaf Disease ◽  
Processing Techniques
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