Sweet Citrus Fruit Detection in Thermal Images Using Fuzzy Image Processing

2019 ◽  
pp. 182-193
Author(s):  
Ingrid Lorena Argote Pedraza ◽  
John Faber Archila Diaz ◽  
Renan Moreira Pinto ◽  
Marcelo Becker ◽  
Mario Luiz Tronco
Keyword(s):  
Image Processing ◽  
Citrus Fruit ◽  
Thermal Images ◽  
Fuzzy Image

Sweet citrus fruit detection in thermal images using fuzzy image processing

2019 ◽  
Author(s):  
Ingrid Lorena Argote Pedraza ◽  
John Faber Archila Diaz ◽  
Renan Moreira Pinto ◽  
Marcelo Becker ◽  
Mario Luiz Tronco
Keyword(s):  
Image Processing ◽  
Citrus Fruit ◽  
Thermal Images ◽  
Fuzzy Image

Modern Technology for Image processing and Computer vision -A Review

2018 ◽  
Vol 1 (2) ◽  
pp. 17-23
Author(s):  
Takialddin Al Smadi
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Augmented Reality ◽  
Video Compression ◽  
Video Processing ◽  
Vision System ◽  
Citrus Fruit ◽  
Modern Technology ◽  
Image And Video Processing ◽  
Depth Video

This survey outlines the use of computer vision in Image and video processing in multidisciplinary applications; either in academia or industry, which are active in this field.The scope of this paper covers the theoretical and practical aspects in image and video processing in addition of computer vision, from essential research to evolution of application.In this paper a various subjects of image processing and computer vision will be demonstrated ,these subjects are spanned from the evolution of mobile augmented reality (MAR) applications, to augmented reality under 3D modeling and real time depth imaging, video processing algorithms will be discussed to get higher depth video compression, beside that in the field of mobile platform an automatic computer vision system for citrus fruit has been implemented ,where the Bayesian classification with Boundary Growing to detect the text in the video scene. Also the paper illustrates the usability of the handed interactive method to the portable projector based on augmented reality.   © 2018 JASET, International Scholars and Researchers Association

scholarly journals An Anatomical Thermal 3D Model in Preclinical Research: Combining CT and Thermal Images

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1200
Author(s):  
Franziska Schollemann ◽  
Carina Barbosa Pereira ◽  
Stefanie Rosenhain ◽  
Andreas Follmann ◽  
Felix Gremse ◽  
...  
Keyword(s):  
Image Processing ◽  
Temperature Profile ◽  
3D Models ◽  
Animal Suffering ◽  
Preclinical Research ◽  
Thermal Images ◽  
Animal Trials ◽  
Superficial Skin ◽  
Ct Data ◽  
3D Information

Even though animal trials are a controversial topic, they provide knowledge about diseases and the course of infections in a medical context. To refine the detection of abnormalities that can cause pain and stress to the animal as early as possible, new processes must be developed. Due to its noninvasive nature, thermal imaging is increasingly used for severity assessment in animal-based research. Within a multimodal approach, thermal images combined with anatomical information could be used to simulate the inner temperature profile, thereby allowing the detection of deep-seated infections. This paper presents the generation of anatomical thermal 3D models, forming the underlying multimodal model in this simulation. These models combine anatomical 3D information based on computed tomography (CT) data with a registered thermal shell measured with infrared thermography. The process of generating these models consists of data acquisition (both thermal images and CT), camera calibration, image processing methods, and structure from motion (SfM), among others. Anatomical thermal 3D models were successfully generated using three anesthetized mice. Due to the image processing improvement, the process was also realized for areas with few features, which increases the transferability of the process. The result of this multimodal registration in 3D space can be viewed and analyzed within a visualization tool. Individual CT slices can be analyzed axially, sagittally, and coronally with the corresponding superficial skin temperature distribution. This is an important and successfully implemented milestone on the way to simulating the internal temperature profile. Using this temperature profile, deep-seated infections and inflammation can be detected in order to reduce animal suffering.

Classifications of citrus fruit using image processing -GLCM parameters

2015 ◽  
Author(s):  
Chandan Kumar ◽  
Siddharth Chauhan ◽  
R.Narmadha Alla ◽  
Harika Mounica gurram
Keyword(s):  
Image Processing ◽  
Citrus Fruit

Innovative yield mapping system using hyperspectral and thermal imaging for precision tree crop management

2014 ◽  
Author(s):  
W.S. Lee ◽  
Victor Alchanatis ◽  
Asher Levi
Keyword(s):  
Image Registration ◽  
Navigation System ◽  
Thermal Imaging ◽  
Imaging System ◽  
Citrus Fruit ◽  
Mobile Platform ◽  
Yield Estimation ◽  
Yield Mapping ◽  
Thermal Images ◽  
Mapping System

Original objectives and revisions – The original overall objective was to develop, test and validate a prototype yield mapping system for unit area to increase yield and profit for tree crops. Specific objectives were: (1) to develop a yield mapping system for a static situation, using hyperspectral and thermal imaging independently, (2) to integrate hyperspectral and thermal imaging for improved yield estimation by combining thermal images with hyperspectral images to improve fruit detection, and (3) to expand the system to a mobile platform for a stop-measure- and-go situation. There were no major revisions in the overall objective, however, several revisions were made on the specific objectives. The revised specific objectives were: (1) to develop a yield mapping system for a static situation, using color and thermal imaging independently, (2) to integrate color and thermal imaging for improved yield estimation by combining thermal images with color images to improve fruit detection, and (3) to expand the system to an autonomous mobile platform for a continuous-measure situation. Background, major conclusions, solutions and achievements -- Yield mapping is considered as an initial step for applying precision agriculture technologies. Although many yield mapping systems have been developed for agronomic crops, it remains a difficult task for mapping yield of tree crops. In this project, an autonomous immature fruit yield mapping system was developed. The system could detect and count the number of fruit at early growth stages of citrus fruit so that farmers could apply site-specific management based on the maps. There were two sub-systems, a navigation system and an imaging system. Robot Operating System (ROS) was the backbone for developing the navigation system using an unmanned ground vehicle (UGV). An inertial measurement unit (IMU), wheel encoders and a GPS were integrated using an extended Kalman filter to provide reliable and accurate localization information. A LiDAR was added to support simultaneous localization and mapping (SLAM) algorithms. The color camera on a Microsoft Kinect was used to detect citrus trees and a new machine vision algorithm was developed to enable autonomous navigations in the citrus grove. A multimodal imaging system, which consisted of two color cameras and a thermal camera, was carried by the vehicle for video acquisitions. A novel image registration method was developed for combining color and thermal images and matching fruit in both images which achieved pixel-level accuracy. A new Color- Thermal Combined Probability (CTCP) algorithm was created to effectively fuse information from the color and thermal images to classify potential image regions into fruit and non-fruit classes. Algorithms were also developed to integrate image registration, information fusion and fruit classification and detection into a single step for real-time processing. The imaging system achieved a precision rate of 95.5% and a recall rate of 90.4% on immature green citrus fruit detection which was a great improvement compared to previous studies. Implications – The development of the immature green fruit yield mapping system will help farmers make early decisions for planning operations and marketing so high yield and profit can be achieved. 

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 Automated processing of thermal imaging to detect COVID-19

2020 ◽  
Author(s):  
Rafael Y. Brzezinski ◽  
Neta Rabin ◽  
Nir Lewis ◽  
Racheli Peled ◽  
Ariel Kerpel ◽  
...  
Keyword(s):  
Image Processing ◽  
Low Income ◽  
Thermal Imaging ◽  
Point Of Care ◽  
Area Under The Curve ◽  
Screening Tools ◽  
Imaging Device ◽  
Thermal Images ◽  
Image Processing Algorithms ◽  
Processing Algorithms

ABSTRACTRapid and sensitive screening tools for SARS-CoV-2 infection are essential to limit the spread of COVID-19 and to properly allocate national resources. Here, we developed a new point-of-care, non-contact thermal imaging tool to detect COVID-19, based on image-processing algorithms and machine learning analysis. We captured thermal images of the back of individuals with and without COVID-19 using a portable thermal camera that connects directly to smartphones. Our novel image processing algorithms automatically extracted multiple texture and shape features of the thermal images and achieved an area under the curve (AUC) of 0.85 in detecting COVID-19 with up to 92% sensitivity. Thermal imaging scores were inversely correlated with clinical variables associated with COVID-19 disease progression. We show, for the first time, that a hand-held thermal imaging device can be used to detect COVID-19. Non-invasive thermal imaging could be used to screen for COVID-19 in out-of-hospital settings, especially in low-income regions with limited imaging resources.

Detection of Diabetic Retinopathy and Maculopathy in Eye Fundus Images Using Fuzzy Image Processing

2015 ◽  
pp. 379-388 ◽  
Author(s):  
Sarni Suhaila Rahim ◽  
Vasile Palade ◽  
Chrisina Jayne ◽  
Andreas Holzinger ◽  
James Shuttleworth
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Fundus Images ◽  
Fuzzy Image
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