scholarly journals Color Analysis and Image Processing Applied in Agriculture

2018 ◽  
Author(s):  
Jesús Raúl Martínez Sandoval ◽  
Ernesto Martínez Sandoval ◽  
Miguel Enrique Martínez Rosas ◽  
Manuel Moisés Miranda Velasco
Keyword(s):  
Image Processing ◽  
Color Analysis

scholarly journals Detection of Cardiac Tissues using K-means Analysis Methods in Nuclear Medicine Images

2021 ◽  
Vol 9 (A) ◽  
pp. 1272-1276
Author(s):  
Yousif Abdallah
Keyword(s):  
Image Processing ◽  
Nuclear Medicine ◽  
Nuclear Cardiology ◽  
Detection Rate ◽  
Color Space ◽  
Heart Tissue ◽  
Color Analysis ◽  
Cardiac Tissues ◽  
Analysis Methods ◽  
Cardiac Disorders

BACKGROUND: Nuclear cardiology uses to diagnose the cardiac disorders such as ischemic and inflammation disorders. In cardiac scintigraphy, unraveling closely adjacent tissues in the image are challenging issue. AIM: The aim of the study is to detect of cardiac tissues using K-means analysis methods in nuclear medicine images. This study also aimed to reduce the existence of fleck noise that disturbs the contrast and make its analysis more difficult. METHODS: Thus, digital image processing uses to increase the detection rate of myocardium easily using its color-based algorithms. In this study, color-based K-means was used. The scintographs were converted into color space presentation. Then, each pixel in the image was segmented using color analysis algorithms. RESULTS: The segmented scintograph was displayed in distinct fresh image. The proposed technique defines the myocardial tissues and borders precisely. Both exactness rate and recall reckoning were calculated. The results were 97.3 + 8.46 (p > 0.05). CONCLUSION: The proposed technique offered recognition of the heart tissue with high exactness amount.

Microalgae biomass quantification by digital image processing and RGB color analysis

2014 ◽  
Vol 27 (1) ◽  
pp. 205-209 ◽  
Author(s):  
Mohammad H. Sarrafzadeh ◽  
Hyun-Joon La ◽  
Jae-Yon Lee ◽  
Dae-Hyun Cho ◽  
Sang-Yoon Shin ◽  
...  
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Digital Image ◽  
Color Analysis ◽  
Microalgae Biomass ◽  
Biomass Quantification

scholarly journals A Four Stage Image Processing Algorithm for Detecting and Counting of Bagworm, Metisa plana Walker (Lepidoptera: Psychidae)

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1265
Author(s):  
Mohd Najib Ahmad ◽  
Abdul Rashid Mohamed Shariff ◽  
Ishak Aris ◽  
Izhal Abdul Halin
Keyword(s):  
Image Processing ◽  
Oil Palm ◽  
Learning Algorithm ◽  
Insect Pest ◽  
Processing Algorithm ◽  
Training Dataset ◽  
Detection Accuracy ◽  
Color Analysis ◽  
Image Processing Algorithm ◽  
Deep Learning Algorithm

The bagworm is a vicious leaf eating insect pest that threatens the oil palm plantations in Malaysia. The economic impact from defoliation of approximately 10% to 13% due to bagworm attack might cause about 33% to 40% yield loss over 2 years. Due to this, monitoring and detecting of bagworm populations in oil palm plantations is required as the preliminary steps to ensure proper planning of control actions in these areas. Hence, the development of an image processing algorithm for detection and counting of Metisa plana Walker, a species of Malaysia’s local bagworm, using image segmentation has been researched and completed. The color and shape features from the segmented images for real time object detection showed an average detection accuracy of 40% and 34%, at 30 cm and 50 cm camera distance, respectively. After some improvements on training dataset and marking detected bagworm with bounding box, a deep learning algorithm with Faster Regional Convolutional Neural Network (Faster R-CNN) algorithm was applied leading to the percentage of the detection accuracy increased up to 100% at a camera distance of 30 cm in close conditions. The proposed solution is also designed to distinguish between the living and dead larvae of the bagworms using motion detection which resulted in approximately 73–100% accuracy at a camera distance of 30 cm in the close conditions. Through false color analysis, distinct differences in the pixel count based on the slope was observed for dead and live pupae at 630 nm and 940 nm, with the slopes recorded at 0.38 and 0.28, respectively. The higher pixel count and slope correlated with the dead pupae while the lower pixel count and slope, represented the living pupae.

scholarly journals Color Analysis and Image Processing Applied in Agriculture

2018 ◽  
Author(s):  
Ernesto Martínez Sandoval ◽  
Miguel Enrique Martínez Rosas ◽  
Jesús Raúl Martínez Sandoval ◽  
Manuel Moises Miranda Velasco ◽  
Humberto Cervantes De Ávila
Keyword(s):  
Image Processing ◽  
Color Analysis

Principles of image processing in machine vision systems for the color analysis of minerals

2014 ◽  
Author(s):  
Daria B. Petukhova ◽  
Elena V. Gorbunova ◽  
Aleksandr N. Chertov ◽  
Valery V. Korotaev
Keyword(s):  
Image Processing ◽  
Machine Vision ◽  
Color Analysis ◽  
Vision Systems

scholarly journals Footprint-Based Health Monitoring Database using Raspberry PI

2020 ◽  
Vol 8 (6) ◽  
pp. 851-854
Keyword(s):  
Image Processing ◽  
Health Monitoring ◽  
Processing System ◽  
Processing Technique ◽  
Health Condition ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Data Allocation ◽  
Color Analysis ◽  
The Web

The health monitoring of the person can be done in the different ways. The health of the patient can be determined by the image processing technique. The biometric parameters can gather the details of the health condition of the patient. The digital image processing can be applied in the various filed such as medical, geology, research etc., In this paper they proposes the foot print technology this can capture the foot print of the patient by using the web cam. The captured image can be analyzed by using the shape and the dimension analysis. The foot print can reads the each person identity. Based upon the identity and the numbers the image processing system is implemented. It uses the raspberry pi as the main part. The data which is captured by the web cam can be stored in the SD card. The data allocation is done in the memory path. The classification of the data is takes place by using the data separation algorithm. The color analysis can takes place a significant place based upon the color we can able to classify the foot print and makes it for further analysis. There are several steps can be took place the image acquisition, edge detection, feature extraction, pattern recognition, pattern matching. The matched image can be provided as the better result. Based upon the result the health condition can be predicted. This method is highly effective and accurate when compared to other method

Image Processing for the Detection of Diabetic Retinopathy and Diabetic Macular Edema

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
R. Manjula Sri ◽  
K.M. M. Rao
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Macular Edema ◽  
Blood Vessels ◽  
Diabetic Macular Edema ◽  
Severity Index ◽  
Diabetic Patients ◽  
Color Analysis ◽  
Hard Exudates ◽  
Patients With Diabetes

Diabetic retinopathy (DR) and diabetic macular edema (DME) are common microvascular retinal diseases in patients with diabetes. The diabetic patients may have a sudden and devastating impact on visual acuity, in the long run leading to blindness. Advanced stages of DR are characterized by the growth of abnormal retinal blood vessels secondary to ischemia. These blood vessels grow in an attempt to supply oxygenated blood to the hypoxic retina. At any time during the progression of DR, patients with diabetes can also develop DME, which involves retinal thickening in the macular area. In the present paper, algorithms are developed to detect DR and DME. For detecting DR the abnormalities in the retina blood vessels are detected by classifying the common abnormalities namely microaneurisms, hard exudates, heammorages and cotton wool spots. DME is detected by finding the nearness of Hard exudate to macula. The macula and hard exudates are localized using image processing techniques. Severity of DME is assessed based on the nearest exudates, their area and color analysis. The algorithm is tested with 65 DR and DME images with severity index 0, 1 and 2 from MESSIDOR database.

scholarly journals Color Classifier for Symptomatic Soybean Seeds Using Image Processing

Plant Disease ◽  
1999 ◽  
Vol 83 (4) ◽  
pp. 320-327 ◽  
Author(s):  
Irfan S. Ahmad ◽  
John F. Reid ◽  
Marvin R. Paulsen ◽  
James B. Sinclair
Keyword(s):  
Image Processing ◽  
Color Analysis ◽  
Knowledge Domain ◽  
Linear Discriminant ◽  
Grading Systems ◽  
Color Differences ◽  
Classifier Performance ◽  
Feature Based ◽  
Distinct Color ◽  
Processing Techniques

Symptoms associated with fungal damage, viral diseases, and immature soybean (Glycine max) seeds were characterized using image processing techniques. A Red, Green, Blue (RGB) color feature-based multivariate decision model discriminated between asymptomatic and symptomatic seeds for inspection and grading. The color analysis showed distinct color differences between the asymptomatic and symptomatic seeds. A model comprising six color features including averages, minimums, and variances for RGB pixel values was developed for describing the seed symptoms. The color analysis showed that color alone did not adequately describe some of the differences among symptoms. Overall classification accuracy of 88% was achieved using a linear discriminant function with unequal priors for asymptomatic and symptomatic seeds with highest probability of occurrence. Individual classification accuracies were asymptomatic 97%, Alternaria spp. 30%, Cercospora spp. 83%, Fusarium spp. 62%, green immature seeds 91%, Phomopsis spp. 45%, soybean mosaic potyvirus (black) 81%, and soybean mosaic potyvirus (brown) 87%. The classifier performance was independent of the year the seed was sampled. The study was successful in developing a color classifier and a knowledge domain based on color for future development of intelligent automated grain grading systems.

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