scholarly journals Geometric Model for Human Body Orientation Classification

2015 ◽  
Vol 9 (1) ◽  
pp. 29
Author(s):  
Igi Ardiyanto
Keyword(s):  
Random Forest ◽  
Human Body ◽  
Geometric Model ◽  
Texture Features ◽  
Random Forest Classifier ◽  
The Body ◽  
Body Orientation ◽  
Texture Model ◽  
Comparison Results ◽  
Orientation Class

This  paper proposes  an approach  for cal- culating  and estimating  human body orientation  using geometric model. A novel framework integrating gradient shape and texture model of the human body orientation is proposed.  The gradient  is a natural way for describing the human  shapes, while the texture  explains the body characteristic. The framework  is then combined with the random  forest classifier to obtain a robust  class  differ- ence  of the human body orientation. Experiments and comparison results are provided to show the advantages of our system over state-of-the-art. For both modeled and un-modeled gradient-texture  features with random forest classifier, they achieve the highest accuracy on separating each human orientation   class, respectively  56.9% and 67.3% for TUD-Stadtmitte  dataset.

scholarly journals Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest

2020 ◽  
Vol 12 (21) ◽  
pp. 3504
Author(s):  
Xingrong Li ◽  
Chenghai Yang ◽  
Wenjiang Huang ◽  
Jia Tang ◽  
Yanqin Tian ◽  
...  
Keyword(s):  
Random Forest ◽  
Root Rot ◽  
Vegetation Index ◽  
Texture Features ◽  
Spectral Model ◽  
Spectral Indices ◽  
Texture Model ◽  
Cotton Root ◽  
Cotton Fields ◽  
Sentinel 2

Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation.

scholarly journals Sickle cell segmentation and classification for thalassemia aid diagnosis

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 1185
Author(s):  
Yen-Siang Leow ◽  
Kok-Why Ng ◽  
Yih-Jian Yoong ◽  
Seng-Beng Ng
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Decision Tree ◽  
Complete Blood Count ◽  
Similarity Index ◽  
Random Forest Classifier ◽  
The Body ◽  
Cell Segmentation ◽  
Support Vector ◽  
Morphological Operations

Background: Thalassemia is a hereditary blood disease in which abnormal red blood cells (RBCs) carry insufficient oxygen throughout the body. Conventional methods of thalassemia detection through a complete blood count (CBC) test and peripheral blood smear image still possess a lot of weaknesses. Methods: This paper proposes a hybrid segmentation method to segment the RBCs. It incorporates adaptive thresholding and canny edge method to segment the RBCs. Morphological operations are performed to clean the leftovers. Shape and texture features are extracted using the segmented masks and the gray level co-occurrence matrix. Data imbalance treatment is used for solving the imbalance cell type class in distribution. In the data resampling layer, the synthetic minority oversampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and random over sampling (ROS) are performed and evaluated using the decision tree and logistic regression. In the classification layer, the decision tree, random forest classifier and support vector machine (SVM) are assessed and compared for the best performance in classification. Results:The proposed method outperforms the other methods in the image segmentation layer with the structural similarity index measure (SSIM) of 89.88%. In the data resampling layer, ADASYN is employed as it is more accurate than the SMOTE and ROS. The random forest classifier is chosen at the classification layer as it is more accurate than the decision tree and support vector machine (SVM). Conclusions:The proposed method is tested on the latest dataset of erythrocyteIDB3 and it solves the issues of imbalanced data due to the insufficient cell classes.

Breast Cancer Detection Using Random Forest Classifier

2021 ◽  
pp. 85-98
Author(s):  
Pavithra Suchindran ◽  
Vanithamani R. ◽  
Judith Justin
Keyword(s):  
Breast Cancer ◽  
Random Forest ◽  
Cancer Detection ◽  
Region Of Interest ◽  
Texture Features ◽  
Speckle Noise ◽  
Random Forest Classifier ◽  
Breast Cancer Detection ◽  
K Nearest Neighbors ◽  
Cad System

Breast cancer is the second most prevalent type of cancer among women. Breast ultrasound (BUS) imaging is one of the most frequently used diagnostic tools to detect and classify abnormalities in the breast. To improve the diagnostic accuracy, computer-aided diagnosis (CAD) system is helpful for breast cancer detection and classification. Normally, a CAD system consists of four stages: pre-processing, segmentation, feature extraction, and classification. In this chapter, the pre-processing step includes speckle noise removal using speckle reducing anisotropic diffusion (SRAD) filter. The goal of segmentation is to locate the region of interest (ROI) and active contour-based segmentation and fuzzy C means segmentation (FCM) are used in this work. The texture features are extracted and fed to a classifier to categorize the images as normal, benign, and malignant. In this work, three classifiers, namely k-nearest neighbors (KNN) algorithm, decision tree algorithm, and random forest classifier, are used and the performance is compared based on the accuracy of classification.

scholarly journals Classification of Sea Ice Types in Sentinel-1 SAR Data Using Convolutional Neural Networks

2020 ◽  
Vol 12 (13) ◽  
pp. 2165 ◽  
Author(s):  
Hugo Boulze ◽  
Anton Korosov ◽  
Julien Brajard
Keyword(s):  
Random Forest ◽  
Sea Ice ◽  
Learning Algorithm ◽  
Computing Time ◽  
Texture Features ◽  
Random Forest Classifier ◽  
Synthetic Aperture ◽  
First Year ◽  
Sar Data

A new algorithm for classification of sea ice types on Sentinel-1 Synthetic Aperture Radar (SAR) data using a convolutional neural network (CNN) is presented. The CNN is trained on reference ice charts produced by human experts and compared with an existing machine learning algorithm based on texture features and random forest classifier. The CNN is trained on two datasets in 2018 and 2020 for retrieval of four classes: ice free, young ice, first-year ice and old ice. The accuracy of our classification is 90.5% for the 2018-dataset and 91.6% for the 2020-dataset. The uncertainty is a bit higher for young ice (85%/76% accuracy in 2018/2020) and first-year ice (86%/84% accuracy in 2018/2020). Our algorithm outperforms the existing random forest product for each ice type. It has also proved to be more efficient in computing time and less sensitive to the noise in SAR data. The code is publicly available.

Early Diabetes Discovery From Tongue Images

2020 ◽  
Author(s):  
Safia Naveed ◽  
Geetha G ◽  
Leninisha S
Keyword(s):  
Human Body ◽  
Early Stage ◽  
Texture Features ◽  
The Body ◽  
Body Parts ◽  
Early Diabetes ◽  
Non Invasive ◽  
Internal Structures ◽  
Invasive Method ◽  
Sugar Levels

Abstract Medical imaging shows the internal structures hidden in the skin and bone to diagnose the disease. Diabetes mellitus (DM) is a metabolic disorder that causes high blood sugar levels due to the inadequate secretion of insulin or the body does not respond properly to the secreted insulin. This paper proposes a non-invasive method to detect DM at an early stage based on the physiognomy extracted from tongue images. The tongue extends to identify the disease of a human body. However, unpredictable response of the human body parts such as the stomach, pancreas, liver and intestines revert in the tongue. The changes in the tongue ensure the dereliction of the internal organs of the human being. The changes are difference in the color and surface of the tongue. Processing of tongue image is done by fractional order Darwinian particle swarm optimization (FODPSO) algorithm. The system framework involves obtaining the image, alluring of the image, identifying the texture and color feature and finally classified as normal or diabetic. In this paper, the authors propose to diagnose DM at an early stage from tongue digital image. The tongue image is acquired and processed with FODPSO to extract edge and texture features. Tongue reflects and diagnoses diabetes in a person.

The Performance Improvement on 4-FB Face using Random Forest Classifier

2018 ◽  
Vol 10 (5) ◽  
pp. 1-12
Author(s):  
B. Nassih ◽  
A. Amine ◽  
M. Ngadi ◽  
D. Naji ◽  
N. Hmina
Keyword(s):  
Random Forest ◽  
Performance Improvement ◽  
Random Forest Classifier
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