Digital Image Vegetation Analysis with Machine Learning

2017 ◽  
Author(s):  
Guang Chen ◽  
Yang Liu ◽  
Nickolas Wergeles ◽  
Yi Shang ◽  
Joel Sartwell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Digital Image ◽  
Vegetation Analysis

Digital Image Soil Analysis based on Machine Learning

2021 ◽  
Author(s):  
Quchao Cheng ◽  
Jiaojie Li ◽  
Guochao Shen ◽  
Qingmin Du
Keyword(s):  
Machine Learning ◽  
Digital Image ◽  
Soil Analysis

scholarly journals Visual Computing and Machine Learning Techniques for Digital Forensics

2015 ◽  
Vol 22 (1) ◽  
pp. 128 ◽  
Author(s):  
Tiago Jose de Carvalho ◽  
Helio Pedrini ◽  
Anderson De Rezende Rocha
Keyword(s):  
Machine Learning ◽  
Digital Image ◽  
Digital Forensics ◽  
Research Area ◽  
Digital Image Forensics ◽  
Machine Learning Techniques ◽  
Open Problems ◽  
Visual Computing ◽  
Science Community ◽  
New Research

It is impressive how fast science has improved day by day in so many different fields. In special, technology advances are shocking so many people bringing to their reality facts that previously were beyond their imagination. Inspired by methods earlier presented in scientific fiction shows, the computer science community has created a new research area named Digital Forensics, which aims at developing and deploying methods for fighting against digital crimes such as digital image forgery.This work presents some of the main concepts associated with Digital Forensics and, complementarily, presents some recent and powerful techniques relying on Computer Graphics, Image Processing, Computer Vision and Machine Learning concepts for detecting forgeries in photographs. Some topics addressed in this work include: sourceattribution, spoofing detection, pornography detection, multimedia phylogeny, and forgery detection. Finally, this work highlights the challenges and open problems in Digital Image Forensics to provide the readers with the myriad opportunities available for research.

scholarly journals Machine Learning Based Statistical Analysis of Emotion Recognition using Facial Expression

2020 ◽  
Vol 11 (1) ◽  
pp. 1-8
Author(s):  
Aqib Ali ◽  
Jamal Abdul Nasir ◽  
Muhammad Munawar Ahmed ◽  
Samreen Naeem ◽  
Sania Anam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Statistical Analysis ◽  
Decision Tree ◽  
Facial Expression ◽  
Emotion Recognition ◽  
Digital Image ◽  
Search Algorithm ◽  
Statistical Feature ◽  
Tree Algorithms ◽  
Two Phases

Background: Humans can deliver many emotions during a conversation. Facial expressions show information about emotions. Objectives: This study proposed a Machine Learning (ML) approach based on a statistical analysis of emotion recognition using facial expression through a digital image. Methodology: A total of 600 digital image datasets divided into 6 classes (Anger, Happy, Fear, Surprise, Sad, and Normal) was collected from publicly available Taiwan Facial Expression Images Database. In the first step, all images are converted into a gray level format and 4 Regions of Interest (ROIs) are created on each image, so the total image dataset gets divided in 2400 (600 x 4) sub-images. In the second step, 3 types of statistical features named texture, histogram, and binary feature are extracted from each ROIs. The third step is a statistical feature optimization using the best-first search algorithm. Lastly, an optimized statistical feature dataset is deployed on various ML classifiers. Results: The analysis part was divided into two phases: firstly boosting algorithms-based ML classifiers (named as LogitBoost, AdaboostM1, and Stacking) which obtained 94.11%, 92.15%, and 89.21% accuracy, respectively. Secondly, decision tree algorithms named J48, Random Forest, and Random Committee were obtained with 97.05%, 93.14%, and 92.15% accuracy, respectively. Conclusion: It was observed that decision tree based J48 classifiers gave 97.05% classification accuracy.

scholarly journals A Review on Automatic Soil Classification in Digital Image Processing

2021 ◽  
Vol 9 (VIII) ◽  
pp. 449-455
Author(s):  
Shraddha Shivhare
Keyword(s):  
Machine Learning ◽  
Image Processing ◽  
False Alarm ◽  
Digital Image Processing ◽  
Digital Image ◽  
Processing Time ◽  
Soil Classification ◽  
Research Papers ◽  
Digital World ◽  
Precision Rate

Soil classification is an essential piece of geology. However, many examinations have assessed the precision and consistency of the soil classification using various techniques. This examination starts by evaluating the verifiable advancement of soil classification science. The verifiable audit contextualizes the wordings and the speculations of soil development factors, which supported soil classification frameworks. This paper is intended to review some research papers on soil classification and analyze the limitations of implemented techniques by their parameters. In the age of digital world, it is beneficial to obtain the information from image without any hassle. Machine learning is an approach through which we can obtain the better level of accuracy and minimize the false alarm rate. But machine learning requires so many samples through which we can observe the correct precision that also requires much storage that may takes much processing time that reduces the feasibility of the system. We have to train a system with limited number of samples with high iterations that produces higher precision rate with minimal errors.

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