Infrared and visible image fusion methods and applications: A survey

A survey of infrared and visual image fusion methods

Early detection of liver disease is never easy, though it is one of the most important diseases on earth. This study, thus, attempts to achieve efficient early detection through a Multilayer Perceptron Neural Network (MLPNN) algorithm based on various decision tree algorithms such as See5 (C5.0), Chi square Automatic interaction detector (CHAID) and classification and regression tree (CART) with boosting technique. Five hundred and eighty-three records related to the Indian Liver Patient Dataset (ILPD) were collected from University of California, Irvine (UCI) repository dataset for the verification of the proposed work. The ILPD dataset is divided into 70% for the training stage and 30% for the testing stage. Several evaluation metrics, such as specificity, sensitivity, precision, false positive rate (FPR), false negative rate (FNR), F1, and accuracy, are applied in this study. These metrics are carried out in two phrases. In the first experiment, we observed that B-C5.0 method presents better performance than B-CHAID and B-CART methods. In the second experiment, a hybridization of B-C5.0 and MLPNN methods, namely MLPNNB-C5.0, indicates the highest rates of detection of liver disease compared to other algorithms. Results indicate that MLPNNB-CHAID method has the most innovative accuracy with a value of 14.57%. The proposed method is able to diagnose and classify the liver disease efficiently. We would argue that the proposed system can be useful as a medical data mining approach in order to provide an early diagnosis of liver disease.

Improving the Diagnosis of Liver Disease Using Multilayer Perceptron Neural Network and Boosted Decision Trees

Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Fusion of visible and infrared images using global entropy and gradient constrained regularization

An improved fusion algorithm for infrared and visible images based on multi-scale transform

The invention discloses an improved visual background extraction based movement target detection method. The improved visual background extraction based movement target detection method comprises the following steps of establishing a background model for every pixel point of a video image; calculating the similarity of the current pixel point and the background model; classifying into backgrounds if the current pixel point and the background model are similar and classifying into foregrounds if not; selecting whether to update the background models and display a movement target or not according to the times of continuous judgment of foregrounds. The improved visual background extraction based movement target detection method can remove Ghost caused by error judgment of the backgrounds into the foregrounds.

Improved visual background extraction based movement target detection method

The moving target detection algorithm based on the improved visual background extraction

The invention discloses a morphology and PCA (principal component analysis) based contourlet fusion method for infrared and visible light images The method includes: firstly, subjecting two images to morphology-hat transformation respectively and subjecting the two obtained images to contourlet decomposition to form a high-frequency image and a low-frequency image respectively; then, fusing the low-frequency image by a PCA method; fusing the high-frequency image by means of average gradient adaptive weighted processing; reconstructing a fused image by means of contourlet reverse transformation The morphology and PCA based contourlet fusion method for the infrared and visible light images is used for image fusion of the infrared and visible light images, so that the fused image is high in contrast ratio, rich in information and clear in texture detail

Wei Huang

Papers

An improved fusion algorithm for infrared and visible images based on multi-scale transform

Improved visual background extraction based movement target detection method

The moving target detection algorithm based on the improved visual background extraction

Morphology and PCA (principal component analysis) based contourlet fusion method for infrared and visible light images

The detection and prediction for oil spill on the sea based on the infrared images