Top Cited Articles in Signal & Image Processing 2021-2022

Target Detection and Classification Improvements using Contrast Enhanced 16-bit Infrared Videos

Chiman Kwan and David Gribben, Applied Research, LLC, USA

https://aircconline.com/sipij/V12N1/12121sipij03.pdf

February 2021 | Cited by 4

Abstract

In our earlier target detection and classification papers, we used 8-bit infrared videos in the Defense Systems Information Analysis Center(DSIAC) video dataset. In this paper, we focus on how we can improve the target detection and classification results using 16-bit videos. One problem with the 16-bit videos is that some image frames have very low contrast. Two methods were explored to improve upon previous detection and classification results. The first method used to improve contrast was effectively the same as the baseline 8-bit video data but using the 16-bit raw data rather than the 8-bit data taken from the avi files. The second method used was a second order histogram matching algorithm that preserves the 16-bit nature of the videos while providing normalization and contrast enhancement. Results showed the second order histogram matching algorithm improved the target detection using You Only Look Once (YOLO) and classificationusing Residual Network (ResNet) performance. The average precision (AP) metric in YOLO was improved by 8%. This is quite significant. The overall accuracy (OA) of ResNet has been improved by 12%. This is also very significant.

Keywords

Deep learning, mid-wave infrared (MWIR) videos, target detection and classification, contrast enhancement, YOLO, ResNet.

Mixed Spectra for Stable Signals from Discrete Observations

Rachid Sabre, University of Burgundy, France

https://aircconline.com/sipij/V12N5/12521sipij02.pdf

October 2021 | Cited by 2

Abstract

This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.

Keywords

Spectral density, stable processes, periodogram, smoothing estimate, aliasing.

Using Distance Measure based Classification in Automatic Extraction of Lungs Cancer Nodules for Computer Aided Diagnosis

Maan Ammar1, Muhammad Shamdeen2, MazenKasedeh2, Kinan Mansour3 and Waad Ammar3 , 1AL Andalus University for Medical Sciences, Syria, 2Damascus University, Syria, 3Al Andalus University Hospital, Syria

https://aircconline.com/sipij/V12N3/12321sipij03.pdf

June 2021 | Cited by 2

Abstract

We introduce in this paper a reliable method for automatic extraction of lungs nodules from CT chest images and shed the light on the details of using the Weighted Euclidean Distance (WED) for classifying lungs connected components into nodule and not-nodule. We explain also using Connected Component Labeling (CCL) in an effective and flexible method for extraction of lungs area from chest CT images with a wide variety of shapes and sizes. This lungs extraction method makes use of, as well as CCL, some morphological operations. Our tests have shown that the performance of the introduce method is high. Finally, in order to check whether the method works correctly or not for healthy and patient CT images, we tested the method by some images of healthy persons and demonstrated that the overall performance of the method is satisfactory.

Keywords

Nodules classification, lungs cancer, morphological operators, weighted Euclidean distance, nodules extraction.

A Comparative Study of Machine Learning Algorithms for EEG Signal Classification

Anam Hashmi, Bilal Alam Khan and Omar Farooq, Aligarh Muslim University, India

https://aircconline.com/sipij/V12N6/12621sipij03.pdf

December 2021 | Cited by 1

Abstract

In this paper, different machine learning algorithms such as Linear Discriminant Analysis, Support vector machine (SVM), Multi-layer perceptron, Random forest, K-nearest neighbour, and Autoencoder with SVM have been compared. This comparison was conducted to seek a robust method that would produce good classification accuracy. To this end, a robust method of classifying raw Electroencephalography (EEG) signals associated with imagined movement of the right hand and relaxation state, namely Autoencoder with SVM has been proposed. The EEG dataset used in this research was created by the University of Tubingen, Germany. The best classification accuracy achieved was 70.4% with SVM through feature engineering. However, our prosed method of autoencoder in combination with SVM produced a similar accuracy of 65% without using any feature engineering technique. This research shows that this system of classification of motor movements can be used in a Brain-Computer Interface system (BCI) to mentally control a robotic device or an exoskeleton.

Keywords

EEG. Machine learning. BCI. Motor Imagery signals. Random Forest.

Sensing Method for Two-Target Detection in Time-Constrained Vector Poisson Channel

Muhammad Fahad and Daniel R. Fuhrmann, Michigan Technological University, USA

https://aircconline.com/sipij/V12N6/12621sipij01.pdf

December 2021 | Cited by 1

Abstract

It is an experimental design problem in which there are two Poisson sources with two possible and known rates, and one counter. Through a switch, the counter can observe the sources individually or the counts can be combined so that the counter observes the sum of the two. The sensor scheduling problem is to determine an optimal proportion of the available time to be allocated toward individual and joint sensing, under a total time constraint. Two different metrics are used for optimization: mutual information between the sources and the observed counts, and probability of detection for the associated source detection problem. Our results, which are primarily computational, indicate similar but not identical results under the two cost functions.

Keywords

sensor scheduling, vector Poisson channels.

General Purpose Image Tampering Detection using Convolutional Neural Network and Local Optimal Oriented Pattern (LOOP)

Ali Ahmad Aminu1, 2 and Nwojo Nnanna Agwu1 , 1Nile University of Nigeria, Nigeria, 2Gombe State University, Nigeria

https://aircconline.com/sipij/V12N2/12221sipij02.pdf

April 2021 | Cited by 1

Abstract

Digital image tampering detection has been an active area of research in recent times due to the ease with which digital image can be modified to convey false or misleading information. To address this problem, several studies have proposed forensics algorithms for digital image tampering detection. While these approaches have shown remarkable improvement, most of them only focused on detecting a specific type of image tampering. The limitation of these approaches is that new forensic method must be designed for each new manipulation approach that is developed. Consequently, there is a need to develop methods capable of detecting multiple tampering operations. In this paper, we proposed a novel general purpose image tampering scheme based on CNNs and Local Optimal Oriented Pattern (LOOP) which is capable of detecting five types of image tampering in both binary and multiclass scenarios. Unlike the existing deep learning techniques which used constrained pre-processing layers to suppress the effect of image content in order to capture image tampering traces, our method uses LOOP features, which can effectively subdue the effect image content, thus, allowing the proposed CNNs to capture the needed features to distinguish among different types of image tampering. Through a number of detailed experiments, our results demonstrate that the proposed general purpose image tampering method can achieve high detection accuracies in individual and multiclass image tampering detections respectively and a comparative analysis of our results with the existing state of the arts reveals that the proposed model is more robust than most of the exiting methods.

Keywords

Image Tampering, General purpose Tampering Detection, Convolutional Neural Network, Local Optimal Oriented Pattern.