Drunk Selfie Detection

The goal of this project was to extract key features from photographs of faces and use machine learning to classify subjects as either sober or drunk. To do this we analyzed photographs of 53 subjects after drinking wine and extracted key features which we used to classify drunkenness. We used random forest machine learning to achieve 81% accuracy. We built an android application that using our classifiers to estimate the subjects drunkenness from a selfie

Remote Sensing for Non‐Technical Survey

This chapter describes the research activities of the Royal Military Academy on remote sensing applied to mine action. Remote sensing can be used to detect specific features that could lead to the suspicion of the presence, or absence, of mines. Work on the automatic detection of trenches and craters is presented here. Land cover can be extracted and is quite useful to help mine action. We present here a classification method based on Gabor filters. The relief of a region helps analysts to understand where mines could have been laid. Methods to be a digital terrain model from a digital surface model are explained. The special case of multi‐spectral classification is also addressed in this chapter. Discussion about data fusion is also given. Hyper‐spectral data are also addressed with a change detection method. Synthetic aperture radar data and its fusion with optical data have been studied. Radar interferometry and polarimetry are also addressed

Applicability of UAV-based optical imagery and classification algorithms for detecting pine wilt disease at different infection stages

As a quarantine disease with a rapid spread tendency in the context of climate change, accurate detection and location of pine wilt disease (PWD) at different infection stages is critical for maintaining forest health and being highly productivity. In recent years, unmanned aerial vehicle (UAV)-based optical remote-sensing images have provided new instruments for timely and accurate PWD monitoring. Numerous corresponding analysis algorithms have been proposed for UAV-based image classification, but their applicability of detecting different PWD infection stages has not yet been evaluated under a uniform conditions and criteria. This research aims to systematically assess the performance of multi-source images for detecting different PWD infection stages, analyze effective classification algorithms, and further analyze the validity of thermal images for early detection of PWD. In this study, PWD infection was divided into four stages: healthy, chlorosis, red and gray, and UAV-based hyperspectral (HSI), multispectral (MSI), and MSI with a thermal band (MSI&TIR) datasets were used as the data sources. Spectral analysis, support vector machine (SVM), random forest (RF), two- and three-dimensional convolutional network (2D- and 3D-CNN) algorithms were applied to these datasets to compare their classification abilities. The results were as follows: (I) The classification accuracy of the healthy, red, and gray stages using the MSI dataset was close to that obtained when using the MSI&TIR dataset with the same algorithms, whereas the HSI dataset displayed no obvious advantages. (II) The RF and 3D-CNN algorithms were the most accurate for all datasets (RF: overall accuracy = 94.26%, 3D-CNN: overall accuracy = 93.31%), while the spectral analysis method is also valid for the MSI&TIR dataset. (III) Thermal band displayed significant potential in detection of the chlorosis stage, and the MSI&TIR dataset displayed the best performance for detection of all infection stages. Considering this, we suggest that the MSI&TIR dataset can essentially satisfy PWD identification requirements at various stages, and the RF algorithm provides the best choice, especially in actual forest investigations. In addition, the performance of thermal imaging in the early monitoring of PWD is worthy of further investigation. These findings are expected to provide insight into future research and actual surveys regarding the selection of both remote sensing datasets and data analysis algorithms for detection requirements of different PWD infection stages to detect the disease earlier and prevent losses

Processing remotely sensed data for geological content over a part of the Barberton Greenstone Belt, Republic of South Africa.

Various methods and techniques developed by researchers worldwide for enhancement and processing ATM, MSS· and TM remotely sensed data are tested. on LANDSAT 5 Thematic Mapper data from a part of the Barberton Greenstone Belt straddling the border between the Republic of South Africa and the Kingdom of Swaziland. Various enhancement techniques employed to facilitate the extraction of structural features and lineaments, and the findings Of the ensuing photogeologlcal interpretation are compared with existing geological maps~ Methods for the detection of zones of hydrothermal alteration. are also considered. The reflectance from vegetation, both natural and cultivated, and the possible reduction of the interference caused by this reflectance, are considered in detail. Partial unmixing of reflectances through the use of various methods and techniques, some of which are readily available from the literature, are performed and its effectiveness tested. Since large areas within the study area are covered by plantations, the interfereiice from the two types of vegetation present (i.e. natural and cultivated), were initially considered separately. In an attempt to isolate the forested areas from the natural vegetation, masks derived through image classification were used to differentially enhance the various features. Results indicate that the use of any particular method to the exclusion of all others will seriously limit the scope of conclusions possible through interpretation of the information present. Enhancement of information in one domain will inadvertently lead to the suppression of information from one or more of the coexisting domains. A series of results from a sequence of procedures interpreted in parallel will in every case produce information of a higher decision making quality.AC201

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

Hyperspectral Imaging and Their Applications in the Nondestructive Quality Assessment of Fruits and Vegetables

Over the past decade, hyperspectral imaging has been rapidly developing and widely used as an emerging scientific tool in nondestructive fruit and vegetable quality assessment. Hyperspectral imaging technique integrates both the imaging and spectroscopic techniques into one system, and it can acquire a set of monochromatic images at almost continuous hundreds of thousands of wavelengths. Many researches based on spatial image and/or spectral image processing and analysis have been published proposing the use of hyperspectral imaging technique in the field of quality assessment of fruits and vegetables. This chapter presents a detailed overview of the introduction, latest developments and applications of hyperspectral imaging in the nondestructive assessment of fruits and vegetables. Additionally, the principal components, basic theories, and corresponding processing and analytical methods are also reported in this chapter

Non-destructive Techniques for Classifying Aircraft Coating Degradation

In this research non-destructive techniques were examined as possible methods of determining aircraft coating degradation. Single Value Decomposition(SVD)-Linear Discriminant Analysis(LDA) algorithms were applied to measured spectra. When applied to infrared emittance spectra only 52% classification accuracy was achieved. When applied to Raman spectroscopy a higher classification accuracy of 70.4% is attained when using the same SVD-LDA algorithm. However the best performing measurement was using infrared reflectance classification accuracies were 100%, 99.83% and 94.4% when using the Bomem FTS, DRIFTS and Telops respectively for one of the sample sets. For DRIFTS data a more accurate fingerprint region was identified 865.6 - 1238.7 cm -1 decreasing classification error by 50%. Feature selection was applied to determine filter locations for multi-spectral measurements. Simulating the optimal and commercially available filters accuracies of 95% and 94% were achieved using 5 filters. Infrared reflectance produces high classification accuracy when using the DRIFTS, Bomem FTS, Telops and a multi-spectral imager