Facial emotion recognition using min-max similarity classifier

Recognition of human emotions from the imaging templates is useful in a wide variety of human-computer interaction and intelligent systems applications. However, the automatic recognition of facial expressions using image template matching techniques suffer from the natural variability with facial features and recording conditions. In spite of the progress achieved in facial emotion recognition in recent years, the effective and computationally simple feature selection and classification technique for emotion recognition is still an open problem. In this paper, we propose an efficient and straightforward facial emotion recognition algorithm to reduce the problem of inter-class pixel mismatch during classification. The proposed method includes the application of pixel normalization to remove intensity offsets followed-up with a Min-Max metric in a nearest neighbor classifier that is capable of suppressing feature outliers. The results indicate an improvement of recognition performance from 92.85% to 98.57% for the proposed Min-Max classification method when tested on JAFFE database. The proposed emotion recognition technique outperforms the existing template matching methods

High voltage outdoor insulator surface condition evaluation using aerial insulator images

High voltage insulator detection and monitoring via drone-based aerial images is a cost-effective alternative in extreme winter conditions and complex terrains. The authors examine different surface conditions of the outdoor electrical insulator that generally occur under winter condition using image processing techniques and state-of-the-art classification methods. Two different types of classification approaches are compared: one method is based on neural networks (e.g. CNN, InceptionV3, MobileNet, VGG16, and ResNet50) and the other method is based on traditional machine learning classifiers (e.g. Bayes Net, Decision Tree, Lazy, Rules, and Meta classifiers). They are evaluated to discriminate the images of insulator surface exposed to freezing, wet, and snowing conditions. The results indicate that traditional machine learning methods with proper selection of features can show high classification accuracy. The classification of the insulator surfaces will assist in determining the insulator conditions, and take preventive measures for its protection

An Improved Semisupervised Outlier Detection Algorithm Based on Adaptive Feature Weighted Clustering

There exist already various approaches to outlier detection, in which semisupervised methods achieve encouraging superiority due to the introduction of prior knowledge. In this paper, an adaptive feature weighted clustering-based semisupervised outlier detection strategy is proposed. This method maximizes the membership degree of a labeled normal object to the cluster it belongs to and minimizes the membership degrees of a labeled outlier to all clusters. In consideration of distinct significance of features or components in a dataset in determining an object being an inlier or outlier, each feature is adaptively assigned different weights according to the deviation degrees between this feature of all objects and that of a certain cluster prototype. A series of experiments on a synthetic dataset and several real-world datasets are implemented to verify the effectiveness and efficiency of the proposal

Assessment and development of novel transition metal oxide radiation detectors

New radiation detector materials based on transition metal oxides [TMOs] are being developed for applications in radiation protection. The TMOs are cheap, simple to manufacture and have semiconducting properties, indicating either p-type or n-type characteristics. Thus, p-n hetrojunction diodes may be formed by combining p-type and n-type specific TMOs to produce radiation sensitive detectors. This is done by layering the materials in a flame-spraying process. Work by Mott on amorphous semiconductors has inspired this work to addressing the theoretical analysis of the disordered TMOs. It is found that charge carriers are polarons due to the presence of localised states in the TMOs. Moreover, the charge transport mechanism is ―Mott Hopping‖, whereby conductivity obeys the relationship of log σ ~ -[1/T]1/4. Practically, early studies have shown that TMO detectors perform successfully at room temperature as ionising radiation detectors. However, limitations existed in their very early design, which used Cu tape electrodes and, though demonstrated acceptable levels of response. This response did not meet the full expectation of the TMO detectors. New electrode deposition and materials, therefore, are sought to form ohmic and well-deposited electrodes. In this work, electrodes of Ag and Al were physically vapour deposited on either side of the detector. The electron microscope was used to fully evaluate the structure of the TMO materials. It is found that the flame-spray deposition is uniform with 3.56% of surface variation. Bulk and surface were scanned and were found to vary by 3.18% with variation in oxygen level of 0.55%. Mechanical polishing has shown a high level of validity to improve the surface by 94%. TMOs have demonstrated superior absorption efficiency of 19%, compared to Si [5%] of 200 μm thickness with 33 keV of incident photon energy. In contrast, CZT showed a superior intrinsic efficiency of 0.47%, compared to TMOs [0.02%] at 33 keV. In terms of x-ray and UV radiation tests, excellent correlations between TMO detectors, and IC and Si photodiode were observed. Also, detection was found to be uniform as expected across the p-n hetrojunction with a variation of 6% as an average. Finally, the TMO detectors were claimed to be feasible for detection of photons in the energy range of UV up to diagnostic x-ray