Face recognition using color local binary pattern from mutually independent color channels

In this paper, a high performance face recognition system based on local binary pattern (LBP) using the probability distribution functions (PDF) of pixels in different mutually independent color channels which are robust to frontal homogenous illumination and planer rotation is proposed. The illumination of faces is enhanced by using the state-of-the-art technique which is using discrete wavelet transform (DWT) and singular value decomposition (SVD). After equalization, face images are segmented by use of local Successive Mean Quantization Transform (SMQT) followed by skin color based face detection system. Kullback-Leibler Distance (KLD) between the concatenated PDFs of a given face obtained by LBP and the concatenated PDFs of each face in the database is used as a metric in the recognition process. Various decision fusion techniques have been used in order to improve the recognition rate. The proposed system has been tested on the FERET, HP, and Bosphorus face databases. The proposed system is compared with conventional and thestate-of-the-art techniques. The recognition rates obtained using FVF approach for FERET database is 99.78% compared with 79.60% and 68.80% for conventional gray scale LBP and Principle Component Analysis (PCA) based face recognition techniques respectively.Comment: 11 pages in EURASIP Journal on Image and Video Processing, 201

Human activity recognition-based path planning for autonomous vehicles

Human activity recognition (HAR) is a wide research topic in a field of computer science. Improving HAR can lead to massive breakthrough in humanoid robotics, robots used in medicine and in the field of autonomous vehicles. The system that is able to recognise human and its activity without any errors and anomalies would lead to safer and more empathetic autonomous systems. During this research work, multiple neural networks models, with different complexity, are being investigated. Each model is re-trained on the proposed unique data set, gathered on automated guided vehicle (AGV) with the latest and the modest sensors used commonly on autonomous vehicles. The best model is picked out based on the final accuracy for action recognition. Best models pipeline is fused with YOLOv3, to enhance the human detection. In addition to pipeline improvement, multiple action direction estimation methods are proposed. © 2020, Springer-Verlag London Ltd., part of Springer Nature

Real-Time Automatic Colour Calibration for NAO Humanoids

A challenge in NAO soccer robots is colour calibration. Good colour calibration can result in robust and accurate self-localization of the robot. Currently manual calibration is the only solution, which is used. In this paper, we are proposing an automatic real-time, accurate YUV colour space based colour calibration technique. In order to define average values for the desired colour classes namely orange, white, green and purple, a specified set of frames from the NAO camera are analysed. These average values are corrected by luminance analysis of a new frame and are passed to the K-means clustering algorithm as a set of initial means. In addition to these four values, a set of initial means of the K-means algorithm contains 16 values that are calculated in the following manner: the frame being processed is divided into 4 by 4 grids and the average value from every grid serves as an initial mean for K-means clustering. Consequently, colours of a similar type are combined into clusters. The final step of the proposed technique is cluster classification in which the average values of the desired colour classes are corrected by luminance analysis. As NAO cameras provide video streams in YUV format and the proposed algorithm uses this format there is no need for additional computational steps for conversation between the colour spaces. As a result, computational process is reduced compared to current techniques

Anti-Windup Compensator Approach to Nanosatellite Fault Tolerant Architecture

Anti-windup (AW) compensator in this study is designed to work with control systems experiencing actuator saturation. While working with an existing controller, the AW compensator prevents degradation in performance during saturation and enhances the system to perform optimally after saturation. In addition, the fault tolerant capability of a proposed integrated fault tolerant architecture is studied with the AW compensator

Resolution enhancement of imagestakenby mobile phonecamera

Carey and co-researchers have estimated a super-resolution technique DA SR (Demirel-Anbarjafari Super Resolution), based on interpolation of the high frequency sub-band images obtained by discrete wavelet transform (DWT). Their estimation was carried out by investigating the evolution of wavelet transform extrema among the same type of subbands. Edges identified by an edge detection algorithm in lower frequency subbands were used to prepare a model for estimating edges in higher frequency subbands; and only the coefficients with significant values were estimated as the evolution of the wavelet coefficients. Finally, interpolated high-frequency sub-band images and the interpolated input image are combined by using IDWT to achieve a high resolution output image. The technique has been implemented in Java language in order to be installed on the mobile phones. The DA SR technique has been tested on well-known benchmark images