Development and characterization of a computer vision system for human body detection and tracking under low-light condition

© 2019, World Academy of Research in Science and Engineering. All rights reserved. Computer vision is an exciting field that integrates soft-computing algorithms and computer hardware powerful enough to handle the computation required especially if it is to be used in real-time applications. Pedestrian detection and tracking is one application of computer vision which is very useful in surveillance applications. Under this application, real-time processing of images to detect human object and perform tracking is carried out. Illumination of the scene is crucial in order the detection algorithm to carry out the computation task correctly. In this study, a computer vision system is developed for the detection and tracking of pedestrian or human body in a video. Detection and tracking of the human object is performed under different illumination. The illumination is provided using an LED lighting system where the intensity of illumination can be varied by changing the duty cycle of the pulse activating the LEDs.

Experimental identification of manipulator dynamics through the minimization of its natural oscillations

This work presents amethod of identifying the dynamics parameters of rigid-body manipulators through the minimization of its natural oscillations. It is assumed that each link has an actuated joint that is different from its center of mass, such that its driving torque is influenced by gravitational force. In this earlier results of our study, it is assumed that the inertias can be expressed in terms of the mass and center of mass. This work utilizes the actual force of gravity for the manipulator link to achieve natural oscillation. The oscillatory motion allows the system to be converted into an optimization problem through the minimization of the frequency of oscillation. The correct dynamics parameters are found when the minimum frequency of oscillation is achieved. The proposed method is analyzed and a theorem is presented that supports the claims presented in this work together with implementation results

Classification of tone stimulated EEG signals using independent components and power spectrum vectors

© 2015 IEEE. The brain responds to different stimuli. In this study, the brain was stimulated by an audio sound that plays the tones C, F and G of the piano keyboard using a predefined audio piece. The brain\u27s response was recorded and a classification scheme was proposed. The EEG information was segmented into baseline, C, F, G and s-baseline. The independent components and the power spectrum vectors of each segment were obtained. The independent components and power spectrum vectors of the baseline (when relaxed) is highly distinguishable as compared to the other segments. The other segments are more scattered in the frequency domain more than in the statistical domain. Artificial neural networks (ANN) were used to classify the segments using a leave-out-one cross validation method. Both features are useful and gave high classification percentages. However, higher classification percentages were obtained using the power spectrum vectors

A comparative analysis of the topological structures of different LPC feature-based speech models

Describes initial experimentations done on three LPC (linear predictive coding) derived feature-based speech models: the LPC-cepstrum, the LSP (line spectral pair) and the postfilter-cepstrum (PFL). A comparative analysis of the topological structures of these models is also given. The structures are basically self-organizing feature maps which accept these models as inputs and after training, used to distinguish between isolated word utterances and speakers. A small database of 5 utterances and 4 speakers is initially used. The performance index of isolated word recognition and speaker identification for all models are calculated based on a hit-and-miss ratio and are also discussed. Experimental results reveal that the three parameters are comparable in performance. The LSP has a slight edge over the other two feature vectors in distinguishing isolated words

Multiple objective optimization of LED lighting system design using genetic algorithm

In order to maximize the advantages of LED lighting systems for controlled environment agriculture (CEA), several considerations must be taken into account such as the achievement of required daily light integral (DLI), uniform light distribution over the plant growing area, and minimize the investment and operating costs associated with the lighting system. This study aims to apply the multiple objective optimization of genetic algorithm in designing a lighting system that meets the mentioned objectives. The optimization variables, number of bits per variable and maximum number of iterations are fixed parameters tuned to the requirements of this application and the population size, mutation rate, and selection rate are genetic parameters for explorations. Results of the algorithm suggest the use of a number of LED lamps that is 31.25% lower than the maximum number of lamps that may be used in the plant growing area and, consequently, reduce the investment and operating costs while maintaining the required light integral capacity and uniformity. This and other studies that aim to develop and optimize LED lighting systems open more possibilities and promote the technology for controlled environment. Moreover, control and optimization of agricultural practices can lead to better plant quality and production even on locations and periods that they do not usually grow. © 2017 IEEE

Detection of fonts and characters with hybrid graphic-text plate numbers

Philippine license plates have different plate styles and character fonts making the plate character recognition challenging. This paper focuses on improving the segmentation method to recognize characters of different formats of Philippine license plates. The proposed system comprises of license plate classification, character segmentation and character recognition. License plate series was classified using color level of pixels in the image. Plate characters were segmented using 3-Class Fuzzy Clustering with Thresholding and Connected Component Analysis and were recognized using Template Matching. The system achieved an accuracy of 95% and 70% for the 2003 plate series and 2014 plate series, respectively, having tested 20 license plates from each series. © 2018 IEEE

Stroke position classification in breast self-examination using parallel neural network and wavelet transform

This study focuses on improving the stroke position classification for the implementation of vision-based breast self-examination guidance system. Previous works have not tackled different variation of breast forms and size and other environment factors. We propose the use of multiple neural networks with parallel computing for more robust classification. Each neural network will be trained for different cases of breast forms and sizes. This creates invariance in breast forms and sizes. Our technique utilized color moments and daubechies-4 wavelet transform for extracting the features in each frames, as the input to the neural networks. This modified approach can classify the stroke position of different breast forms at 89.5% accuracy. © 2014 IEEE

Depth estimation in monocular breast self-examination image sequence using optical flow

In this paper, we study the depth estimation for image sequence with small displacements as in Breast Self Examination (BSE). We utilized its Lucas-Kanade optical flow vectors, the concept of divergence and focus of expansion to estimate the apparent depth level for each frame. Moreover, orientation binning is also introduced to supplement its invariance to translation. The experiment used an actual BSE performance and the results show its effectiveness in predicting palpation depth level. This algorithm has shown to be in realtime implementation with a frame rate of 30 frames per second that is very useful for implementing the computer vision-based BSE guidance system. © 2014 IEEE