Alignment control using visual servoing and mobilenet single-shot multi-box detection (SSD): a review

The concept is highly critical for robotic technologies that rely on visual feedback. In this context, robot systems tend to be unresponsive due to reliance on pre-programmed trajectory and path, meaning the occurrence of a change in the environment or the absence of an object. This review paper aims to provide comprehensive studies on the recent application of visual servoing and DNN. PBVS and Mobilenet-SSD were chosen algorithms for alignment control of the film handler mechanism of the portable x-ray system. It also discussed the theoretical framework features extraction and description, visual servoing, and Mobilenet-SSD. Likewise, the latest applications of visual servoing and DNN was summarized, including the comparison of Mobilenet-SSD with other sophisticated models. As a result of a previous study presented, visual servoing and MobileNet-SSD provide reliable tools and models for manipulating robotics systems, including where occlusion is present. Furthermore, effective alignment control relies significantly on visual servoing and deep neural reliability, shaped by different parameters such as the type of visual servoing, feature extraction and description, and DNNs used to construct a robust state estimator. Therefore, visual servoing and MobileNet-SSD are parameterized concepts that require enhanced optimization to achieve a specific purpose with distinct tools

Lettuce growth stage identification based on phytomorphological variations using coupled color superpixels and multifold watershed transformation

Identifying the plant's developmental growth stages from seed leaf is crucial to understand plant science and cultivation management deeply. An efficient vision-based system for plant growth monitoring entails optimum segmentation and classification algorithms. This study presents coupled color-based superpixels and multifold watershed transformation in segmenting lettuce plant from complicated background taken from smart farm aquaponic system, and machine learning models used to classify lettuce plant growth as vegetative, head development and for harvest based on phytomorphological profile. Morphological computations were employed by feature extraction of the number of leaves, biomass area and perimeter, convex area, convex hull area and perimeter, major and minor axis lengths of the major axis length the dominant leaf, and length of plant skeleton. Phytomorphological variations of biomass compactness, convexity, solidity, plant skeleton, and perimeter ratio were included as inputs of the classification network. The extracted Lab color space information from the training image set undergoes superpixels overlaying with 1,000 superpixel regions employing K-means clustering on each pixel class. Six-level watershed transformation with distance transformation and minima imposition was employed to segment the lettuce plant from other pixel objects. The accuracy of correctly classifying the vegetative, head development, and harvest growth stages are 88.89%, 86.67%, and 79.63%, respectively. The experiment shows that the test accuracy rates of machine learning models were recorded as 60% for LDA, 85% for ANN, and 88.33% for QSVM. Comparative analysis showed that QSVM bested the performance of optimized LDA and ANN in classifying lettuce growth stages. This research developed a seamless model in segmenting vegetation pixels, and predicting lettuce growth stage is essential for plant computational phenotyping and agricultural practice optimization

Automation and Control for Adaptive Management System of Urban Agriculture Using Computational Intelligence

It has been predicted by the United Nations that the world population will increase to 9.8 billion in 2050. This causes agricultural development areas to be transformed into urban areas. This urbanization and increase in population density cause food insecurity. Urban agriculture using precision farming becomes a feasible solution to meet the growing demand for food and space. An adaptive management system (AMS) is necessary for such farm to provide an artificial environment suitable to produce cultivars effectively. This research proposes the development of a computational intelligence-based urban farm automation and control system utilizing machine learning and fuzzy logic system models. A quality assessment is employed for adjusting the environmental parameters with respect to the cultivars’ requirements. The system is composed of sensors for data acquisition and actuators for model-dictated responses to stimuli. Data logging was done wirelessly through a router that would collect and monitor data through a cloud-based dashboard. The model intended for training from the acquired data undergo statistical comparative analysis and least computational cost analysis to optimize the performance. The system performance was evaluated by monitoring the conditions of the sensors and actuators. Experiment results showed that the proposed system is accurate, robust, and reliable

Stereo Vision 3D Tracking of Multiple Free-Swimming Fish for Low Frame Rate Video

3D multiple fish tracking has gained a significant growing research interest to quantify fish behavior. However, most tracking techniques have used a high frame rate that is currently not viable for real-time tracking applications. This study discusses multiple fish tracking techniques using low frame rate sampling of stereo video clips. The fish are tagged and tracked based on the absolute error of predicted indices using past and present fish centroid locations and a deterministic frame index. In the predictor sub-system, the linear regression and machine learning algorithms intended for nonlinear systems, such as Adaptive Neuro-Fuzzy Inference System (ANFIS), symbolic regression, and Gaussian Process Regression (GPR), were investigated. Results have shown that in the context of tagging and tracking accuracy, the symbolic regression attained the best performance, followed by the GPR, i.e., 74% to 100% and 81% to 91%, respectively. Considering the computation time, symbolic regression resulted in the highest computing lag of approximately 946 ms per iteration, whereas GPR achieved the lowest computing time of 39 ms

Mapping and inverse mapping relation in image compression using neural network

Image Compression involves converting an image into a new representation that uses a similar number of bits. The resulting representation can be used to reconstruct the original image without sacrificing the quality of the image. There are several techniques in image compression but those techniques depend on the application. This research will present a new technique in image compression for gray levels using a neural network. The 64 by L by 64 and 128 by L by 128 neural network architectures will be used to figure out the most appropriate mapping and inverse mapping relation for a particular application. Simulation is done in a personal computer to achieve at most an 8 to 1 compression ratio

Blind image quality assessment based on natural statistics of double-opponency

One of the challenges in image quality assessment (IQA) is to determine the quality score without the presence of the reference image. In this paper, the authors proposed a no-reference image quality assessment method based on the natural statistics of double opponent (DO) cells. It utilizes the statistical modeling of the three opponency channels using the generalized Gaussian distribution (GGD) and asymmetric generalized Gaussian distribution (AGGD). The parameters of GGD and AGGD are then applied to feedforward neural network to predict the image quality. Result shows that for any opposing channels, its natural statistics parameters when applied to feedforward neural network can achieve satisfactory prediction of image quality. © 2018 Fuji Technology Press.All Rights Reserved

Improved noise robust automatic speech recognition system with spectral subtraction and minimum statistics algorithm implemented in FPGA

In this study, spectral subtraction speech enhancement is integrated to a two word vocabulary speech recognition system to effectively reduce the effects of background noise and increase the recognition rate. The whole system was implemented in FPGA and was modelled in MATLAB. The preprocessing subsystem contains the spectral subtraction algorithm and acoustic front end speech enhancements while the speech recognition subsystem contains the HMM and Viterbi search algorithms. 10 dirty speech samples of word \u27stop\u27 and \u27clockwise\u27 (sampled at 84 dB) were tested in the speech recognition prototype with varying background noise from 44.6 to 85.4 dB and noise floor (β) from 0.01 to 1. At the end of the testing, the system was able to recognize the two words (stop and clockwise) efficiently with accuracy rate of above 80% until a background noise of 68.6 dB. The best average recognition rate (from 44.6 to 85.4 dB background noise) of 48.5% on the other hand was recorded at 0.01 noise floor. The system without spectral subtraction enhancement was noticed to function efficiently only at 56.6 dB. © 2012 IEEE

Underwater distance ranging implemented through a stereo vision system

Vision systems are becoming popular as a method of gathering information and stimuli from a robot\u27s environment. The goal of this paper is to implement an underwater distance ranging system using a stereo vision system. Target tracking is based on the color of the targets. The ranging algorithm was designed so that it can range targets up to 2 meters away with a minimum accuracy of 90%. An algorithm for ranging above ground and underwater was implemented and the two were compared. Algorithms were setup using samples from an aquarium, pool and beach. It was found that the algorithm may be setup more accurately using samples from a wider range of distances. Different colors of lighting was used to illuminate a target to see which wavelength will best lights up the underwater environment. It was found that wavelengths above the visible range could be ruled out. Camera performance was measured under varying conditions of salinity. It was found that the camera used performed worse for salt water as the recorded videos became blurred with increase in salinity. © 2015 IEEE

Designing anaglyphs with minimal ghosting and retinal rivalry

The anaglyph is a widely overlooked method of viewing three-dimensional images on any colored display. This is done by selectively filtering the image through colored lenses. Despite the simplicity of this system, the approach to designing anaglyph images remained largely empirical until a recent mathematical analysis by Eric Dubois. While the methods shown in the said work create good anaglyphs, they still exhibit a large amount of retinal rivalry which makes anaglyphs uncomfortable to view. This paper tackles modifications to the said approach to tackle several anaglyph issues, namely ghosting, retinal rivalry, and color reproduction, simultaneously. Subjective testing showed an improvement in viewer acceptance of images designed using the proposed method. © 2013 IEEE