Near-Recursive Optical Flow From Weighted Image Differences

Model-based analysis of production systems is one of the main areas in manufacturing research. The foundation of the successful application of these theoretical studies is the availability of valid and high-fidelity mathematical models that are capable of capturing the behavior of job flow in production systems. The modeling process of a production system, however, may require a significant amount of nonstandardized work that can only be done properly by someone with solid training in the area and extensive experience through real case studies. This poses a critical challenge in the effective implementation of these valuable theoretical results in the Industry 4.0 era. To overcome this, we propose a new production systems modeling paradigm inspired by system identification: calculate production system model parameters that best match the standard system performance metrics measured on the factory floor. Specifically, in this article, we consider production lines characterized by the Bernoulli serial line model and develop algorithms that identify model parameters to fit the system throughput and work-in-process. Analytical algorithms are derived to solve this problem in a two-machine line case and then extended to multi-machine lines. The accuracy and computational efficiency of the algorithms are demonstrated through extensive numerical experiments. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —A high-fidelity mathematical model is of critical importance to the implementation of any model-based production system analysis method. Currently, the construction of such models is carried out in an ad hoc manner. The quality of the resulting models may heavily depend on the training, experience, intuition, and personal preference of the modeler. The proposed model parameter identification method focuses on standard key performance indices commonly measured on the factory floor. The advantage is twofold. First, these standard performance metrics are consistently defined regardless of industry, thus avoiding any data-ambiguity issue that may occur when using complex machine/equipment status data. Second, measuring these performance metrics in real time is typically convenient and cost effective, even for manufacturing plants without high-end IT infrastructure, thus making the technology accessible to not only large but also small- and mid-sized manufacturers. Using the algorithms developed in this article, a practitioner can quickly construct a serial production line model and then utilize it to access the rich library of production analysis, design, and control methods available in the literature.

Heart rate variability (HRV) analysis is considered as a preliminary diagnosis method to check the cardiac health of the human heart. The reliability of the HRV analysis system solely depends on the accuracy of the QRS complex detector. Hence, in this paper, an optimally designed digital differentiator (DD) for precise detection of QRS complex is proposed. The proposed DD is designed by using an efficient evolutionary optimization technique called gases Brownian motion optimization (GBMO) algorithm and is used in the preprocessing stage of the QRS detector. In GBMO algorithm, a balanced trade-off is maintained between both the exploration and the exploitation phases to find the global optimum solution. The electrocardiogram signal is preprocessed by using the proposed DD to generate the feature signals corresponding to the R-peaks only. The detection technique utilizes the principle of Hilbert transform and zeroes crossing detection. The proposed approach is verified against all the first channel records of MIT/BIH arrhythmia database by considering the standard QRS detection performance metrics and produces a sensitivity (Se) of 99.92%, positive predictivity (+P) of 99.92%, detection error rate (DER) of 0.1562%, QRS detection rate of 99.92%, accuracy (Acc) of 99.84%, and F score of 0.9992%. With respect to the standard performance metrics, the proposed QRS detector outperforms all the recently reported QRS detection techniques.

Distance estimation from visually simulated self-motion is imprecise. Depending on the evaluation method, travelled distance can be under- or over-estimated. One particular method consists of asking a stationary observer, exposed to an immersive optical flow, simulating forward self-motion, to indicate when s/he thinks s/he has reached the remembered position of a previously seen distant target. In this case, subjective evaluation of travelled distance is generally overestimated (i.e., the subject undershoots the target). Recent studies suggest that a translational optical flow with biological additional oscillations (simulating the optical effects of natural locomotion) would increase the sensation of walking and improve spatial perception, as compared to a purely translational optical flow. In the present study we tested this hypothesis, by measuring travelled distance estimation, according to two conditions of visual simulation of forward self-motion, at constant speed, in a CAVE setup: (1) an optical flow simulating pure forward translation (2) an optical flow with added "biological" oscillations, reproducing the optical effects of the natural motion of the head during walking. Our results show that an optical flow containing additional biological information enhances the accuracy of travelled distance estimation. The perceptual advantage provided by the biological oscillations in the optical flow is discussed.

Optic flow informs moving observers about their heading direction. Neurons in monkey medial superior temporal (MST) cortex show heading selective responses to optic flow and planar direction selective responses to patches of local motion. We recorded MST neuronal responses to a 90 x 90 degrees optic flow display and to a 3 x 3 array of local motion patches covering the same area. Our goal was to test the hypothesis that the optic flow responses reflect the sum of the local motion responses. The local motion responses of each neuron were modeled as mixtures of Gaussians, combining the effects of two Gaussian response functions derived using a genetic algorithm, and then used to predict that neuron's optic flow responses. Some neurons showed good correspondence between local motion models and optic flow responses, others showed substantial differences. We used the genetic algorithm to modulate the relative strength of each local motion segment's responses to accommodate interactions between segments that might modulate their relative efficacy during co-activation by global patterns of optic flow. These gain modulated models showed uniformly better fits to the optic flow responses, suggesting that coactivation of receptive field segments alters neuronal response properties. We tested this hypothesis by simultaneously presenting local motion stimuli at two different sites. These two-segment stimuli revealed that interactions between response segments have direction and location specific effects that can account for aspects of optic flow selectivity. We conclude that MST's optic flow selectivity reflects dynamic interactions between spatially distributed local planar motion response mechanisms.

In this paper, investigations of brain activity response from experimental works, based on optic flow and wavelet using near infrared spectroscopy are presented. The outcomes of our research project showed that optical flow is a proper technology for the investigation of brain activity based on near-infrared spectroscopy (NIRS). Our research also showed that the results in the temporal domain, spatial domain, and wavelet domain are coherently supporting the analyses of our experimental results. Our wavelet analysis can obtain the majority of the brain activity for the spatiotemporal distribution. Based on the spatiotemporal analysis, we can clearly observe the existence of the dominant channel (DC), which plays a critical role in the brain activity. The spatial distribution of the cortical activity origin can be described by the hemodynamic response in the cerebral cortex after evoked stimulation using near infrared spectroscopy. Further application is expected in the next research outcomes.

The fly visual system, although tiny when compared to the mammalian visual system, can still perform highly sophisticated spatial tasks like collision avoidance, landing on objects, pursuit of prey, etc. Flies outperform human-made autonomous flying systems in solving such spatial tasks by a long way. This is partly due to their ability to perceive and respond to optical flow generated by motion in the environment. They are also known to take actions that alter the image flow on their eyes. Higher level neurons in the fly visual system respond to different types of complex optical flows due to rotation and translation, by pooling information from local motion detectors called the elementary motion detectors (EMDs) in the lower level. In this sense, neuronal responses (spikes) from these optical flow detectors in the fly carry highly encoded signals: a single spike can represent a complex dynamical pattern of movement in the visual field. In this paper, we investigate how such highly encoded signals can be interpreted and utilized in the fly's brain, while solely operating on the encoded internal spike patterns within their brain and no direct external (unencoded) sensory information, i.e. a form of grounding. With a computational model of the optical flow detectors based on those in the fly, we show that specific pattern of action (or coordinated motor output) is the only way that a fly can decode its internal spikes and generate meaningful, relevant behavior based on that.

Introduction: We propose a novel approach for the assessment of the similarity of retinal vessel segmentation images that is based on linking the standard performance metrics of a segmentation algorithm, with the actual structural properties of the images through the fractal dimension.Method: We apply our methodology to compare the vascularity extracted by automatic segmentation against manually segmented images.Results: We demonstrate that the strong correlation between the standard metrics and fractal dimension is preserved regardless of the size of the subimages analyzed.Discussion or Conclusion: We show that the fractal dimension is correlated to the segmentation algorithm’s performance and therefore it can be used as a comparison metric.

6D Object Pose Tracking with Optical Flow Network for Robotic Manipulation

Generation of a sequence of behaviors is necessary for the RoboCup Humanoid league to realize not simply an individual robot performance but also cooperative ones between robots. A typical example task is passing a ball between two humanoids, and the issues are: (1) basic skill decomposition, (2) skill learning, and (3) planning to connect the learned skills. This paper presents three methods for basic skill learning (trapping, approaching to, and kicking a ball) based on optic flow information by which a robot obtains sensorimotor mapping to realize the desired skill, assuming that skill decomposition and planning are given in advance. First, optic flow information of the ball is used to predict the trapping point. Next, the flow information caused by the self-motion is classified into the representative vectors, each of which is connected to motor modules and their parameters. Finally, optical flow for the environment caused by kicking motion is used to predict the ball trajectory after kicking. The experimental results are shown and discussion is given with future issues.

Joint direct estimation of 3D geometry and 3D motion using spatio temporal gradients

Vision-based control is being aggressively pursued for autonomous systems. Such control is particularly valuable for path planning to achieve mission objectives like target tracking and obstacle avoidance. This paper presents a multi-rate strategy that utilises a fast-rate optic flow approach and a slow-rate scene reconstruction approach. The vehicle uses scene reconstruction to generate an accurate map for path planning; however, optic flow is used to avoid obstacles while the scene reconstruction is computed. A switch element is used in the feedback path to determine whether information relating to the reconstructed map or the optical flow should be used for navigation. The resulting controller is able to generate flight trajectories and perform obstacle avoidance within a computational cost which is reasonable given performance demands and computational resources on a wide range of aircraft. A simulation demonstrates the performance of an aircraft that uses the multi-rate controller to avoid an obstacle which is only observed after a turn. Essentially, the fast-rate optic flow indicates the presence of the obstacle during the time that slow-rate scene reconstruction is being performed. The resulting flight path is able to follow mission objectives and avoid a collision.

In recent times, the medical image processing solves several clinical issues by inspecting the visual images, which are generated in the clinical health care units. The main objective of the research is to gain valuable information from the images for better clinical diagnosis. In the biomedical engineering domain, the medical image analysis is an emerging research topic. In the recent decades, the use of medical images is highly growing, which are acquired from different image modalities; so, there is a necessity for data compression for transmission, storage, and management of digital medical image datasets. Hence, the machine learning methods are effective for medical image analysis, where the deep learning models are used in the machine learning tools for automatically learning the feature vectors from the huge medical datasets. The automated deep learning models are effective compared to the conventional handcrafted features. In addition to this, a wireless sensor network (WSN) is used to create a primary health care scheme, which brings patient data together and expands the medical conveniences, whereas the WSN design must comprise of sensor nodes, because it consumes less power and resources at a relatively low cost; so, it is essential for implementing the Raspberry Pi-based WSN nodes. The sensor nodes are important for limited battery capacity and to transmit the vast amount of medical data. The proposed work is broadly classified into two categories such as (i) the medical image compression algorithm is developed using the deep learning model based on autoencoders and restricted Boltzmann machines (RBM) and (ii) implementation of the WSN sensors nodes with Raspberry Pi and Messaging Queue Telemetry Transport (MQTT) Internet of Things (IoT) protocol for secure transmission of the medical images. The experimental results are evaluated using the standard performance metrics like peak signal to noise ratio (PSNR) and presented a Real-Time Linux (RTL) implementation of the design. The proposed model showed 10 dB to 15 dB improvement in the PSNR value, while transmitting the medical images, which is better compared to the existing model.

Edge features or characteristics are the significant local intensity variation of pictorial structure in an image or frame or video sequence. Edge detection process finds the existence and position of pixels causes by significant differences in intensity of the images or frames or video sequences. However, selection of appropriate edge features specially to detect moving objects using aerial types of video sequences is still an elusive research concern because of various constraints for aerial frames, i.e. various altitudes, lack of features available for detection, motion variation etc. This research proposes comparative evaluation of different edge features based moving object detection methods to make it easy to decide which edge features based detection method is appropriate for extraction of moving object from aerial frames. Proposed research selected three edge features for detection, i.e. Sobel, Prewitt and Canny for moving object extraction to process high and low pictorial brightness variation for aerial video sequences. Proposed research illustrates the comparative performance of edge features based moving object detection. Each of the method is evaluated with extensive experimental analysis with standard performance metrics. Experimental results demonstrate effective comparison among each method with various performance metrics to ensure optimum detection performance for moving objects.

Real-time camera tracking for marker-less and unprepared augmented reality environments

Abstract. Benchmarking model performance across large samples of catchments is useful to guide model selection and future model development. Given uncertainties in the observational data we use to drive and evaluate hydrological models, and uncertainties in the structure and parameterisation of models we use to produce hydrological simulations and predictions, it is essential that model evaluation is undertaken within an uncertainty analysis framework. Here, we benchmark the capability of several lumped hydrological models across Great Britain by focusing on daily flow and peak flow simulation. Four hydrological model structures from the Framework for Understanding Structural Errors (FUSE) were applied to over 1000 catchments in England, Wales and Scotland. Model performance was then evaluated using standard performance metrics for daily flows and novel performance metrics for peak flows considering parameter uncertainty. Our results show that lumped hydrological models were able to produce adequate simulations across most of Great Britain, with each model producing simulations exceeding a 0.5 Nash–Sutcliffe efficiency for at least 80 % of catchments. All four models showed a similar spatial pattern of performance, producing better simulations in the wetter catchments to the west and poor model performance in central Scotland and south-eastern England. Poor model performance was often linked to the catchment water balance, with models unable to capture the catchment hydrology where the water balance did not close. Overall, performance was similar between model structures, but different models performed better for different catchment characteristics and metrics, as well as for assessing daily or peak flows, leading to the ensemble of model structures outperforming any single structure, thus demonstrating the value of using multi-model structures across a large sample of different catchment behaviours. This research evaluates what conceptual lumped models can achieve as a performance benchmark and provides interesting insights into where and why these simple models may fail. The large number of river catchments included in this study makes it an appropriate benchmark for any future developments of a national model of Great Britain.