Gas–Liquid Two-Phase Flow Measurement Based on Optical Flow Method with Machine Learning Optimization Model

A gesture motion detection algorithm based on optical flow method is elaborated in this paper as significant way of human-computer interaction. The skin color segmentation algorithm based on ellipse skin color model in YCbCr color space and Pyramid Lucas-Kanade optical flow method constitute the basic modules of gesture motion detection. The skin color areas are selected in sequence by skin color segmentation algorithm which produce the optical flow vectors within the Pyramid Lucas-Kanade optical flow module. Optical flow vectors of the adjacent two frames after isolation are used as parameters for judging direction. When two results after judgment are consistent, the direction of gesture movement is determined. The algorithm proposed in this paper, testified from the experiments, is of high accuracy and less time-consuming which can be applied in real-time broadly.

To address the degradation of visual simultaneous localization and mapping (VSLAM) pose estimation accuracy caused by extensive moving objects and the resulting contamination of feature points in indoor dynamic environments, this study proposes a dynamic feature point rejection strategy that integrates a You Only Look Once (YOLO) family detector, YOLO11, with the Lucas–Kanade (LK) optical flow method within the ORB_SLAM3 framework. First, YOLO11 is employed to detect potentially dynamic targets such as pedestrians in the image, and ORB features extracted within the detection bounding boxes are labeled as high-risk dynamic points. Subsequently, based on pyramidal LK optical flow, the motion consistency of feature points is analyzed across multiple frames, and a secondary discrimination is performed on features located at the edges and in the vicinity of the detection boxes. In this way, feature points that conform to a static motion model are maximally preserved, thereby alleviating the excessive removal and misclassification of static points caused by oversized detection boxes. On this basis, a new map is reconstructed using the filtered static feature points, and the pose estimation process is further refined. Experiments on multiple indoor dynamic scenarios demonstrate that, compared with the original ORB_SLAM3, the proposed method effectively suppresses dynamic interference, enhances the stability of feature points, and significantly improves pose estimation accuracy and trajectory continuity, thus providing a valuable reference for robust VSLAM applications in dynamic environments.

Enhanced river suspended sediment concentration identification via multimodal video image, optical flow, and water temperature data fusion

The keyframe image character of 3D animation refers to the image state of the character or scene and other objects presented at specific keyframes in the animation timeline. The character texture will be blurred, distorted and discontinuous in the animation sequence due to motion, perspective change or lighting difference. Due to the lack of optical flow information to guide the current 3D animation keyframe image enhancement process, it is difficult to effectively distinguish the foreground character from the background elements, which affects the algorithm’s ability of recognizing the animation character’s movement patterns. To this end, a character texture enhancement method based on the SIFT algorithm and LK (Lucas-Kanade) optical flow is investigated for 3D animation keyframe images. The SIFT algorithm is used to extract the feature points of the character in each frame of 3D animation image as the initial tracking template, the LK optical flow method is used to calculate the optical flow vector of the feature points in the new frame, and the weighted directional histogram is used to estimate the direction of the character’s movement, and then the feature points in the tracking template are optimized through feature matching and weight updating to complete the tracking of the character’s target in each frame of the image. According to the target tracking results, a fractional-order differential mask is constructed in the character target region, and an adaptive function is constructed by combining the modes of the local gradient, information entropy and variance parameters of the image, so as to realize the enhancement of the character texture of the key frame images of the 3D animation by dynamically adjusting the order of the fractional-order differential. The experimental results show that the method can realize the dynamic tracking of 3D animation image character, and the extracted 3D animation image character feature points show a balanced distribution, and the distribution ratio is reasonable. It can realize the texture enhancement of 3D animation keyframe images, and the enhanced animation character clothing, facial details and other information are clearly recognizable, so as to improve the overall quality and visual impact of 3D animation.

Unmanned aerial vehicles (UAVs) have become an important part of today’s world. Micro Aerial Vehicles (MAVs) are small-sized UAVs that are suited for closed spaces, indoor or covert applications. Due to their small size and limited energy and load they can bear, there are several challenges in intelligent control of these vehicles. Also, due to their limitations, using energy-consuming sensors and complex control strategies which need high computational power, is not an option. To overcome MAV’s limitations, researchers are working on visual navigation systems. In this paper, we present an efficient strategy for the guidance and navigation of a 30gr MAV in indoor environments using a mixture of visual and inertial sensors. By taking inspiration from nature and flying insects, we combined optical flow and stereopsis methods to detect stationary obstacles and avoid collisions. Our control system uses two lateral cameras to avoid collision with lateral walls. To detect frontal walls and obstacles, instead of using optical flow methods, we use two frontal cameras with the same viewing direction and calculate the discrepancy between their images to detect walls and obstacles. For guidance, we designed a guidance system that functions well with our obstacle detection system and only needs destination and MAV’s current coordinates. By implementing our control system on a simulated coaxial helicopter, we were able to record more than 150 hours of collision-free flight and successful guidance to appointed destinations. Our control system is best suited for small sized UAVs in indoor environments and only needs inexpensive sensors and microcontrollers to implement

This study adopted an improved fuzzy clustering and optical flow method for the multiscale identification and forecasting of a cloud system based on the cloud images from a 10.8-micron infrared channel of the Advanced Geosynchronous Radiation Imager. First, we used the locally constrained fuzzy c-means (FCM) clustering method to identify typhoon-dominant cloud systems. Second, we coupled the background field-constrained optical flow method with the semi-Lagrangian scheme to forecast typhoon-dominant cloud systems. The experimental results for Typhoon Maria showed that the improved FCM method was able to effectively identify changes in the cloud system while retaining its edge information through the effective removal of the offset field. The identified dominant cloud system was consistent with the precipitation field of the Global Precipitation Measurement mission. We optimized the semi-Lagrangian nonlinear extrapolation of the optical flow field by introducing background field information, thus improving the forecast accuracy of the optical flow field. Based on the assessment indicators of structural similarity, normalized mutual information, peak signal-to-noise ratio, relative standard deviation, and root mean square error, the forecast results demonstrated that the forecast effect of the background field-constrained optical flow method was better than that of the standard optical flow method.

Paper 100020I, 11 S.

Digital subtraction angiography is the gold standard vascular imaging and it is used for all endovascular treatment of intracranial anerysms. Optical flow imaging has been described as a potential method to evaluate cerebral hemodynamics through DSA. In this study, we aimed to compare the flow patterns measured during angiography, by using an optical flow method, with those measured by using computational fluid dynamics in intracranial aneurysms. A consecutive series of 21 patients harboring unruptured saccular intracranial aneurysms who underwent diagnostic angiography before treatment was considered. High-frame-rate digital subtraction angiography was performed to obtain an intra-aneurysmal velocity field by following the cardiac-modulated contrast wave through the vascular structures by using optical flow principles. Additionally, computational fluid dynamics modeling was performed for every case by using patient-specific inlet-boundary conditions measured with the optical flow method from both DSA and 3D rotational angiography datasets. Three independent observers compared qualitatively both the inflow direction and the apparent recirculation in regular DSA, optical flow images, and computational fluid dynamics flow patterns for each patient; κ statistics were estimated. We included 21 patients. In 14 of these 21, the flow patterns were conclusive and matching between the optical flow images and computational fluid dynamics within the same projection view (κ = .91). However, in only 8 of these 14 patients the optical flow images were conclusive and matching regular DSA images (observer κ = 0.87). In 7 of the 21 patients, the flow patterns in the optical flow images were inconclusive, possibly due to improper projection angles. The DSA-based optical flow technique was considered qualitatively consistent with computational fluid dynamics outcomes in evaluating intra-aneurysmal inflow direction and apparent recirculation. Moreover, the optical flow technique may provide the premises for new solutions for improving the visibility of flow patterns when contrast motion in DSA is not apparent. This technique is a diagnostic method to evaluate intra-aneurysmal flow patterns and could be used in the future for validation and patient evaluation.

Accurate inverse-consistent symmetric optical flow for 4D CT lung registration

The article is devoted to the study of the optical flow method for detecting changes in the shooting angle of city surveillance cameras. The study was conducted using data obtained from surveillance cameras in Kazan. The study was conducted under conditions of a static position of the cameras, as well as under conditions of their vertical and horizontal shift by 10 to 50 degrees. For each of the conditions, the corresponding data samples were used. To study the optical flow method, four algorithms for its implementation were used. Two algorithms were used for a sparse flow (Lucas-Kanade and Pyramidal Lucas-Kanade), and two more were used for a dense flow (Dense Inverse Search and FarneBack). To evaluate their effectiveness, and, consequently, the optical flow method, the following metrics were calculated: flow value, execution time in seconds, average shift value, proportion of non-zero vectors, shift consistency. Based on the obtained results, several important conclusions were made. With a static position of the camera, the average shift value for dense flows was significantly higher than for sparse flows. This increases the chance of obtaining false positive results, especially at night. With controlled camera displacement, the average shift value for the Lucas-Kanade and FarneBack algorithms was almost zero, which indicates their inability to reliably detect displacement. The Dense Inverse Search algorithm turned out to be the most sensitive to displacements, and Pyramidal Lucas-Kanade detected displacements, but with less accuracy. Dense flow algorithms are inferior in speed to sparse flow algorithms. Optical flow algorithms do not respond to text elements of the image. Thus, the study solved the problem. The study allowed us to evaluate the effectiveness of the selected method and algorithms in terms of the accuracy of calculating the average shift and resistance to dynamic objects. The optical flow method and its algorithmic implementation Pyramidal Lucas-Kanade are best suited for detecting the displacement of CCTV cameras in an urban environment. Minimization of false positives when the camera is stationary, combined with reliable detection of shifts, makes it an optimal choice, and immunity to text elements in the image increases its effectiveness.

The principle of an optical flow method is formulated for neutron radiography (NR) flow diagnostics to determine high-resolution velocity fields of two-phase flows. This method is based on solving an optical flow equation derived from the continuous equation for NR image analysis as an inverse problem using a variational method. The mathematical definition and physical meaning of the optical flow in NR images of a two-phase flow are clarified. As an example, the optical flow method is used to extract complex velocity fields from a time sequence of dynamic NR images acquired in an air–water two-phase flow in a flat bubbler.

Key frame extraction is an important component of video analysis, and has gradually become a research hotspot in the computer vision community in recent years. Early key frame extraction algorithms were mostly based on fixed time intervals, and their accuracy could not meet practical application requirements. Thanks to the rapid development of machine learning technology, key frame extraction algorithms based on image quality, motion analysis, and deep learning are gradually becoming mature. Although the above methods significantly improve the accuracy of key frame extraction, few work pay attention to the extraction effect in different scenarios. In this article, selecting the key frame extraction algorithm based on optical flow method, we compared the key frame extraction algorithm based on optical flow method in detail for different video scenarios. Extensive experimental results demonstrate the robuteness of key frame extraction algorithm based on optical flow.

This paper focuses on crowd motion analysis and consists two parts. Firstly, we propose a new foreground detection approach called optical flow and background model (OFBM) based on Lucas-Kanade optical flow and Gaussian background model methods. This approach overcomes the shortages of optical flow and background subtract, such as sensitiveness of light changing and producing accumulate errors. Secondly, according to moment analysis, we propose a new feature based on the zeroth-order Tehebichef discrete orthogonal moment (TOM), which is employed for crowd density estimation. Some experimental results show that this approach is useful and efficient in crowd density estimation.

The strap-down missile-borne image guidance system can be easily affected by the unwanted jitters of the motion of the camera, and the subsequent recognition and tracking functions are also influenced, thus severely affecting the navigation accuracy of the image guidance system. So, a real-time image stabilization technology is needed to help improve the image quality of the image guidance system. To satisfy the real-time and accuracy requirements of image stabilization in the strap-down missile-borne image guidance system, an image stabilization method based on optical flow and image matching with binary feature descriptors is proposed. The global motion of consecutive frames is estimated by the pyramid Lucas-Kanade (LK) optical flow algorithm, and the interval frames image matching based on fast retina keypoint (FREAK) algorithm is used to reduce the cumulative trajectory error. A Kalman filter is designed to smooth the trajectory, which is conducive to fitting to the main motion of the guidance system. Simulations have been carried out, and the results show that the proposed algorithm improves the accuracy and real-time performance simultaneously compared to the state-of-art algorithms.

The seminal work of Horn and Schunck is the first variational method for optical flow estimation. It introduced a novel framework where the optical flow is computed as the solution of a minimization problem. From the assumption that pixel intensities do not change over time, the optical flow constraint equation is derived. This equation relates the optical flow with the derivatives of the image. There are infinitely many vector fields that satisfy the optical flow constraint, thus the problem is ill-posed. To overcome this problem, Horn and Schunck introduced an additional regularity condition that restricts the possible solutions. Their method minimizes both the optical flow constraint and the magnitude of the variations of the flow field, producing smooth vector fields. One of the limitations of this method is that, typically, it can only estimate small motions. In the presence of large displacements, this method fails when the gradient of the image is not smooth enough. In this work, we describe an implementation of the original Horn and Schunck method and also introduce a multi-scale strategy in order to deal with larger displacements. For this multi-scale strategy, we create a pyramidal structure of downsampled images and change the optical flow constraint equation with a nonlinear formulation. In order to tackle this nonlinear formula, we linearize it and solve the method iteratively in each scale. In this sense, there are two common approaches: one approach that computes the motion increment in the iterations; or the one we follow, that computes the full flow during the iterations. The solutions are incrementally refined over the scales. This pyramidal structure is a standard tool in many optical flow methods.