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Academic literature on the topic 'Speed Invariant Learned Corners'
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Journal articles on the topic "Speed Invariant Learned Corners"
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Bai, Shiyu, Weisong Wen, Yue Yu, and Li-Ta Hsu. "Invariant Extended Kalman Filtering for Pedestrian Deep-Inertial Odometry." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4-2024 (October 21, 2024): 607–12. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-2024-607-2024.
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Abstract. Indoor localization for pedestrians, which relies solely on inertial odometry, has been a topic of great interest. Its significance lies in its ability to provide positioning solutions independently, without the need for external data. Although traditional strap-down inertial navigation shows rapid drift, the introduction of pedestrian dead reckoning (PDR), and artificial intelligence (AI) has enhanced the applicability of inertial odometry for indoor localization. However, inertial odometry continues to be affected by drift, inherent to the nature of dead reckoning. This implies that even a slight error at a given moment can lead to a significant decrease in accuracy after continuous integration operations. In this paper, we propose a novel approach aimed at enhancing the positioning accuracy of inertial odometry. Firstly, we derive a learning-based forward speed using inertial measurements from a smartphone. Unlike mainstream methods where the learned speed is directly used to determine the position, we use the forward speed combined with non-holonomic constraint (NHC) as a measurement to update the state predicted within a strap-down inertial navigation framework. Secondly, we employ an invariant extended Kalman filter (IEKF)-based state estimation to facilitate fusion to cope with the nonlinearity arising from the system and measurement model. Experimental tests are carried out in different scenarios using an iPhone 12, and traditional methods, including PDR, robust neural inertial navigation (RONIN), and the EKF-based method, are compared. The results suggest that the method we propose surpasses these traditional methods in performance.
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Wood, Justin N., and Samantha M. W. Wood. "The development of newborn object recognition in fast and slow visual worlds." Proceedings of the Royal Society B: Biological Sciences 283, no. 1829 (2016): 20160166. http://dx.doi.org/10.1098/rspb.2016.0166.
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Object recognition is central to perception and cognition. Yet relatively little is known about the environmental factors that cause invariant object recognition to emerge in the newborn brain. Is this ability a hardwired property of vision? Or does the development of invariant object recognition require experience with a particular kind of visual environment? Here, we used a high-throughput controlled-rearing method to examine whether newborn chicks ( Gallus gallus ) require visual experience with slowly changing objects to develop invariant object recognition abilities. When newborn chicks were raised with a slowly rotating virtual object, the chicks built invariant object representations that generalized across novel viewpoints and rotation speeds. In contrast, when newborn chicks were raised with a virtual object that rotated more quickly, the chicks built viewpoint-specific object representations that failed to generalize to novel viewpoints and rotation speeds. Moreover, there was a direct relationship between the speed of the object and the amount of invariance in the chick's object representation. Thus, visual experience with slowly changing objects plays a critical role in the development of invariant object recognition. These results indicate that invariant object recognition is not a hardwired property of vision, but is learned rapidly when newborns encounter a slowly changing visual world.
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P, Sabelnikov, and Sabelnikov Yu. "Search for identical regions in images using invariant moments." Artificial Intelligence 26, jai2021.26(2) (2021): 55–62. http://dx.doi.org/10.15407/jai2021.02.055.
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One of the ways to describe objects on images is to identify some of their characteristic points or points of attention. Areas of neighborhoods of attention points are described by descriptors (lots of signs) in such way that they can be identified and compared. These signs are used to search for identical points in other images. The article investigates and establishes the possibility of searching for arbitrary local image regions by descriptors constructed with using invariant moments. A feature of the proposed method is that the calculation of the invariant moments of local areas is carried out with using the integral representation of the geometric moments of the image. Integral representation is a matrix with the same size as the image. The elements of the matrix is the sums of the geometric moments of individual pixels, which are located above and to the left with respect to the coordinates of this element. The number of matrices depends on the order of the geometric moments. For moments up to the second order (inclusively), there will be six such matrices. Calculation of one of six geometric moments of an arbitrary rectangular area of the image comes down up to 3 operations such as summation or subtraction of elements of the corresponding matrix located in the corners of this area. The invariant moments are calculated on base of six geometric moments. The search is performed by scanning the image coordinate grid with a window of a given size. In this case, the invariant moments and additional parameters are calculated and compared with similar parameters of the neighborhoods of the reference point of different size (taking into account the possible change in the image scale). The best option is selected according to a given condition. Almost all mass operations of the procedures for calculating the parameters of standards and searching of identical points make it possible explicitly perform parallel computations in the SIMD mode. As a result, the integral representation of geometric moments and the possibility of using parallel computations at all stages will significantly speed up the calculations and allow you to get good indicators of the search efficiency for identical points and the speed of work
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Li, Ruoxiang, Dianxi Shi, Yongjun Zhang, Ruihao Li, and Mingkun Wang. "Asynchronous event feature generation and tracking based on gradient descriptor for event cameras." International Journal of Advanced Robotic Systems 18, no. 4 (2021): 172988142110270. http://dx.doi.org/10.1177/17298814211027028.
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Recently, the event camera has become a popular and promising vision sensor in the research of simultaneous localization and mapping and computer vision owing to its advantages: low latency, high dynamic range, and high temporal resolution. As a basic part of the feature-based SLAM system, the feature tracking method using event cameras is still an open question. In this article, we present a novel asynchronous event feature generation and tracking algorithm operating directly on event-streams to fully utilize the natural asynchronism of event cameras. The proposed algorithm consists of an event-corner detection unit, a descriptor construction unit, and an event feature tracking unit. The event-corner detection unit addresses a fast and asynchronous corner detector to extract event-corners from event-streams. For the descriptor construction unit, we propose a novel asynchronous gradient descriptor inspired by the scale-invariant feature transform descriptor, which helps to achieve quantitative measurement of similarity between event feature pairs. The construction of the gradient descriptor can be decomposed into three stages: speed-invariant time surface maintenance and extraction, principal orientation calculation, and descriptor generation. The event feature tracking unit combines the constructed gradient descriptor and an event feature matching method to achieve asynchronous feature tracking. We implement the proposed algorithm in C++ and evaluate it on a public event dataset. The experimental results show that our proposed method achieves improvement in terms of tracking accuracy and real-time performance when compared with the state-of-the-art asynchronous event-corner tracker and with no compromise on the feature tracking lifetime.
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Wu, Yanbo, Yan Yao, Ning Wang, and Min Zhu. "Deep Learning-Based Timing Offset Estimation for Deep-Sea Vertical Underwater Acoustic Communications." Applied Sciences 10, no. 23 (2020): 8651. http://dx.doi.org/10.3390/app10238651.
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This study proposes a novel receiver structure for underwater vertical acoustic communication in which the bias in the correlation-based estimation for the timing offset is learned and then estimated by a deep neural network (DNN) to an accuracy that renders subsequent use of equalizers unnecessary. For a duration of 7 s, 15 timing offsets of the linear frequency modulation (LFM) signals obtained by the correlation were fed into the DNN. The model was based on the Pierson–Moskowitz (PM) random surface height model with a moderate wind speed and was further verified under various wind speeds and experimental waveforms. This receiver, embedded with the DNN model, demonstrated lower complexity and better performance than the adaptive equalizer-based receiver. The 5000 m depth deep-sea experimental data show the superiority of the proposed combination of DNN-based synchronization and the time-invariant equalizer.
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Shmuelof, Lior, John W. Krakauer, and Pietro Mazzoni. "How is a motor skill learned? Change and invariance at the levels of task success and trajectory control." Journal of Neurophysiology 108, no. 2 (2012): 578–94. http://dx.doi.org/10.1152/jn.00856.2011.
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The public pays large sums of money to watch skilled motor performance. Notably, however, in recent decades motor skill learning (performance improvement beyond baseline levels) has received less experimental attention than motor adaptation (return to baseline performance in the setting of an external perturbation). Motor skill can be assessed at the levels of task success and movement quality, but the link between these levels remains poorly understood. We devised a motor skill task that required visually guided curved movements of the wrist without a perturbation, and we defined skill learning at the task level as a change in the speed–accuracy trade-off function (SAF). Practice in restricted speed ranges led to a global shift of the SAF. We asked how the SAF shift maps onto changes in trajectory kinematics, to establish a link between task-level performance and fine motor control. Although there were small changes in mean trajectory, improved performance largely consisted of reduction in trial-to-trial variability and increase in movement smoothness. We found evidence for improved feedback control, which could explain the reduction in variability but does not preclude other explanations such as an increased signal-to-noise ratio in cortical representations. Interestingly, submovement structure remained learning invariant. The global generalization of the SAF across a wide range of difficulty suggests that skill for this task is represented in a temporally scalable network. We propose that motor skill acquisition can be characterized as a slow reduction in movement variability, which is distinct from faster model-based learning that reduces systematic error in adaptation paradigms.
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Choi, Ouk, Jongwun Choi, Namkeun Kim, and Min Chul Lee. "Combustion Instability Monitoring through Deep-Learning-Based Classification of Sequential High-Speed Flame Images." Electronics 9, no. 5 (2020): 848. http://dx.doi.org/10.3390/electronics9050848.
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In this study, novel deep learning models based on high-speed flame images are proposed to diagnose the combustion instability of a gas turbine. Two different network layers that can be combined with any existing backbone network are established—(1) An early-fusion layer that can learn to extract the power spectral density of subsequent image frames, which is time-invariant under certain conditions. (2) A late-fusion layer which combines the outputs of a backbone network at different time steps to predict the current combustion state. The performance of the proposed models is validated by the dataset of high speed flame images, which have been obtained in a gas turbine combustor during the transient process from stable condition to unstable condition and vice versa. Excellent performance is achieved for all test cases with high accuracy of 95.1–98.6% and a short processing time of 5.2–12.2 ms. Interestingly, simply increasing the number of input images is as competitive as combining the proposed early-fusion layer to a backbone network. In addition, using handcrafted weights for the late-fusion layer is shown to be more effective than using learned weights. From the results, the best combination is selected as the ResNet-18 model combined with our proposed fusion layers over 16 time-steps. The proposed deep learning method is proven as a potential tool for combustion instability identification and expected to be a promising tool for combustion instability prediction as well.
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Wang, Jiujian, Shaopu Yang, Yongqiang Liu, and Guilin Wen. "Deep Subdomain Transfer Learning with Spatial Attention ConvLSTM Network for Fault Diagnosis of Wheelset Bearing in High-Speed Trains." Machines 11, no. 2 (2023): 304. http://dx.doi.org/10.3390/machines11020304.
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High-speed trains operate under varying conditions, leading to different distributions of vibration data collected from the wheel bearings. To detect bearing faults in situations where the source and target domains exhibit differing data distributions, the technique of transfer learning can be applied to move the distribution of features gleaned from unlabeled data in the source domain. However, traditional deep transfer learning techniques do not take into account the relationships between subdomains within the same class of different domains, resulting in suboptimal transfer learning performance and limiting the use of intelligent fault diagnosis for wheel bearings under various conditions. In order to tackle this problem, we have developed the Deep Subdomain Transfer Learning Network (DSTLN). This innovative approach transfers the distribution of features by harmonizing the subdomain distributions of layer activations specific to each domain through the implementation of the Local Maximum Mean Discrepancy (LMMD) method. The DSTLN consists of three modules: a feature extractor, fault category recognition, and domain adaptation. The feature extractor is constructed using a newly proposed SA-ConvLSTM model and CNNs, which aim to automatically learn features. The fault category recognition module is a classifier that categorizes the samples based on the extracted features. The domain adaptation module includes an adversarial domain classifier and subdomain distribution discrepancy metrics, making the learned features domain-invariant across both the global domain and subdomains. Through 210 transfer fault diagnosis experiments with wheel bearing data under 15 different operating conditions, the proposed method demonstrates its effectiveness.
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Sun, Zihao, Xianfeng Yuan, Xu Fu, Fengyu Zhou, and Chengjin Zhang. "Multi-Scale Capsule Attention Network and Joint Distributed Optimal Transport for Bearing Fault Diagnosis under Different Working Loads." Sensors 21, no. 19 (2021): 6696. http://dx.doi.org/10.3390/s21196696.
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In recent years, intelligent fault diagnosis methods based on deep learning have developed rapidly. However, most of the existing work performs well under the assumption that training and testing samples are collected from the same distribution, and the performance drops sharply when the data distribution changes. For rolling bearings, the data distribution will change when the load and speed change. In this article, to improve fault diagnosis accuracy and anti-noise ability under different working loads, a transfer learning method based on multi-scale capsule attention network and joint distributed optimal transport (MSCAN-JDOT) is proposed for bearing fault diagnosis under different loads. Because multi-scale capsule attention networks can improve feature expression ability and anti-noise performance, the fault data can be better expressed. Using the domain adaptation ability of joint distribution optimal transport, the feature distribution of fault data under different loads is aligned, and domain-invariant features are learned. Through experiments that investigate bearings fault diagnosis under different loads, the effectiveness of MSCAN-JDOT is verified; the fault diagnosis accuracy is higher than that of other methods. In addition, fault diagnosis experiment is carried out in different noise environments to demonstrate MSCAN-JDOT, which achieves a better anti-noise ability than other transfer learning methods.
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Sobel, L., M. El-Masri, and J. L. Smith. "Heat Transfer and Flow Visualization in Natural Convection in Rapidly Spinning Systems." Journal of Heat Transfer 108, no. 3 (1986): 547–53. http://dx.doi.org/10.1115/1.3246969.
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The design of airborne superconducting generators for intermittent duty requires the understanding of some unique free-convection processes in the spinning helium bath. Toward that end, some fundamental experiments on steady and transient free convection in rotating containers of representative geometries have been performed. Heat transfer data from heaters of various geometries mounted on the outer container surface to several fluids are reported. A correlation for steady-state Nusselt number is presented for a wide range of Rayleigh and Prandtl numbers. The heat transfer coefficient was found to be independent of heater size, geometry, and fluid viscosity. Heat transfer measurements during simultaneous thermal transients and sudden increases in rotational speed were also made. They show an enhancement of heat transfer due to the relative counterrotation of the fluid following the acceleration of the container. This persists for a period well below that for fluid spinup. A model based upon the submergence of the thermal boundary layer by the diffusive wave from the wall was successful in correlating this period. Quasi-steady flow visualization experiments indicate that the thermal plumes generate two-dimensional, axially invariant flow fields. Their trajectories are radial relative to the spinning container. Those observations are shown to be consistent with the fact that weak buoyant plumes in containers rotating at small Ekman numbers result in low Rossby number motions. Those are two dimensional according to the Taylor–Proudman theorem. It is shown that the Coriolis and pressure forces on such a thermal column are in azimuthal equilibrium, hence the radial trajectory. Flow visualization following impulsive acceleration in an off-axis, nonaxisymmetric container shows that the flow field is dominated by vortices expelled from corners. The fluid spinup time, however, was found to be the same as that for an on-axis circular cylinder of the same characteristic diameter.
Conference papers on the topic "Speed Invariant Learned Corners"
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Schils, George F., and Donald W. Sweeney. "Recognition of elementary geometric figures using rotationally invariant correlation filters." In OSA Annual Meeting. Optica Publishing Group, 1986. http://dx.doi.org/10.1364/oam.1986.fq6.
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Sophisticated machine vision systems will require a high-speed front end that can compress the massive information present in visual data. These machine vision systems require as input the locations of basic geometric features, such as lines, arcs, and corners. Objects such as lines maintain their shape when viewed from any perspective, but they still must be recognized from an arbitrary rotation angle.
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Chiodini, S. "Trajectory reconstruction by means of an event-camera-based visual odometry method and machine learned features." In Aeronautics and Astronautics. Materials Research Forum LLC, 2023. http://dx.doi.org/10.21741/9781644902813-150.
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Abstract. This paper presents a machine learned feature detector targeted to event-camera based visual odometry methods for unmanned aerial vehicles trajectory reconstruction. The proposed method uses machine-learned features to enhance the accuracy of the trajectory reconstruction. Traditional visual odometry methods suffer from poor performance in low light conditions and high-speed motion. The event-camera-based approach overcomes these limitations by detecting and processing only the changes in the visual scene. The machine-learned features are crafted to capture the unique characteristics of the event-camera data, enhancing the accuracy of the trajectory reconstruction. The inference pipeline is composed of a module repeated twice in sequence, formed by a Squeeze-and-Excite block and a ConvLSTM block with residual connection; it is followed by a final convolutional layer that provides the trajectories of the corners as a sequence of heatmaps. In the experimental part, a sequence of images was collected using an event-camera in outdoor environments for training and test.
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Theodoracatos, Vassilios E., and Ranganath R. Katti. "An Automated and Interactive Approach for Fitting B-Spline Surfaces Through 3D Planar Visual Data." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0102.
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Abstract The work reported here is an integral part of a system developed for the automated reconstruction of arbitrary-shaped physical objects using vision systems, three dimensional computer graphics, and B-Spline surface approximation techniques. Digitized planar contour points are automatically fitted in the image and world space to define a minimum number of B-Spline control points using least-squares approximation techniques. The final set of points represent the B-Spline control net of the entire surface of the object. The resulting curves and surfaces can be further interactively modified until a satisfactory fit is obtained. Three parametrization techniques, viz., uniform, chord length, and affine invariant angle method are implemented and adjusted to their local minima using the Newton-Raphson iteration method. The effect of each method on the accuracy of the reconstructed surface is discussed. The techniques were tested using a clay model of a human face. The uniform parametrization performed better with the highest speed of convergence and best least-squares error characteristics. On the other hand, it was less effective in detecting sharp corners as compared to the other two methods. The results also show that there is a minimum number of control points for every surface beyond which there is no error improvement. This is useful in several industrial applications when checking surface accuracy of manufactured parts using Coordinate Measuring Machines (CMM).