Accurate Motion Measurements Research Articles

Robot motion visual measurement based on RANSAC and weighted constraints method

Precision of the coordinate transformation model parameters is crucial for the accuracy of the vision-based robot spatial motion measurement method. In this work, an optimization algorithm integrating RANSAC and iterative weighted singular value decomposition (IR-SVD) is proposed for improving the coordinate transformation model solution precision, aiming at enhancing the spatial motion measurement accuracy of the binocular vision system. Considering noises existing in reference transformation point pairs, the RANSAC algorithm is introduced to filter the raw measurement point pairs and extract inliers, thereby eliminating potential gross errors and realizing the cluster of advantageous points. An enhanced SVD method based on iterative weighted constraints is proposed to substitute traditional SVD. After calculating the coordinate transformation model parameters, the measurement errors of inliers are solved synchronously, and the weights are reallocated in light of the measurement errors value, subsequently recalculating the coordinate transformation model parameters repeatedly until the errors converge. The validation experiments are conducted on the self-built three-degree-of-freedom rectangular coordinate robot platform. The experimental results of discrete point-to-point motion and continuous trajectory motion measurement show that the proposed method can improve the coordinate transformation model parameters solution accuracy effectively, comparing with the traditional SVD method. Comparative experiment with existing commonly used coordinate transformation methods including Quaternion and iterative closest point indicates that the proposed method exhibits the best applicability and minimal errors in robot motion visual measurement. Both accuracy of the coordinate transformation model solution and the visual system’s motion measurement are enhanced with this newly-proposed, optimized method.

A novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment

The relative motion measurement has become the key technology for high precision imaging of array Synthetic Aperture Radar (SAR), which can be obtained by distributed Position and Orientation System (POS). The traditional method based on absolute parameters matching transfer alignment can measure the relative motion, but it will be affected by the main-system motion error and reduce precision. To solve this problem, a novel relative motion measurement method based on distributed POS relative parameters matching transfer alignment is proposed. Firstly, the relative inertial navigation algorithm is used to preliminarily measure the relative motion. Then, the transfer alignment model based on relative parameters matching is established to rectify the inertial measured error and obtain the high-precision relative motion. Finally, experimental results show the proposed method is effective to isolate the main-system motion error and improve relative motion measurement accuracy compared to the traditional method.

SwingNet

Sports analytics in the wild (i.e., ubiquitously) is a thriving industry. Swing tracking is a key feature in sports analytics. Therefore, a centimeter-level tracking resolution solution is required. Recent research has explored deep neural networks for sensor fusion to produce consistent swing-tracking performance. This is achieved by combining the advantages of two sensor modalities (IMUs and depth sensors) for golf swing tracking. Here, the IMUs are not affected by occlusion and can support high sampling rates. Meanwhile, depth sensors produce significantly more accurate motion measurements than those produced by IMUs. Nevertheless, this method can be further improved in terms of accuracy and lacking information for different domains (e.g., subjects, sports, and devices). Unfortunately, designing a deep neural network with good performance is time consuming and labor intensive, which is challenging when a network model is deployed to be used in new settings. To this end, we propose a network based on Neural Architecture Search (NAS), called SwingNet, which is a regression-based automatic generated deep neural network via stochastic neural network search. The proposed network aims to learn the swing tracking feature for better prediction automatically. Furthermore, SwingNet features a domain discriminator by using unsupervised learning and adversarial learning to ensure that it can be adaptive to unobserved domains. We implemented SwingNet prototypes with a smart wristband (IMU) and smartphone (depth sensor), which are ubiquitously available. They enable accurate sports analytics (e.g., coaching, tracking, analysis and assessment) in the wild. Our comprehensive experiment shows that SwingNet achieves less than 10 cm errors of swing tracking with a subject-independent model covering multiple sports (e.g., golf and tennis) and depth sensor hardware, which outperforms state-of-the-art approaches.

Image Motion Measurement and Image Restoration System Based on an Inertial Reference Laser.

Satellites have many high-, medium-, and low-frequency micro vibration sources that lead to the optical axis jitter of the optical load and subsequently degrade the remote sensing image quality. To address this problem, this paper developed an image motion detection and restoration method based on an inertial reference laser, and describe edits principle and key components. To verify the feasibility and performance of this method, this paper also built an image motion measurement and restoration system based on an inertial reference laser, which comprised a camera (including the inertial reference laser unit and a Hartmann wavefront sensor), an integrating sphere, a simulated image target, a parallel light pope, a vibration isolation platform, a vibration generator, and a 6 degrees of freedom platform. The image restoration principle was also described. The background noise in the experiment environment was measured, and an image motion measurement accuracy experiment was performed. Verification experiments of image restoration were also conducted under various working conditions. The experiment results showed that the error of image motion detection based on the inertial reference laser was less than 0.12 pixels (root mean square). By using image motion data to improve image quality, the modulation transfer function (MTF) of the restored image was increased to 1.61–1.88 times that of the original image MTF. The image motion data could be used as feedback to the fast steering mirror to compensate for the satellite jitter in real time and to directly obtain high-quality images.

SLIMM: Slice localization integrated MRI monitoring

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.

WeHAPTIC: A Wearable Haptic interface for Accurate Position Tracking and Interactive force Control

In this paper, a hand wearable system for virtual reality (VR), which is called as WeHAPTIC (Wearable Haptic interface for Accurate Position Tracking and Interactive force Control), is proposed. It shows improved performances in terms of finger motion measurement and force feedback compared with existing systems such as finger joint angle calculation and precise force control. Intensive experiments are also conducted to verify the mechanical performance as a haptic interface. The finger joint angles are inversely calculated by an estimation algorithm based on the fingertip positions with only one calibration posture. Not only the index finger motion which is generally measured, but also the thumb motion is accurately measured by our proposed algorithm. Accurate force feedback is achieved by robust control of the motor current. The motion measurement accuracy of average 2.62∘ errors in the thumb and index finger was verified by an infrared motion capture system. For performance evaluation of the force control, a Z-width and various interaction experiments were conducted; variable stiffness were generated, and various sizes & shapes of virtual objects were identified.

Simultaneous Motion and Distortion Correction Using Dual-Echo Diffusion-Weighted MRI.

Geometric distortions resulting from large pose changes reduce the accuracy of motion measurements and interfere with the ability to generate artifact-free information. Our goal is to develop an algorithm and pulse sequence to enable motion-compensated, geometric distortion compensated diffusion-weighted MRI, and to evaluate its efficacy in correcting for the field inhomogeneity and position changes, induced by large and frequent head motions. Dual echo planar imaging (EPI) with a blip-reversed phase encoding distortion correction technique was evaluated in five volunteers in two separate experiments and compared with static field map distortion correction. In the first experiment, dual-echo EPI images were acquired in two head positions designed to induce a large field inhomogeneity change. A field map and a distortion-free structural image were acquired at each position to assess the ability of dual-echo EPI to generate reliable field maps and enable geometric distortion correction in both positions. In the second experiment, volunteers were asked to move to multiple random positions during a diffusion scan. Images were reconstructed using the dual-echo correction and a slice-to-volume registration (SVR) registration algorithm. The accuracy of SVR motion estimates was compared to externally measured ground truth motion parameters. Our results show that dual-echo EPI can produce slice-level field maps with comparable quality to field maps generated by the reference gold standard method. We also show that slice-level distortion correction improves the accuracy of SVR algorithms as slices acquired at different orientations have different levels of distortion, which can create errors in the registration process. Dual-echo acquisitions with blip-reversed phase encoding can be used to generate slice-level distortion-free images, which is critical for motion-robust slice to volume registration. The distortion corrected images not only result in better motion estimates, but they also enable a more accurate final diffusion image reconstruction.

Accuracy improvement in motion‐measurement using frequency‐weighted Kalman filter

Accurate antenna motion-measurement is an essential pre-condition for synthetic aperture radar (SAR) to produce well-focused images. The measurement accuracy at high and low frequencies affects the quality of images and the geolocation error. Hence, motion compensation and focusing techniques require a minimum degree of accuracy in the radar motion measurements. The Inertial Measurement Unit (IMU) sensor selection process is done based on the SAR parametric requirements. The IMU instruments have inherent limited precision and accuracy, which are significantly related to the technology and the cost. The navigation algorithm may improve the precision and accuracy to overcome the limitations of the IMU sensor. This study intends to improve motion measurement accuracy using frequency-weighted state estimation. The proposed algorithm shows an error reduction in state estimation and an improvement in some parameter indices which are in relation to SAR image quality and geolocation. Two innovative algorithms are proposed in this study. The first one is the frequency-weighted Kalman filter which is an improved form of the Kalman filter. Another one is multi-resolution decomposition which is an approach to evaluate the SAR motion measurement system.

Displacement monitoring for slope stability evaluation based on binocular vision systems

Landslide monitoring systems play an important part in slope engineering, and effective monitoring techniques can ensure the safety of slopes. This paper proposes an accurate and stable slope displacement monitoring method based on binocular vision systems. In the binocular vision system, Zhang’s calibration algorithm is used to calibrate the camera parameters, which are critical for gathering accurate motion measurements. Measurement accuracy analysis is performed by adjusting the baseline distance from 0.2 m to 3.6 m and measurement distance from 1 m to 10 m during calibration and measurement, and the error curves for measurements are obtained. The results obtained illustrate that the accuracy is affected by the ratio of the baseline distance to the measurement distance. Displacement monitoring of a slope model is performed to demonstrate the practicality of the system and verify its accuracy. Results show that the proposed binocular vision based approach is convenient and accurate for slope displacement monitoring.

IGS polar motion measurement accuracy

We elaborate an error budget for the long-term accuracy of IGS (International Global Navigation Satellite System Service) polar motion estimates, concluding that it is probably about 25–30 μas (1-sigma) overall, although it is not possible to quantify possible contributions (mainly annual) that might transfer directly from aliases of subdaily rotational tide errors. The leading sources are biases arising from the need to align daily, observed terrestrial frames, within which the pole coordinates are expressed and which are continuously deforming, to the secular, linear international reference frame. Such biases are largest over spans longer than about a year. Thanks to the very large number of IGS tracking stations, the formal covariance errors are much smaller, around 5 to 10 μas. Large networks also permit the systematic frame-related errors to be more effectively minimized but not eliminated. A number of periodic errors probably also influence polar motion results, mainly at annual, GPS (Global Positioning System) draconitic, and fortnightly periods, but their impact on the overall error budget is unlikely to be significant except possibly for annual tidal aliases. Nevertheless, caution should be exercised in interpreting geophysical excitations near any of the suspect periods.

Gravity Compensation Method for Combined Accelerometer and Gyro Sensors Used in Cardiac Motion Measurements.

A miniaturized accelerometer fixed to the heart can be used for monitoring of cardiac function. However, an accelerometer cannot differentiate between acceleration caused by motion and acceleration due to gravity. The accuracy of motion measurements is therefore dependent on how well the gravity component can be estimated and filtered from the measured signal. In this study we propose a new method for estimating the gravity, based on strapdown inertial navigation, using a combined accelerometer and gyro. The gyro was used to estimate the orientation of the gravity field and thereby remove it. We compared this method with two previously proposed gravity filtering methods in three experimental models using: (1) in silico computer simulated heart motion; (2) robot mimicked heart motion; and (3) in vivo measured motion on the heart in an animal model. The new method correlated excellently with the reference (r 2>0.93) and had a deviation from reference peak systolic displacement (6.3±3.9mm) below 0.2±0.5mm for the robot experiment model. The new method performed significantly better than the two previously proposed methods (p<0.001). The results show that the proposed method using gyro can measure cardiac motion with high accuracy and performs better than existing methods for filtering the gravity component from the accelerometer signal.

WE‐G‐18C‐03: Effects of Breathing Irregularity On Tumor Motion Measurement of 4D‐MRI: A Comparison Between Cine and Sequential Image Acquisition Modes

Purpose:We have recently developed a cine‐mode T2*/T1‐weighted 4DMRI technique, and a sequential‐mode T2‐weighted 4D‐MRI for imaging respiratory motion . The latter has better tumor‐contrast due to its T2‐ weighting. This study aims at investigating which 4D‐MRI image acquisition mode, cine or sequential, provides more accurate measurement of respiratory motion.Methods:A 4D Digital Extended Cardiac‐Torso (XCAT) human phantom with a hypothesized tumor was used to simulate image acquisition and 4D‐MRI reconstruction. Tumor was set to move continuously with a given breathing signal. Its trajectories were measured from both sequential‐ and cine‐mode 4D‐MRI. Comparisons of measurements with the average tumor trajectories calculated from the input profile as references were conducted. Absolute tumor motion amplitude differences (D) were determined. A total of 500 simulated respiratory profiles with a wide range of irregularity were used to investigate the relationship between D and Ir. Statistical analysis of D for breathing profiles of 20 real cancer patients were conducted with a sign rank test regarding two modes. Lastly, we investigated the possibility of further improving motion measurement accuracy in sequential‐mode 4D‐MRI by removing data points of high irregularity. An irregularity filter was applied to the same 20 patients. Sign rank tests for D of sequential‐mode with filter and cine‐mode were performed.Results:D increased faster for cine‐mode (D=0.37*Ir) than sequential‐mode (D=0.16*Ir) as irregularity increased. For 20 cancer patients, D was 0.12cm and 0.10cm for cine‐ and sequentialmodes on average; p‐value was 0.0228. Removing highly irregular data points increased accuracy of tumor trajectory for sequential‐mode 4D‐MRI, D was decreased by 10%. P‐value was 0.0065.Conclusion:Tumor motion measurement is more accurate and less susceptible to breathing irregularity in sequential‐mode 4D‐MRI than that in cine‐mode 4D‐MRI; its accuracy could be further improved in sequential‐mode 4D‐MRI by selectively removing data points of high irregularity.NIH (1R21CA165384‐01A1)

Determination of three-dimensional movement for rotary blades using digital image correlation

Non-contact and accurate motion measurement of the rotary objects is crucial in engineering applications. A modified Newton–Raphson algorithm, which is capable of positioning marks with large rotation, has been proposed. A stereo imaging system with a pair of synchronized digital high-speed cameras was developed and achieved full-field displacement measurement based on 3D image correlation photogrammetry for rotary objects. This system has been applied to measuring the 3D motion of a wind turbine blade model. The displacement components of the rotary blade were presented, and the corresponding frequency spectra were investigated. The experimental results demonstrated that the proposed system could measure the 3D motion of rotary blades precisely, and it also provided an alternative potential non-contact diagnosis means for large wind turbine blades.

Measurement of intervertebral cervical motion by means of dynamic x-ray image processing and data interpolation.

Accurate measurement of intervertebral kinematics of the cervical spine can support the diagnosis of widespread diseases related to neck pain, such as chronic whiplash dysfunction, arthritis, and segmental degeneration. The natural inaccessibility of the spine, its complex anatomy, and the small range of motion only permit concise measurement in vivo. Low dose X-ray fluoroscopy allows time-continuous screening of cervical spine during patient's spontaneous motion. To obtain accurate motion measurements, each vertebra was tracked by means of image processing along a sequence of radiographic images. To obtain a time-continuous representation of motion and to reduce noise in the experimental data, smoothing spline interpolation was used. Estimation of intervertebral motion for cervical segments was obtained by processing patient's fluoroscopic sequence; intervertebral angle and displacement and the instantaneous centre of rotation were computed. The RMS value of fitting errors resulted in about 0.2 degree for rotation and 0.2 mm for displacements.

Target‐specific optimization of four‐dimensional cone beam computed tomography

Under-sampling artifacts are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) and may compromise evaluation of target motion. Several studies have addressed scan parameter selection for minimizing under-sampling artifacts; however, the role of the target characteristics in scan parameter selection has not been investigated. In this work, the authors evaluated 4D-CBCT performance by assessing the accuracy of target motion measurements for various target sizes and motions. The results may serve as patient-specific guidelines for scan parameters selection in 4D-CBCT. The authors acquired 4D-CBCT scans of a moving phantom consisting of six water-filled sphere targets of sizes 10-37 mm, using various scan times ranging from 30 s to 3 min., setting the motion to 3-s and 6-s periods. The authors used automatic image registration to extract the target motion trajectories and evaluated these measurements for various target sizes and motions over various combinations of scan parameters including scan time, detector configuration, number of respiration phases, and reconstruction filters. The most important object parameter to 4D-CBCT performance was the period of motion. Measurements for the 6-s motion were always systematically less accurate than measurements for the 3-s motion for 34 of 36 objects of various sizes and periods of motion. The 6-s motion required a greater scan time than the 3-s motion for equivalent measurement accuracy. The second most influential parameter was the target size. For the 3-s period of motion, objects larger than 13 mm were tracked with sub-millimeter accuracy with a 1-min scan time. For the 6-s period of motion, objects larger than 22-mm were tracked with sub-millimeter accuracy with a 1.5-min scan time. For all sizes and motions, temporal blurring was observed when the number of phases was fewer than 8. Offset detector configuration provided the same performance as centered detector except for small targets (20 mm.) at short scan times (≤1 min). Finally, reconstruction filtering and number of respiratory phases did not affect performance. Scan time should be set according to target size and motion. The authors have provided figures that provide the minimum scan time needed to achieve the particular motion measurement accuracy for a particular size and motion period.

SU-E-J-129: Target-Specific Optimization of Four-Dimensional Cone Beam Computed Tomography.

Undersampling artifacts are a major problem in four-dimensional cone-beam computed tomography (4D-CBCT) and may compromise evaluation of target motion. The role of the target characteristics in scan parameter selection has not been previously investigated. In this work, we evaluated 4D-CBCT performance by assessing the accuracy of target motion measurements for various target sizes and motions. The results may serve as patient-specific guidelines for selection of scan parameters in 4D-CBCT. We acquired 4D-CBCT scans of a moving phantom consisting of six water-filled sphere targets of sizes 10 to 37 mm, with various scan times ranging from 30 sec to 3 min. Two different (3-sec and 6-sec) periods of motions were tested. We used automatic image registration to extract the target motion trajectories and evaluated the accuracy of the measurement for the various target sizes and motions for various combinations of scan parameters. The most important object parameter to 4D-CBCT performance was the period of motion. Measurements for the 6-sec motion were always systematically less accurate than measurements for the 3-sec motion for 34 of 36 target sizes/motions for any given scan time. For any given target size, a 45-sec scan for the 3-sec motion yielded nearly equivalent accuracy to a 3-min scan for a 6-sec motion. The second most important parameter was the target size. A 1-min scan was needed for sub-mm motion measurement accuracy of 28-mm target, whereas a 1.5-min scan was needed for the 22-mm target. For all sizes and motions, temporal blurring was observed when the number of phases was fewer than 8. Scan time should be set according to target size and motion. We have provided figures that provide the minimum scan time needed to achieve the particular motion measurement accuracy for a particular size and period of motion.

Improved digital processing method used for image motion measurement based on hybrid opto-digital joint transform correlator

Hybrid opto-digital joint transform correlator (HODJTC) is effective for image motion measurement, but it is different from the traditional joint transform correlator because it only has one optical transform and the joint power spectrum is directly input into a digital processing unit to compute the image shift. The local cross-correlation image can be directly obtained by adopting a local Fourier transform operator. After the pixel-level location of cross-correlation peak is initially obtained, the up-sampling technique is introduced to relocate the peak in even higher accuracy. With signal-to-noise ratio ? 20 dB, up-sampling factor k ? 10 and the maximum image shift ? 60 pixels, the root-mean-square error of motion measurement accuracy can be controlled below 0.05 pixels.

SU‐FF‐J‐55: Acceptance Testing and Quality‐Assurance Protocols for the Calypso® 4D Localization System™ and Q‐Fix®/Calypso® Couchtop

Purpose: To develop quality‐assurance (QA) protocols for the Calypso® 4D Localization System including acceptance testing, commissioning, daily QA, monthly QA, and annual QA. Method and Materials: The following acceptance testing/commissioning/QA protocols were developed following the implementation of the Calypso System at our institution. Both the Calypso System itself and a new Q‐fix/Calypso couch‐top were tested. The Calypso System tests consisted of (1) transponder imaging and treatment planning, (2) localization and tracking accuracy, (3) accuracy of motion measurements, (4) operational range of the array and system integration, (5) on‐board x‐ray imaging when using the array, (6) safety and collision checks, (7) automatic couch repositioning accuracy and functionality, (8) clinical application protocols and workflow. The Q‐fix/Calypso® couch‐top tests included mechanical tests (accuracy of motion along the three axes, angle indicator accuracy, isocentricity, and sag) as well as measurements of the attenuation through the new couch top and rails. Results: Localization/Tracking Accuracy: The Calypso‐measured displacements agreed with the known displacements within 1mm. Motion: The Calypso System measured the period and amplitude of the longitudinal sinusoidal motion within 0.1s and 0.1cm. Operational Range: We measured the operational range of the Calypso® array to be 14cm lateral, 15.8cm longitudinal, and 28.7cm vertical. Imaging: kV imaging clearly delineates the transponders. The presence of the array introduces significant noise into the CBCT scan, but still allows for localization of the transponders. Safety Checks: The appropriate warning messages were generated when the test patient plans were used. Automatic Couch Repositioning: The ACR correctly repositioned the phantom within 0.05cm of a known 3D shift. Q‐Fix/Calypso® Couch‐top: Recalibrating the vertical couch position was required (3mm change). The maximum couch attenuation was 3.6%, and rail attenuation was 9.9%. Conclusion: The Calypso® 4D Localization System was successfully implemented. Collection of baseline accuracy is important to ensure the continuing functionality of the system.

Can magnetic resonance imaging–derived bone models be used for accurate motion measurement with single‐plane three‐dimensional shape registration?

The purpose of this study was to compare three-dimensional (3D) kinematic measurements from single-plane radiographic projections using bone models created from magnetic resonance imaging (MRI) and computed tomography (CT). MRI is attractive because there is no ionizing radiation, but geometric field distortion and poor bone contrast degrade model fidelity compared to CT. We created knee bone models of three healthy volunteers from both MRI and CT and performed three quantitative comparisons. First, differences between MRI- and CT-derived bone model surfaces were measured. Second, shape matching motion measurements were done with bone models for X-ray image sequences of a squat activity. Third, synthetic X-ray images in known poses were created and shape matching was again performed. Differences in kinematic results were quantified in terms of root mean square (RMS) error. Mean differences between CT and MRI model surfaces for the femur and tibia were -0.08 mm and -0.14 mm, respectively. There were significant differences in three of six kinematic parameters comparing matching results from MRI-derived bone models and CT-derived bone models. RMS errors for tibiofemoral poses averaged 0.74 mm for sagittal translations, 2.0 mm for mediolateral translations, and 1.4 degrees for all rotations with MRI models. Average RMS errors were 0.53 mm for sagittal translations, 1.6 mm for mediolateral translations, and 0.54 degrees for all rotations with the CT models. Single-plane X-ray imaging with model-based shape matching provides kinematic measurements with sufficient accuracy to assess knee motions using either MRI- or CT-derived bone models. However, extra care should be taken when using MRI-derived bone models because model inaccuracies will affect the quality of the shape matching results.

End-Stopping and the Aperture Problem: Two-Dimensional Motion Signals in Macaque V1

Our perception of fine visual detail relies on small receptive fields at early stages of visual processing. However, small receptive fields tend to confound the orientation and velocity of moving edges, leading to ambiguous or inaccurate motion measurements (the aperture problem). Thus, it is often assumed that neurons in primary visual cortex (V1) carry only ambiguous motion information. Here we show that a subpopulation of V1 neurons is capable of signaling motion direction in a manner that is independent of contour orientation. Specifically, end-stopped V1 neurons obtain accurate motion measurements by responding only to the endpoints of long contours, a strategy which renders them largely immune to the aperture problem. Furthermore, the time course of end-stopping is similar to the time course of motion integration by MT neurons. These results suggest that cortical neurons might represent object motion by responding selectively to two-dimensional discontinuities in the visual scene.