Template Image Research Articles

A Dynamic Adjust‐Head Siamese network for object tracking

AbstractSiamese network based trackers formulate tracking as a similarity matching problem between a target template and a search region. Virtually all popular Siamese trackers use cross‐correlation to measure the similarity between the deep feature of template and search image. However, the emphasis for feature extraction in different parts of the image are the same. Besides, the global matching between the template and search region also seriously neglects the part‐level information and the deformation of targets during tracking. In this study, to tackle the above issues, a simple but effective Dynamic Adjust‐Head (SiamDAH) model is proposed to extract features from different parts of an object. In addition, an improved pixelwise cross‐correlation model (PWCC) is designed to enhance the naive cross‐correlation operation to produce multiple similarity maps associated with different parts of the target. Experiments on serval challenging benchmarks including OTB‐100, GOT‐10k, LaSOT, and TrackingNet demonstrate that the proposed SiamDAH outperforms many state‐of‐the‐art trackers and achieves leading performance.

SAM: Self-Supervised Learning of Pixel-Wise Anatomical Embeddings in Radiological Images

Radiological images such as computed tomography (CT) and X-rays render anatomy with intrinsic structures. Being able to reliably locate the same anatomical structure across varying images is a fundamental task in medical image analysis. In principle it is possible to use landmark detection or semantic segmentation for this task, but to work well these require large numbers of labeled data for each anatomical structure and sub-structure of interest. A more universal approach would learn the intrinsic structure from unlabeled images. We introduce such an approach, called Self-supervised Anatomical eMbedding (SAM). SAM generates semantic embeddings for each image pixel that describes its anatomical location or body part. To produce such embeddings, we propose a pixel-level contrastive learning framework. A coarse-to-fine strategy ensures both global and local anatomical information are encoded. Negative sample selection strategies are designed to enhance the embedding's discriminability. Using SAM, one can label any point of interest on a template image and then locate the same body part in other images by simple nearest neighbor searching. We demonstrate the effectiveness of SAM in multiple tasks with 2D and 3D image modalities. On a chest CT dataset with 19 landmarks, SAM outperforms widely-used registration algorithms while only taking 0.23 seconds for inference. On two X-ray datasets, SAM, with only one labeled template image, surpasses supervised methods trained on 50 labeled images. We also apply SAM on whole-body follow-up lesion matching in CT and obtain an accuracy of 91%. SAM can also be applied for improving image registration and initializing CNN weights.

SiamAGN: Siamese attention-guided network for visual tracking

Most Siamese-based trackers utilize cross-correlation to calculate the similarity scores between the target template and the search image, which may cause the loss of spatial information and lead to inaccurate target estimation. To address this issue, we propose an attention-guided model under the Siamese framework for object tracking, named SiamAGN. Specifically, we combine the template and search image through the proposed feature fusion module. It contains a self-context interaction (SCI) module, cross-context interaction (CCI) module, and target location module (TLM). SCI based on self-attention learns global context by emphasizing channel-wise complementary features. CCI based on cross-attention explores rich dynamic context via the channel interaction between the template and the search image. TLM based on cross-attention reformulates the template according to the pixel-level similarity scores between the template and the search image, which can keep as much spatial information as possible and enable our model to predict more precise bounding boxes. Extensive experimental results on the GOT10k, OTB100, VOT2016, VOT2018, UAV123, and LaSOT benchmarks indicate that the proposed tracker SiamAGN achieves competitive performance compared with state-of-the-art trackers.

Multiple Thermal Sensor Array Fusion Toward Enabling Privacy-Preserving Human Monitoring Applications

Human-centric applications of a single thermal sensor array (TSA) have performed extremely well in many areas. However, most of these works have not yet reached the real applicability stage of the Internet of Things (IoT) applications. The main limitation of deploying such systems on a large scale is the challenge of fusing multiple TSAs to cover a wide inspection area, e.g., smart homes, hospitals, and many other domestic environments. On the other hand, objects that appear in the low-resolution thermal images acquired from TSA have low intraclass variations and high interclass similarities, making the identification of the overlapping regions through matching a comparable template image in multiple images very difficult. This article proposes a motion-based approach to fuse multiple TSAs and learn the domestic environment layout to enable further human-centred IoT applications to run in the cloud. Besides, a privacy improvement on utilizing these sensors in IoT applications is proposed. The proposed approach is evaluated with comprehensive experiments on different sensor placements and domestic environment conditions. This article shows an average performance of 92.5% accuracy using various machine learning techniques and use case scenarios.

Deep brain stimulation for extreme behaviors associated with autism spectrum disorder converges on a common pathway: a systematic review and connectomic analysis.

Individuals with autism spectrum disorder (ASD) may display extreme behaviors such as self-injury or aggression that often become refractory to psychopharmacology or behavioral intervention. Deep brain stimulation (DBS) is a surgical alternative that modulates brain circuits that have yet to be clearly elucidated. In the current study the authors performed a connectomic analysis to identify brain circuitry engaged by DBS for extreme behaviors associated with ASD. A systematic review was performed to identify prior reports of DBS as a treatment for extreme behaviors in patients with ASD. Individual patients' perioperative imaging was collected from corresponding authors. DBS electrode localization and volume of tissue activated modeling were performed. Volumes of tissue activated were used as seed points in high-resolution normative functional and structural imaging templates. The resulting individual functional and structural connectivity maps were pooled to identify networks and pathways that are commonly engaged by all targets. Nine patients with ASD who were receiving DBS for symptoms of aggression or self-injurious behavior were identified. All patients had some clinical improvement with DBS. Connectomic analysis of 8 patients (from the systematic review and unpublished clinical data) demonstrated a common anatomical area of shared circuitry within the anterior limb of the internal capsule. Functional analysis of 4 patients identified a common network of distant brain areas including the amygdala, insula, and anterior cingulate engaged by DBS. This study presents a comprehensive synopsis of the evidence for DBS in the treatment of extreme behaviors associated with ASD. Using network mapping, the authors identified key circuitry common to DBS targets.

Self-adjusting multilayer nonlinear coupled mapping for low-resolution face recognition

In practical surveillance scenes, effective recognition of low-resolution (LR) images is an urgent issue. At present, the improvement of methods for LR image recognition tasks mainly focuses on how to effectively extract information from LR images. This paper explores a novel self-adjusting multilayer nonlinear coupled mapping (SMNCM) network. The proposed SMNCM includes two main components, the multilayer nonlinear coupled mapping (MNCM) module, which is the feature extractor, and the self-adjusting feature fusion (SaFF) module. In the MNCM module, the LR template image set and multiple different high-resolution template image sets are used to construct a pyramid-like multicoupled mapping feature extraction network. In the SaFF module, the fusion weights of multiple features and the other parameters are collaboratively learned for the purpose of correct classification by the gradient learning network. To achieve this goal, a new gradient optimization model for fusion weights and other parameters is constructed. The features fused by the learned weights and the multiple features of the MNCM module are more suitable for classification tasks. Experimental results indicate that on the five databases, our proposed method outperforms the state-of-the-art approaches for LR face recognition. The ablation experiment suggests that each module in the proposed method can contribute to improving the LR image recognition performance.

An antennal electric signal detection system based on template matching

As the most efficient perception system in nature, the perception mechanism of the insect (such as honeybee) antennae is the key to imitating the high-performance sensor technology. An automated experimental device suitable for collecting electrical signals (including antenna reaction time information) of antennae was developed, in response to the problems of the non-standardized experimental process, interference of manual operation, and low efficiency in the study of antenna perception mechanism. Firstly, aiming at the automatic identification and location of insect heads in experiments, the image templates of insect head contour features were established. Insect heads were template-matched based on the Hausdorff method. Then, for the angle deviation of the insect heads relative to the standard detection position, a method that calculates the angle of the insect head mid-axis based on the minimum external rectangle of the long axis was proposed. Eventually, the electrical signals generated by the antennae in contact with the reagents were collected by the electrical signal acquisition device. Honeybees were used as the research object in this study. The experimental results showed that the accuracy of template matching could reach 95.3% to locate the bee head quickly, and the deviation angle of the bee head was less than 1°. The distance between antennae and experimental reagents could meet the requirements of antennae perception experiments. The parameters, such as the contact reaction time of honeybee antennae to sucrose solution, were consistent with the results of the manual experiment. The system collects effectively antenna contact signals in an undisturbed state and realizes the standardization of experiments on antenna perception mechanisms, which provides an experimental method and device for studying and analyzing the reaction time of the antenna involved in biological antenna perception mechanisms.

Structured Reporting in Multiple Sclerosis - Consensus-Based Reporting Templates for Magnetic Resonance Imaging of the Brain and Spinal Cord.

As a result of technical developments and greater availability of imaging equipment, the number of neuroradiological examinations is steadily increasing [1]. Due to improved image quality and sensitivity, more details can be detected making reporting more complex and time-intensive. At the same time, reliable algorithms increasingly allow quantitative image analysis that should be integrated in reports in a standardized manner. Moreover, increasing digitalization is resulting in a decrease in the personal exchange between neuroradiologists and referring disciplines, thereby making communication more difficult. The introduction of structured reporting tailored to the specific disease and medical issue [2, 3] and corresponding to at least the second reporting level as defined by the German Radiological Society (https://www.befundung.drg.de/de-DE/2908/strukturierte-befundung/) is therefore desirable to ensure that the quality standards of neuroradiological reports continue to be met.The advantages of structured reporting include a reduced workload for neuroradiologists and an information gain for referring physicians. A complete and standardized list with relevant details for image reporting is provided to neuroradiologists in accordance with the current state of knowledge, thereby ensuring that important points are not forgotten [4]. A time savings and increase in efficiency during reporting were also seen [5]. Further advantages include report clarity and consistency and better comparability in follow-up examinations regardless of the neuroradiologist's particular reporting style. This results in better communication with the referring disciplines and makes clinical decision significantly easier [6, 7]. Although the advantages are significant, any potential disadvantages like the reduction of autonomy in reporting and inadequate coverage of all relevant details and any incidental findings not associated with the main pathology in complex cases or in rare diseases should be taken into consideration [4]. Therefore, studies examining the advantages of structured reporting, promoting the introduction of this system in the clinical routine, and increasing the acceptance among neuroradiologists are still needed.Numerous specific templates for structured reporting, e. g., regarding diseases in cardiology and oncology, are already available on the website www.befundung.drg.de . Multiple sclerosis (MS) is an idiopathic chronic inflammatory and neurodegenerative disease of the central nervous system and is the most common non-trauma-based inflammatory neurological disease in young adults. Therefore, it has significant individual and socioeconomic relevance [8]. Magnetic resonance imaging (MRI) plays an important role in the diagnosis, prognosis evaluation, and follow-up of this disease. MRI is established as the central diagnostic method in the diagnostic criteria. Therefore, specific changes are seen on MRI in almost all patients with a verified MS diagnosis [9]. Reporting of MRI datasets regarding the brain and spinal cord of patients with MS includes examination of the images with respect to the relevant medical issue in order to determine whether the McDonald criteria, which were revised in 2017 [10] and define dissemination in time and space clinically as well as with respect to MRI based on the recommendations of the MAGNIMS groups [11, 12], are fulfilled. A more precise definition of lesion types and locations according to the recommendations of an international expert group [13] is discussed in the supplementary material. Spinal cord signal abnormalities are seen in up to 92 % of MS patients [14-16] and are primarily located in the cervical spine [15]. The recommendations of the MAGNIMS-CMSC-NAIMS working group published in 2021 [11] explicitly recommend the use of structured reporting for MS patients.Therefore, a reporting template for evaluating MRI examinations of the brain and spinal cord of patients with MS was created as part of the BMBF-funded DIFUTURE consortium in consensus with neuroradiological and neurological experts in concordance with the recommendations mentioned above [11] and was made available for broad use (https://github.com/DRGagit/ak_befundung). The goal is to facilitate efficient and comprehensive evaluation of patients with MS in the primary diagnostic workup and follow-up imaging. These reporting templates are consensus-based recommendations and do not make any claim to general validity or completeness. The information technology working group (@GIT) of the German Radiological Society and the German Society for Neuroradiology strive to keep the reporting templates presented here up-to-date with respect to new research data and recommendations of the MAGNIMS-CMSC-NAIMS group [11]. KEY POINTS:: · consensus-based reporting templates. · template for the structured reporting of MRI examinations of patients with multiple sclerosis. · structured reporting might facilitate communication between neuroradiologists and referring disciplines. CITATION FORMAT: · Riederer I, Mühlau M, Wiestler B et al. Structured Reporting in Multiple Sclerosis - Consensus-Based Reporting Templates for Magnetic Resonance Imaging of the Brain and Spinal Cord. Fortschr Röntgenstr 2023; 195: 135 - 138.

Plastron color patterns allow for individual photo-identification in two different chelonian species

Natural marks have increasingly been used as a tool for individual identification in capture–mark–recapture techniques. Photo-identification is a noninvasive alternative to traditional marking techniques, allowing individual recognition of species through time and space. We tested the APHIS (Automatic Photo Identification Suite) software as a software capable of identifying individuals of Hermann’s Tortoise ( Testudo hermanni Gmelin, 1789) and European Pond Turtle ( Emys orbicularis (Linneaus, 1758)) in different populations during capture–release sessions in the field based on plastron color patterns, since they can be used as natural marks for identification. For this individual identification, spot pattern matching (SPM) and image template matching (ITM) procedures were tested, achieving 100% success of individuals recognized in both procedures and visually verified by comparing the images. However, the ITM procedure was more efficient at recognizing recaptures than SPM because ITM allowed faster recapture verification, since most of the matches were directly placed in the first position on the candidate list. Previous studies used photo-identification on freshwater or sea turtles but never with terrestrial tortoise species. Consequently, it was corroborated that APHIS is a competent and efficient software considering photo-identification of T. hermanni and E. orbicularis, and that it can be applied to close species with similar and unique individual color patterns in their plastron.

Contactless Material Tensile Testing Using a High-Resolution Camera

This article deals with the use of contactless measurement with a high-resolution imaging device during tensile testing of materials in a universal tearing machine (UTM). Setting the material parameters in tensile testing is based on changes in the geometrical properties of the sample being tested. In this article, authors propose the method and system for automated measuring the height, width, and crack occurrence during tensile testing. The system is also able to predict the location of crack occurrence. The proposed method is based on selected algorithms of image analysis, feature extraction, and template matching. Our video extensometry, working with common inspection cameras operating in visible range, can be an alternative method to expensive laser extensometry machines. The motivation of our work was to develop an automated measurement system for use in a UTM.

Automatic macaque brain segmentation based on 7T MRI

BackgroundIn monkey neuroimaging, particularly magnetic resonance imaging (MRI) studies, quick and accurate automatic macaque brain segmentation is essential. However, there are few processing and analysis tools dedicated to automatic brain tissue segmentation and labeling of the macaque brain on a subject-specific basis. As a result, currently most adopted methods are through direct implementation of existing tools that have been designed for human brain. However, the operation steps combining different functional modules of a variety of processing and analysis tool software are inevitably complicated, cumbersome, time-consuming and labor-intensive. New methodIn this study, we proposed a novel quick and accurate automatic macaque brain segmentation method based on multi-atlas registration and majority-vote algorithm. First, the single-atlas method based on S-HAMMER is used to register each template image of the reference atlas set (including brain tissue labeled images and brain anatomical structure labeled images) to the preprocessed image to be segmented. Thus, we obtain the corresponding deformation field and spatially transform the labeled image, and then get multiple segmentation results by local weighted voting method, which perform label fusion to obtain the final labeled images of brain structures segmentation result. ResultsBy collecting high SNR and high spatial resolution images of macaque brain images from our 7T human MRI scanner, we have constructed two brain templates for each individual macaque subject, and macaque brain tissues and brain anatomical structure by one-atlas method. However, segmentation result of single-atlas method is not much accurate in some brain tissue area. It takes about 2 h and need more manual correction for segmentation. Automatic segmentation of macaque brain structure based on multi-atlas method was reasonably successful, the accuracy of segmentation was greatly improved without manual correction. Also, the proposed method provided good tissue fitting to V1 with smooth and continuous boundary. The Dice similarity of multi-atlas method showing 3.24%, 4.24%, 2.55%, 2.85%, 3.05%, and 0.35% improvement in image slices of 63, 66, 70, 71, 99 and 100, respectively. The entire processing time for the construction of a single template map took ~40 min. ConclusionsThis study proposed a novel automatic segmentation method of individual macaque brain structure based on multi-atlas registration method, which is concise and reliable. It may offer a valuable tool to applications in the field of brain and neuroscience research using the macaque as an experimental animal model.

Dynamic imaging using motion-compensated smoothness regularization on manifolds (MoCo-SToRM)

Objective. We introduce an unsupervised motion-compensated reconstruction scheme for high-resolution free-breathing pulmonary magnetic resonance imaging. Approach. We model the image frames in the time series as the deformed version of the 3D template image volume. We assume the deformation maps to be points on a smooth manifold in high-dimensional space. Specifically, we model the deformation map at each time instant as the output of a CNN-based generator that has the same weight for all time-frames, driven by a low-dimensional latent vector. The time series of latent vectors account for the dynamics in the dataset, including respiratory motion and bulk motion. The template image volume, the parameters of the generator, and the latent vectors are learned directly from the k-t space data in an unsupervised fashion. Main results. Our experimental results show improved reconstructions compared to state-of-the-art methods, especially in the context of bulk motion during the scans. Significance. The proposed unsupervised motion-compensated scheme jointly estimates the latent vectors that capture the motion dynamics, the corresponding deformation maps, and the reconstructed motion-compensated images from the raw k-t space data of each subject. Unlike current motion-resolved strategies, the proposed scheme is more robust to bulk motion events during the scan.

Iterative Recognition of Bird's Nest in Aerial Photograph of High Voltage Transmission Tower

Background: Unmanned aerial vehicle automatic fault identification of high voltage transmission equipment has entered the stage of product development, in which image recognition technology is one of the key technologies. There are often bird nests on the high voltage transmission tower, which have an impact on the transmission, so they needs to be automatically detected. Methods: For bird's nest recognition, a novel algorithm is proposed. Firstly, the template image and auxiliary function are used to construct the system, and the iterative point trajectory set, called feature set, is obtained by iteration; Then, the target image is searched by blocks, and the image blocks are iterated with the same auxiliary function to construct the iterative system, and the set of iterative point tracks to be identified is obtained. The correlation coefficient is calculated by comparing the feature set with those to be recognized. And we can confirm whether the image block is a bird's nest according to the size of correlation coefficient. Results: Different from the general image recognition method, the iterative algorithm obtains the iterative trajectory by iterating the image and the auxiliary function, and takes the iterative trajectory as the image feature, then the feature comparison is carried out, so as to achieve the goal of bird's nest recognition. The effectiveness of the method is proved by experiments. The recognition accuracy is 99% by experiment on the self-built data set. Conclusion: This paper proposes a new feature extraction algorithm for bird's nest recognition. The algorithm based on iteration is very simple and effective for bird's nest identification. As a new method, it needs further development and improvement.

Real-time Siamese visual object tracking using attention and anchor-free mechanism

Trackers based on Siamese have consistently demonstrated superior performance in tracking visual objects. The majority of existing trackers calculate the features of the target template and search image independently and then estimate the target's scale and aspect ratio using either a multi-scale searching scheme or pre-defined anchor boxes. This paper proposed a Siamese attention network for tracking visual objects. An attention fusion mechanism is generated using pixel-level matching of template and search features. The framework proposed is anchor-free, making it both simple and effective. Extensive experiments on visual tracking benchmark VOT2018 and UAV123 demonstrate that our tracker operates at 42 fps and achieves state-of-the-art performance.

Robot arm grasping using learning-based template matching and self-rotation learning network

Applying deep neural network models to robot-arm grasping tasks requires the laborious and time-consuming annotation of a large number of representative examples in the training process. Accordingly, this work proposes a two-stage grasping model, in which the first stage employs learning-based template matching (LTM) algorithm for estimating the object position, and a self-rotation learning (SRL) network is then proposed to estimate the rotation angle of the grasping objects in the second stage. The LTM algorithm measures similarity between the feature maps of the search and template images which are extracted by a pre-trained model, while the SRL network performs the automatic rotation and labelling of the input data for training purposes. Therefore, the proposed model does not consume an expensive human-annotation process. The experimental results show that the proposed model obtains 92.6% when testing on 2400 pairs of the template and target images. Moreover, in performing practical grasping tasks on a NVidia Jetson TX2 developer kit, the proposed model achieves a higher accuracy (88.5%) than other grasping approaches on a split of Cornell-grasp dataset.

An Enhanced Visual Object Tracking Approach based on Combined Features of Neural Networks, Wavelet Transforms, and Histogram of Oriented Gradients

In this paper, a new Visual Object Tracking (VOT) approach is proposed to overcome the main problem the existing approaches encounter, i.e. the significant appearance changes which are mainly caused by heavy occlusion and illumination variation. The proposed approach is based on a combination of Deep Convolutional Neural Networks (DCNNs), Histogram of Oriented Gradient (HOG) features, and discrete wavelet packet transforms. The problem of illumination variation is solved by incorporating the coefficients of the image discrete wavelet packet transform instead of the image template to handle the case of images with high saturation in the input of the used CNN, whereas the inverse discrete wavelet packet transforms are used at the output for extracting the CNN features. By combining four learned correlation filters with the convolutional features, the target location is deduced using multichannel correlation maps at the CNN output. On the other side, the maximum value of the resulting maps from the correlation filters with convolutional features produced by the previously obtained HOG feature of the image template are calculated and are used as an updating parameter of the correlation filters extracted from CNN and from HOG. The major aim is to ensure long-term memory of the target appearance so that the target item may be recovered if tracking fails. In order to increase the performance of HOG, the coefficients of the discrete packet wavelet transform are employed instead of the image template. The obtained results demonstrate the superiority of the proposed approach.

A Simple and Strong Baseline for Universal Targeted Attacks on Siamese Visual Tracking

Siamese trackers are shown to be vulnerable to adversarial attacks recently. However, the existing attack methods craft the perturbations for each video independently, which comes at a non-negligible computational cost. In this paper, we show the existence of universal perturbations that can enable the targeted attack, e.g., forcing a tracker to follow the ground-truth trajectory with specified offsets, to be video-agnostic and free from inference in a network. Specifically, we attack a tracker by adding a universal translucent perturbation to the template image and adding a fake target, i.e., a small universal adversarial patch, into the search images adhering to the predefined trajectory, so that the tracker outputs the location and size of the fake target instead of the real target. Our approach allows perturbing a novel video to come at no additional cost except the mere addition operations – and not require gradient optimization or network inference. Experimental results on several datasets demonstrate that our approach can effectively fool the Siamese trackers in a targeted attack manner. We show that the proposed perturbations are not only universal across videos, but also generalize well across different trackers. Such perturbations are therefore doubly universal, both with respect to the data and the network architectures. Our code is available at https://github.com/lizhenbang56/SiamAttack.

A Universal Retinal Image Template for Automated Screening of Diabetic Retinopathy

A Universal Retinal Image Template for Automated Screening of Diabetic Retinopathy

Investigation and optimization of a novel precision measurement method based on template matching

The polar microstructure is a 10 μm aperiodic surface pattern, which is designed to achieve nanoscale resolution and applied in the precision measurement area. In this paper, an investigation of the major factors of a novel template-matching-based precision measurement method that integrates with the polar microstructure and computer vision (IPMCV) is proposed. In order to optimize the measurement performance of the IPMCV method, three crucial factors are determined according to the template-matching method: the size of the global image, the size of the template image and the number of interpolated sub-pixels. According to the measurement accuracy, the size of the global image has little influence on the position error, but significantly influences the standard deviation value of the error gradually. In addition, the size of the template image helps to significantly improve the measurement accuracy, and the increase in the number of interpolated sub-pixels increases the measurement accuracy within a certain range but has no effect afterward. The experimental results have great significance on the determination and optimization of parameters for the IPMCV method to achieve a better measurement performance.

Technical note: Analysis of brachytherapy source movement by high-speed camera.

To assess the accuracy of the movement of a brachytherapy source using a high-speed camera for evaluating source position, dwell time, and transit dose. A high-speed camera was used to record the source position of an Ir-192 source relative to a ruler within a custom positioning jig in a remote afterloading system. The analyzed frames can be used to assess dwell positions and times. Treatment plans had multiple dwell times equal to 0.1, 0.5, 1.0, and 2.0 s in 2.5- and 5-mm step sizes. Images were acquired at a rate of 146 frames/s. Acquired images were processed to automatically track the actual source using the correlation between a template image and each frame. The brachytherapy dose calculation formalism (AAPM TG43-U1) was applied to each frame to evaluate the transit dose contribution to the total dose. The differences in measured source positions from the nominal for dwell times equal to 0.1, 0.5, 1.0, and 2.0 s in treatment plans were approximately ≤1mm. The corresponding differences in measured dwell times from the nominal values at 5 mm steps were -15, -9, -5, and 5 ms, respectively. The source velocities at 5mm steps were approximately 393mm/s. The dose differences at 5mm from the source movement with and without the transit dose for these dwell times were 38%, 7%, 3%, and 2%, respectively. Recording a brachytherapy source using a high-speed camera allowed the evaluation of positional and dwell time accuracies as well as dosimetry assessments, such as the transit dose, based on the application of AAPM TG-43U1.