We missed few measurements because of marker loss. This could happen while they are attached only with a double-sided adhesive tape, and can get lost or displaced, which is a disadvantage in motion ana- lysis. The advantage of the 3Dmotion capture sys- tem is the dynamic evaluation of the wrist and all finger joints simultaneously. Therefore, it can be ap- plied for the assessment of the ROM as well as dy- namic functional tasks, such as activities of daily living. The main advantages of the manual goniom- etry are that it is much easier to implement in the clinical setting. Our study shows that in applications where the goniometer is not precise enough, motion analysis is a possible alternative due to its lower MDD. The choice of the method has to be in ac- cordance with the research question and the ex- pected or clinically relevant change in joint ROM.
Although both cues coexist in the natural world, each are sufficient to generate an MID percept in isolation (3–11). Due to constraints placed on the disparity and velocity computations they depend on, CD and IOVD operate optimally across reasonably distinct spatial and temporal ranges (12) and thus may be subserved by dissociable neural mechanisms. Recent neuroimaging studies have emphasized the role of the human medial temporal (hMT + ) area in processing both CD and IOVD (9), while corresponding neurophysiological evidence has identified cells tuned to 3Dmotion direction in this area (13, 14). Although hMT + integrates both motion and disparity cues (15–18), no evidence for cross-cue adaptation between CD and IOVD has been found (6). This implies that separate subpopulations of neurons are tuned to either CD or IOVD within a common network of areas (19). Other work suggests differences between CD and IOVD pro- cessing, both in an extended network of regions outside the hMT + as well as in the pathways that relay cues to the hMT + . By comparing the fMRI response to a CD-type stimulus against the response to a static disparity plane, an area anterior to hMT + , the putative cyclopean stereo
Image-based tracking of objects is becoming an important area of research within computer vision and image processing community. However, there are still challenges with regard to robustness of the algorithms. This paper explains an algorithm to track the pre-defined objects within stereo videos (image sequences) in a condition where cameras are fixed and objects are moving. The tracking technique used in this research, applies the intensity-based least squares matching (LSM) to find the correspondent targets in successive frames. Unlike ordinary correlation-based registration methods, LSM takes both geometric and radiometric variations of images into account, succeeding at sub-pixel scale feature tracking. The proposed algorithm combines three dimensional updated object constraints with adaptive two dimensional LSM to ensure the robustness and convergence to optimum solution. While tracking the features in stereo images, photogrammetric techniques are applied to extract the coordinates of the features in object space which result in detecting the 3D trajectory of the features. The average tracking error is about 0.11 pixel at x-direction and 0.15 pixel at y-direction. The 3Dmotion vectors are modeled by mean magnitude precision of 0.65 millimeter and orientation precision of 0.27 degree.
In this paper, we propose a non-intrusive three-step heart rate and rhythm estimation system via 3Dmotion tracking in depth video. First, we restore the low-bit- depth, noise-corrupted depth images via a joint bit-depth enhancement / denoising procedure, using a graph- signal smoothness prior for regularization. Second, we track an automatically detected head region throughout the depth video to deduce 3Dmotion vectors. The detected vectors are fed back to the depth restoration module in a loop to ensure that the motion information in the two modules are consistent, resulting in a boost in performance for both restoration and motion tracking. Third, we project the computed 3Dmotion vectors onto its principal component via PCA for 1D signal analysis: trend removal, band-pass filtering and wavelet-based motion denoising. Finally, we estimate the heart rate via Welch power spectrum analysis, and estimate the heart rhythm via peak detection. Experimental results show robustness to different views, and accurate estimation of the heart rate and rhythm using our proposed algorithm compared to the values estimated by a portable finger pulse oximeter. Unlike conventional texture (RGB and grayscale) based methods that require the subject to face the camera for reasonable measurements, our proposed scheme estimates the heart rate and rhythm accurately with various views, such as front, side and back views, and even when the subject is wearing a mask.
Unfolding Thailand is a project that aims to bring a new approach in introducing the country as well as promoting the tourism. My specific scope of this project is to create a visual representation of popular destinations from the northern, central, and southern areas through the use of 3Dmotion graphics. These three of the six regions in Thailand are chosen because they vary in geography, have a very distinctive characteristic and most importantly, they are the most popular vacation destinations for locals and tourists alike. Northern Thailand is surrounded by mountain ranges and is a home of exclusively stunning Lanna-style temples. Central Thailand, focusing mainly on Bangkok, the capital of Thailand was ranked as the most visited city in the world in 2013 according to TIME magazine 1 .
Other Areas Involved in the Extraction of 3DMotion. The role of hMT + in CD and IOVD processing has been documented previously (9, 13, 14), with emphasis on 2D and 3Dmotion being processed by the same cortical pathways (41). In addition to MID responses in classic motion pathways, from V1 to hMT + , we measured strong CD-driven responses in area IPS-0. The human IPS is involved in a variety of cognitive functions, including the top-down control of vi- sual attention and eye movements, which modulates activity in ear- lier visual areas (62 – 64). In addition, the IPS also contains distinct populations of neurons that are sensitive to motion (65) and 3D structure from motion (66, 67). This may explain why activation in IPS-0 was more pronounced for CD stimuli than for IOVD stimuli. Because IOVD stimuli lack the concrete depth information provided by the binocular disparity cues in the CD stimulus (8), they are much less likely to convey shape or form information and are thus less likely to engage form-from-motion mechanisms. IPS-0 activation observed here may constitute a part of the MID pathway that is involved in extracting 3D shape from disparity and the allocation of visual attention, rather than in extracting 3Dmotion per se.
Abstract: As my research concerned it is basically concentrate on “To Design and Fabrication of 3Dmotion mixer Industrial Mixers and Blenders are used to mix or blend a wide range of materials used in different industries including the food, chemical, pharmaceutical, plastic and mineral industries. They are mainly used to mix different materials using different types of blades to make a good quality homogeneous mixture. Included are dry blending devices, paste mixing designs for high viscosity products and high shear models for emulsification, particle size reduction and homogenization.
Massage therapy students learn gait assessment as a part of physical assessment. Traditionally, the GAS has been low-tech. The benefit of this method, discov- ered through this study, lies in the interactivity, and subsequent engagement, of students. The integration of technology, in the form of the Qualisys 3D MCS, was novel and provided students the opportunity to visualize the theory they were learning. However, students reported that they were not engaged dur- ing the experience. In the future, we recommend combining the traditional module with technology to optimize the benefits of each. The Qualisys 3Dmotion capture system can cost hundreds of thou- sands of dollars. This combination of technology is recommended for programs that can arrange access to existing technology. It is not recommended that massage therapy programs invest in this technology, unless they can share the cost with other programs in their institution.
From the obtained 3D coordinates of the markers the orientation can be calculated using the known placement of the markers. Figure 2.2 shows that the distance between markers M4&M1, M3&M2, M8&M5, and M7&M6 is only in the direction of the x-axis, markers M2&M1, M3&M4, M6&M5, and M8&M7 in the direction of y, and markers M5&M1, M6&M2, M7&M3, and M8&M4 in the direction of z. From the set of coordinates received from the measurements, one or more of each set mentioned above will be looked for. Because of the way the target is designed, it is possible to find at least one pair out of two of the sets. The distance vector point- ing from one to the other marker of each of these detected pairs will be calculated. If only pairs were found out of two of the sets, a vector along the last axis can be obtained by the cross product of the two known vectors. (This step will be done after normalizing the vectors.) Because all the vector lengths are equal to the length of the ribs of the cube, it is possible to normalize the vectors by dividing them by the rib length. This results in vectors which are of length one and that have their direction along one of the axis of the cubes frame, expressed in the reference frame.
The expected output of this project is that motion capture simulation video with effect on minor car repair. User was able to improve their knowledge about minor car repair through these videos. The videos will be used motion capture simulation method and addition with visual and sound effect. Thus, the video is more realistic and users will fell the immersive environment.
reconstruction (Fig. 4b). Second, there is significant signal variation from slice to slice in the axial and coronal views (Fig. 4a), whereas the motion corrected version produces consistent volumetric information (Fig. 4b). Third, a corrupted shot seems to be spoiling brain structures imaged in the axial view (Fig. 5a) but after motion correction, an artifact-free image can be recovered (Fig. 5b). Fourth, damaged slices are also affecting a large proportion of the sagittal and coronal information (Fig. 5a), with a much more consistent retrieval when motion correction is applied (Fig. 5b). Regarding aggregated results on the cohort listed in Fig 3, first, motion correction improves the quality of obtained reconstructions in all the cases studied. Second, in most of them motion is fully or almost fully recovered, with 130/131 (99.24%) T 2 s and 104/108 (96.30%) T 1 s showing
As an alternative to motion tracking, [Joshi et al. 2003] present a learning approach for the estimation of generative 3D morph mod- els based on a combination of a reference 3D head model and a RBF deformation approach driven by sparse Motion Capture data projected onto the reference model. Similar to that idea, a muscle- based head animation system has been demonstrated by [Sifakis et al. 2005] that uses motion capture data in a non-linear optimiza- tion process to estimate facial muscle activation parameters. For better generalization across identities, [Blanz and Vetter 1999] used a statistical approach for which a large corpus of colored 3D scanned head models in neutral pose were obtained and automati- cally put into correspondence. This 3D morphable model was aug- mented with scans of facial expressions and was subsequently used to track and alter facial motion in video sequences [Blanz et al. 2003]. [Vlasic et al. 2005] presented a video-driven motion retar- geting approach for animation that controls animation factors such as the identity, type of expression and visemes of animated head models. These 3D head models are determined by a multi-linear model estimation based on tensor algebra and are directly estimated from high-temporal resolution 3D scans that are in dense correspon- dence.
Abstract. With the development of industrial automation, location measurement of 3D objects is becoming more and more important, especially as it can provide necessary positional parameters for the manipulator to grasp the object accurately. In view of the disabled object which is in widespread use currently, its image is captured to obtain positional parameters and transmitted to manipulators in industry. The above process is delayed, affecting the work efficiency of the manipulator. A method for calculating the position information of target object in motion is proposed. This method uses monocular vision technology to track 3D moving objects ， then uses contour sorting method to extract the minimum constrained contour rectangle, and combines the video alignment technology to realize the tracking. Thus, the measurement error is reduced. The experimental results and analysis show that the adopted measurement method is effective.
spatial modulation of magnetization (CSPAMM) . These methods can either be applied in conjunction with two dimensional (2D) or three dimensional (3D) im- aging. In 2D acquisitions, multiple slices are imaged along the left ventricle (LV) and strain maps are calcu- lated slice-by-slice. Two-dimensional acquisitions re- quire additional techniques in order to compensate for through-plane motion. To this end, slice following [15,16], acquisition of additional orthogonal slices  or the encoding of through-plane displacement [18,19] (zHARP) have been proposed. While reconstruction of 3D strain patterns from two bi-planar acquisitions does require interpolation, slice-following techniques provide only a projection of true 3Dmotion onto a 2D subspace. Using zHARP additional gradients in through-slice dir- ection are applied to estimate through-plane displace- ment by solving a set of linear equations .
Dynamic three-dimensional (3D) modeling of real-world objects using multiple cameras has been an active research area in recent years [1–5]. Since such sequential 3D mod- els, which we call 3D video, are generated employing a lot of cameras and represented as 3D polygon mesh, realistic rep- resentation of dynamic 3D objects is obtained. Namely, the objects’ appearance such as shape and color and their tem- poral change are captured in 3D video. Therefore, they are di ﬀ erent from conventional 3D computer graphics and 3Dmotion capture data. Similar to 2D video, 3D video consists of consecutive sequences of 3D models (frames). Each frame contains three kinds of data such as coordinates of vertices, connection, and color.
One solution to model human motion priors is to construct statistical mo- tion models from prerecorded human motion data [1–3]. Howe and colleagues  learned human motion priors with a Mixture-of-Gaussians density model and then applied the learned density model for constraining the 3Dmotion search within a Bayesian tracking framework. Brand  applied Hidden Markov Models (HMMs) to statistically model dynamic full-body movements and used them to transform a sequence of 2D silhouette images into 3D human motion. Pavlovic and his colleagues  introduced switching linear dynamic systems (SLDS) for human motion modeling and present impressive results for 3D human motion synthesis, classification, and visual tracking. A number of researchers have also constructed various statistical models for human poses and used them to sequen- tially transform 2D silhouette images into 3D full-body human poses [4–6].
Action sport cameras (ASC) are currently adopted mainly for entertainment purposes but their uninterrupted technical improvements, in correspondence of cost decreases, are going to disclose them for three-dimensional (3D) motion analysis in sport gesture study and athletic performance evaluation quantitatively. Extending this technology to sport analy- sis however still requires a methodologic step-forward to making ASC a metric system, encompassing ad-hoc camera setup, image processing, feature tracking, calibration and 3D reconstruction. Despite traditional laboratory analysis, such requirements become an issue when coping with both indoor and outdoor motion acquisitions of athletes. In swim- ming analysis for example, the camera setup and the calibration protocol are particularly demanding since land and underwater cameras are mandatory. In particular, the underwa- ter camera calibration can be an issue affecting the reconstruction accuracy. In this paper, the aim is to evaluate the feasibility of ASC for 3D underwater analysis by focusing on cam- era setup and data acquisition protocols. Two GoPro Hero3+ Black (frequency: 60Hz; image resolutions: 1280×720/1920×1080 pixels) were located underwater into a swimming pool, surveying a working volume of about 6m 3 . A two-step custom calibration procedure, consisting in the acquisition of one static triad and one moving wand, carrying nine and one spherical passive markers, respectively, was implemented. After assessing camera param- eters, a rigid bar, carrying two markers at known distance, was acquired in several positions within the working volume. The average error upon the reconstructed inter-marker dis- tances was less than 2.5mm (1280×720) and 1.5mm (1920×1080). The results of this study demonstrate that the calibration of underwater ASC is feasible enabling quantitative kine- matic measurements with accuracy comparable to traditional motion capture systems.
We present an action-specific model of human motion suitable for many applications, that has been successfully used for full body tracking [4, 5, 17]. In this paper, we explore and extend its capabilities for gait analysis and recognition tasks. Our action-specific model is trained with 3Dmotion capture data for the walking action from the CMU Graphics Lab Motion capture database. In our work, human postures are represented by means of a full body 3D model composed of 12 limbs. Limbs’ orientations are represented within the kinematic tree using their direction cosines . As a result, we avoid singularities and abrupt changes due to the representation. Moreover, near configurations of the body limbs account for near positions in our representation at the expense of extra parameters to be included in the model. Then, PCA is applied to the training data to perform dimensionality reduction over the highly correlated input data. As a result, we obtain a lower-dimensional representation of human postures which is more suitable to describe human motion, since we found that each dimension on the PCA space describes a natural mode of variation of human motion. Additionally, the main modes of variation of human gait are naturally represented by means of the principal components found. This leads to a coarse-to-fine representation of human motion which relates the precision of the model with its complexity in a natural way and makes it suitable for diﬀerent kinds of applications which demand more or less complexity in the model.
Silhouette coherence is an improved technique of previous silhouette-based approach where it can deal with partial or truncated silhouette. This technique can be used to exploits all the information not only at the epipolar tangency points but also in the silhouette. Although it is better than other silhouette-based technique this approach facing a major problem in producing the final result of 3D image. The silhouette calculation is relative sensitive for error such as wrong camera calibration. It will cause problem for the intersection of the silhouette cones thus produces bad results for the 3D shapes . Some of the factor that contribute to this issues are on the techniques of 2D images sequence, background color, and calibration technique.