To solve these problems, we propose a method to in- tegrate the learning-based and model-based methods to improve the estimation accuracy. An initial pose is de- termined using regression analysis in the learning-based approach, and the estimation method is switched to a particle filter in the model-based approach to improve the precision. Unfortunately, given the large dimension- ality of a 3D human model space, it is almost impractical to apply particle filtering directly as a large number of particles is required to adequately approximate the un- derlying probability distribution in the human pose space. Therefore, we first use PCA to learn the eigenspace of each motion. Then, the optimal human pose is efficiently searched in the eigenspaces selected according to the estimated type of human motion in the input images.
As the number of points in the database grows larger, several problems with this approach begin to appear. First, it becomes harder to find true nearest neighbors due to the approximate nature of high-dimensional search. Moreover, the nearest neighbor might very well be an incorrect match (even if a true match exists in the database) due to similar- looking visual features in different parts of the world. Even if the closest match is correct, there may still be many other similar points, such that the distances to the two nearest neighbors have similar values. Hence, in order to get good recall of correspondence, the ratio test threshold must be set ever higher, resulting in poor precision (i.e., many outlier matches). Given such noisy correspondence, RANSAC methods will need to run for many rounds to find a consistent pose, and may fail outright. To address this problem, we introduce two techniques that yield much more efficient and reliable pose estimates from very noisy correspondences: a co-occurrence-based sampling prior for speeding up RANSAC and a bidirectional matching scheme to improve the set of putative matches.
The goal of model-based approach is to construct the function that gives the likelihood of the image, given a set of parameters which include body configuration parameters, body shape and appearances. The approach usually adopts an articulated model to represent the relationship among human body parts as a kinematics tree, consisting of divisions linking by joints. The prior knowledge describing kinematic properties by shape, texture and appearance has been used to make the problem tractable. One of the fundamental representations is the Johansson’s moving light displays . It adopted a relatively simple representation of the human body called the stick figure that consists of line segments linked by joints. It was demonstrated that relatively little information (motion of a set of selected points on the body) is needed for humans to perform reconstruction of the body configurations. Poseestimation is challenging due to its non-rigid nature, self-occlusion, variable appearance, and high degree of freedom. By incorporating articulation knowledge, model-based approaches are able to overcome these problems to a great extent and are actively explored by many researchers.
There are two reasons the gradients are unneeded: Our lifting 3D model we use makes its best predictions when the 2D predictions of the same layer are closest to ground truth, and this is a constraint naturally enforced by the objective of equation (8) of the main paper. Further, as with Convolutional Pose Machines  our architecture suffers from problems with vanishing gradients. To overcome this Wei et al.  defined an objective at each layer, which acted to locally strengthen the gradients. However, a side effect of this multi-stage objective is that most of the effects of back-propagation happen locally and gradients back-propagated from other layers have little effect on the learning. This makes subtle interactions between layers less influential, and forces the learning process to concentrate on simply making accurate 2D predictions in each layer. We first give the results for computing the gradients of sparse predicted locations Y ˆ from Y (see section 5 of the main paper), before discussing the gradients induced on the confidence maps by these sparse locations.
In this chapter the subject of 3D reconstruction is treated. The 3D reconstruction reconstructs the 3D coordinates of an object that is seen by two cameras at different positions. In a situation without noise it is not difficult to reconstruct the coordinates of an object. But most of the times the image lines will not cross each other in the 3D space due to noise. So a more advanced method is needed to reconstruct an objects 3D coordinates in this case. There are various methods to reconstruct a 3D point from two noisy image points. The methods that are covered here are the rectification method, the linMMSE estimation (both suggested by ) and the optimal triangulation method.
As for Kazakhstan, we must take into account the Russian experience and move on our own directions. The Decree of the President of the Republic of Kazakhstan dated June 4, 2013 № 579, said: "We need to focus resources to meet the requirements of engineering and technical personnel with the relevant expertise of international level…” . For the proposed scheme, as a confirmatory factor can give an example of the Swiss Federal Institute of Technology Zurich (ETH Zürich).An investment of financial resources in technology of 3D-printinginstitute guide explains the brewing needs to improve educational programs of the engineering direction.The acquisition of a number of 3D printers will improve the level of teaching, including courses from CAD systems and engineering graphics. Thus, the European Institute of ETH Zurich, promotes rapprochement between educational institutions and industry, and focused on the European Employers who need specialists in the three-dimensional printing.
Abstract: In this work, three different algorithms such as Brute Force, Delaunay Triangulation and k-d Tree, are analyzed on matching comparison for 3D shape representation. It is intended for developing the pose tracking of moving objects in video surveillance. To determine 3Dpose of moving objects, some tracking system may require full 3Dposeestimation of arbitrarily shaped objects in real time. In order to perform 3Dposeestimation in real time, each step in the tracking algorithm must be computationally efficient. This paper presents method comparison for the computationally efficient registration of 3D shapes including free-form surfaces. Matching of free-form surfaces are carried out by using geometric point matching algorithm (ICP). Several aspects of the ICP algorithm are investigated and analyzed by using specified surface setup. The surface setup processed in this system is represented by simple geometric primitive dealing with objects of free-from shape. Considered representations are a cloud of points.
Perhaps the most prominent current latent variable mod- els are derived from the Gaussian Process Latent Variable Model [8, 23] and the Gaussian Process Dynamical Model . Such models can serve as effective priors for track- ing [23, 24] and can be learned with small training cor- pora . However, larger corpora are problematic since learning and inference are O(N 3 ) and O(N 2 ), where N is the number of training exemplars. While sparse approxima- tions to GPs exist , sparsification is not always straight- forward and effective. Recent additions to the GP family include the topologically-constrained GPLVM , Multi- factor GPLVM , and Hierarchical GPLVM . Such models permit stylistic diversity and multiple motions (un- like the GPLVM and GPDM), but to date these models have not been used for tracking, and complexity remains an issue. Most generative priors do not address the issue of ex- plicit inference over activity labels. While latent variable models can be constructed from data that contains multi- ple activities , knowledge about the activities and tran- sitions between them is typically only implicit in training data. As a result, training prior models to capture transi- tions, especially when they do not occur in training data, is challenging and often requires that one constrain the model explicitly (e.g. ). In  a coordinated mixture of fac- tor analyzers was used to facilitate model selection, but to our knowledge, this model has not been used for tracking multiple activities and transitions. Another way to handle transitions is to to build a discriminative classifier for activ- ities, and then use corresponding activity-specific priors to bootstrap the pose inference . The proposed imCRBM model bridges the gap between pose and activity inference within a single coherent and efficient generative framework.
To learn a more powerful latent pose space, we exploit additional motion capture data from the MPI-INF-3DHP dataset  for training the autoencoder. In Table 4, we report results with and without this additional data. We achieve better poseestimation accuracy when we train on a wider range of poses. As Human3.6m already includes a large variety of poses and the marker placements between the two datasets do not ex- actly match each other, we only observe a slight improve- ment. However, our results suggest that training an autoen- coder on a larger pose space without any dataset bias would result in an even more representative latent pose space and, eventually, a higher poseestimation accuracy. We further compare our autoencoder-based regression approach to a di- rect regression baseline. The relative contribution of the au- toencoder on very deep neural networks is smaller than that on a shallower network. However, we still increase the ac- curacy by applying our autoencoder training on top of the ResNet architecture.
Changing the reference object from specular (Ex- periment 1) to matte textured (Experiment 2) caused a signiﬁcant change in bias for specular and mixed materials for in-depth rotations. Compared with the matte reference, these objects appeared much less bumpy (downward shift of the line), although their discriminability remained the same across all condi- tions. How can these shifts be explained? We suggested above that parallax information may allow observers to construct 3D depth information more robustly than specular ﬂow. Seeing a matte reference object on every trial might thus affect the observer’s internal reference point for bumpiness, and consequently, any (not so robust and possibly nonrigid) specular object would be (down) scaled with respect to that reference point. This is in line with previous reports of scaling between matte and glossy static objects (Nefs, 2008; Todd, Norman, Koenderink, & Kappers, 1997). It is interesting, however, that we observed this ‘‘scaling’’ only for in- depth rotations, whereas no such shift occurs for rotations around the viewing axis. This indicates that the matte-textured object that is rotating in depth is perceived as the bumpiest and the easiest discriminable object in our set of stimuli. One possible explanation for this is that this condition is the only one in which both ﬁrst-order information (slant, conveyed through motion) and second-order information (curvature conveyed through texture compression) are available.
Human Action Recognition and Prediction are some of the hot topics in Computer Vision these days. It has its formidable contribution in the Anomaly detection. Many research scientists have been working in this field. Many new algorithms have been tried out in recent decades. In this paper, eight such approaches proposed in eight research papers have been reviewed. Compared to their counterparts for still images (the 2D CNNs for visual recognition), the 3D CNNs are considered to be comparatively less efficient, due to the limitations like high training complexity of spatio-temporal fusion and huge memory cost. So in the first referred paper the authors have proposed MiCT (Mixed Convolution Tube – for videos) with the right use of both 2D CNNs and 3D CNNs which reduces the training time. In the second research paper, the glimpse sequences in each frame correspond to interest points in the scene that are relevant to the classified activities. Unlike the last referred paper, the third referred paper presents a novel method to recognize human action as the evolution of poseestimation maps. The fourth referred paper presents a model for long term prediction of pedestrians from on-board observations. In the fifth research article referred, an attempt has been made to recognize the Human Rights Violation activities using the Deep Convolutional Neural Networks. In the sixth research article, Convolutional LSTM is used for the purpose of detecting violent videos. The seventh paper introduces a new Two-Stream Inﬂated 3D ConvNet (I3D) that is based on 2D ConvNet inﬂation. In the eighth research paper, a new temporal transition layer (TTL) that models variable temporal convolution kernel depths is embedded into 3D CNN to form T3D (Temporal 3D Convnets). Transferring knowledge from a pre-trained 2D CNN to a 3D CNN reduces the number of training samples required for 3D CNNs.
Palm vein recognition is a one of the most efficient biometric technologies, each individual can be identified through its veins unique characteristics, palm vein acquisition techniques is either contact based or contactless based, as the individual's hand contact or not the peg of the palm imaging device, the needs a contactless palm vein system in modern applications rise tow problems, the pose variations (rotation, scaling and translation transformations) since the imaging device cannot aligned correctly with the surface of the palm, and a delay of matching process especially for large systems, trying to solve these problems. This paper proposed a pose invariant identification system for contactless palm vein which include three main steps, at first data augmentation is done by making multiple copies of the input image then perform out-of- plane rotation on them around all the X,Y and Z axes. Then a new fast extract Region of Interest (ROI) algorithm is proposed for cropping palm region. Finally, features are extracted and classified by specific structure of Convolutional Neural Network (CNN). The system is tested on two public multispectral palm vein databases (PolyU and CASIA); furthermore, synthetic datasets are derived from these mentioned databases, to simulate the hand out-of-plane rotation in random angels within range from -20° to +20° degrees. To study several situations of pose invariant, twelve experiments are performed on all datasets, highest accuracy achieved is 99.73% ∓ 0.27 on PolyU datasets and 98 % ∓ 1 on CASIA datasets, with very fast identification process, about 0.01 second for identifying an individual, which proves system efficiency in contactless palm vein problems.
render the corresponding silhouettes of the 3D body model. The silhouette distance is measured in the vectorized GMM feature space. Comparison results using five walking images are included in this section. For each input silhouette, fif- teen poses were inferred using BME learned according to the method presented in Section 6.3. Given a pose, two vector- ized GMM descriptors were obtained using both the RVM- and model-based approaches. The root mean square errors (RMSEs) between the predicted and true image features were then computed. Figure 12(a) shows exemplar input silhou- ettes from view number 1 through view number 5, indexed starting from the leftmost figure. For each view and each method, given an input silhouette, we found the smallest RMSE among all of the 15 candidate poses provided by BME. We then compute the average of the smallest RMSE over all the input silhouettes. The average RMSEs of all the five views from both methods are shown in Figure 12(b). It can be seen that the average RMSEs are close for these two approaches, which indicates that the likelihoods of a good pose candidate computed using both methods are similar. Hence, we can use the example-based approach for computation eﬃciency. In addition, the example-based approach does not need a 3D body model of the subject, which also simplifies the prob- lem.
A. Perspective Projection The first work is pretreatment to adjust the gravity of 3D model to the coordinate’s original point, so the three directions with most information is adjusted respectively to the three main directions of 3 D coordinate system. At the same time, size normalization, coordinate proportion and rotating normalization are realized. The adjustment effect is shown in Figure1.a. After pretreatment, a tetrahedron is used to surround the 3D model to get the most information while not lose the information on the surface. We can get six images of the 3D model by perspective project from six perspective points that the 3D model crossed with the 3D coordinate. The 3D model is transformed from 3D space into six images in 2D space with size of M=N=64. The Six respective is K, L, M, K’, L’, M’ as shown in Figure1.b. The attribute f (a, b) of each point (a, b) of the image is the closest distance, as shown in Eq. (6),
3D human poseestimation outputs an estimate of joints’ 3D positions (or the angles) using 2D information, which is usually given in terms of either 2D images or joints’ 2D locations (2D pose). 3Dposeestimation methods based on 2D joints’ location assume that the 2D joints have been given or already extracted using some 2D poseestimation technique. They use various techniques such as sparse representation[6, 11], factorization , and neural networks to estimate the 3D poses from 2D poses. Zhou et al. in [6, 11] propose to use a convex approach while using sparse representation for the 3D human poseestimation from 2D landmarks. The method in  presents a 2D joints’ uncertainty map predictor to handle the cases when 2D joints’ information is not available. Wandt et al.  try to factorize 2D poses in camera parameters, base 3D human poses, and mixing coefficients. They also show that making periodic assumptions on the mixing coefficients can improve the performance in 3D human poseestimation. Tekin et al.  have introduced some method to fuse two different streams, one acting on 2D joints information and the other on the images to extract the 3Dpose information. Martinez et al.  discuss different natures of the error between the ground truth and estimated poses while having a 3Dpose estimator with a 2D pose estimator as an intermediate stage; that is, they compare the results while extracting the 3Dpose directly from the ground truth 2D poses and while regressing the 3Dpose from 2D poses extracted by some off-the-shelf 2D poseestimation techniques.
Some CPUs support additional 3D instructions that complement your nVIDIA Vanta and improve performance in 3D games or applications. This option allows you to disable support for these additional 3D instructions in the drivers. This can be useful for performance comparisons or for troubleshooting. Allows you to select between two monitor timing modes:
On the basis of the results of the dynamic CT scan, the optimal scan delays for 3D-CT arteriography and 3D-CT venography were determined (Fig 1). Because the time interval between the time-attenuation curves of the ICA and SS was 5– 6 seconds, we determined that the time for scanning for 3D-CT arteriography or 3D-CT venography was 5 seconds. The scan for 3D-CT arteriography was performed from 4 seconds before the peak time of time-attenuation curve of ICA to 1 second after the peak. After an interscan delay of 4 –5 seconds, the scanning for 3D-CT venography resumed. With the use of a dual-head power injector, a bolus injection of nonionic con- trast medium was delivered at a rate of 6 –7 mL/s (total 30 –35 mL) for 5 seconds followed by a chaser bolus of 18 –21 mL of saline at 6 –7 mL/s. The scan parameters were as follows: 135 kV; 260 mA; 16 sections of 0.5-mm section thickness (simulta- neous acquisition of 16 sections per one rotation), a helical pitch of 13; 0.5 seconds per rotation of the radiograph tube; a table speed of 9.0 mm/s; and 45 mm scan range. The scans for 3D-CT arteriography and 3D-CT venography were performed in a caudal-to-cephalic direction.
In PTAM, the initial map is reconstructed using the five- point algorithm . In the tracking, mapped points are projected onto an image to make 2D–3D correspondences using texture matching. From the correspondences, cam- era poses can be computed. In the mapping, 3D positions of new feature points are computed using triangulation at certain frames called keyframes. One of the significant contributions of PTAM is to introduce this keyframe- based mapping in vSLAM. An input frame is selected as a keyframe when a large disparity between an input frame and one of the keyframes is measured. A large disparity is basically required for accurate triangulation. In contrast to MonoSLAM, 3D points of feature points are optimized using lobal BA with some keyframes and global BA with all keyframes with the map. Also, in the tracking pro- cess, the newer vision of PTAM employ a relocalization algorithm . It uses a randomized tree-based feature classifier for searching the nearest keyframe of an input frame. In summary, PTAM is composed of the following four components.
Results: We presented a versatile, user-friendly, and efficient online tool for computer-aided drug design based on pharmacophore and 3D molecular similarity searching. The web interface enables binding sites detection, virtual screening hits identification, and drug targets prediction in an interactive manner through a seamless interface to all adapted packages (e.g., Cavity, PocketV.2, PharmMapper, SHAFTS). Several commercially available compound databases for hit identification and a well-annotated pharmacophore database for drug targets prediction were integrated in iDrug as well. The web interface provides tools for real-time molecular building/editing, converting, displaying, and analyzing. All the customized configurations of the functional modules can be accessed through featured session files provided, which can be saved to the local disk and uploaded to resume or update the history work. Conclusions: iDrug is easy to use, and provides a novel, fast and reliable tool for conducting drug design experiments. By using iDrug, various molecular design processing tasks can be submitted and visualized simply in one browser without installing locally any standalone modeling softwares. iDrug is accessible free of charge at http://lilab.ecust.edu.cn/idrug.