In contrast to supervised efforts, unsupervised methods have attracted more attention because they do not rely on ground truth. The first unsupervised deep CNN **model** used **stereo** image pairs which have a known camera baseline to train the network. 10 The authors explicitly generated an inverse warp of one image of a random **stereo** image pair, then they used the predicted depth map to reconstruct the other image, with the difference between the synthesized and input images used to replace the ground truth. A similar work was proposed by C. Godard et al. 11 Unlike the above methods, some researchers 12 have used monocular videos as input to achieve depth **estimation**, considering the pro- cessing speed for real-time inference, such as mounted on an embedded platform. To estimate the scene depth on an embedded platform, a real-time monocular **model** 13 for depth **estimation** was proposed.

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Depth **estimation** from monocular video plays a crucial role in scene perception. The signiﬁcant draw- back of supervised learning models is the need for vast amounts of manually labeled data (ground truth) for training. To overcome this limitation, unsupervised learning strategies without the requirement for ground truth have achieved extensive attention from researchers in the past few years. This paper presents a novel unsupervised framework for estimating single-view depth and predicting camera mo- tion jointly. **Stereo** image sequences are used to train the **model** while monocular images are required for inference. The presented framework is composed of two CNNs (depth CNN and pose CNN) which are trained concurrently and tested independently. The objective function is constructed on the basis of the epipolar geometry constraints between **stereo** image sequences. To improve the accuracy of the **model**, a left-right consistency loss is added to the objective function. The use of **stereo** image sequences enables us to utilize both spatial information between **stereo** images and temporal photometric warp error from image sequences. Experimental results on the KITTI and Cityscapes datasets show that our **model** not only outperforms prior unsupervised approaches but also achieving better results comparable with sev- eral supervised methods. Moreover, we also train our **model** on the Euroc dataset which is captured in an indoor environment. Experiments in indoor and outdoor scenes are conducted to test the generalization capability of the **model**.

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as the dynamic change of the camera itself. Thus, object detection from a stationary camera is simpler in that it involves fewer **estimation** procedures. Initial approaches in this field involve spatial, temporal and spatio-temporal analysis of video sequences. Using a sequence of images the detection principle is based essentially on the fact that the objects to be searched for are in **motion**. These methods prioritize temporal characteristics compared with spatial characteristics, i.e. the detection deals mainly with the analysis of variations in time of one and the same pixel rather than with the information given by the environment of a pixel in one image [15]. More advanced and effective approaches consider object modeling and tracking using state-space **estimation** procedures for matching the **model** to the observations and for estimating the next state of the object. The most common techniques, i.e. analysis of the optical flow field and processing of **stereo** images, involve processing two or more images. With optical-flow-field analysis, multiple images are acquired at different times [16]; **stereo** images, of course, are acquired simultaneously from different points of view [17]. Optical-flow-based techniques detect obstacles indirectly by analyzing the velocity field. **Stereo** image techniques identify the correspondences between pixels in the different images. Stereovision has advantages in that it can detect obstacles directly and, unlike optical-flow-field analysis, is not constrained by speed. Several approaches considering different aspects of object and **motion** perception from a stationery camera are considered.

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The **stereo** camera that we will simulate in our experiments is the STH-MDCS2 from Videre Design. To process the information from this **stereo** camera, we have to fill in the fixed parameters in our camera **model**, or in other words, calibrate the camera. These include the intrinsic parameters like focal length and image plane origin and the extrinsic parameters that describe the relative orientation of the two cameras to each other. Besides that, there can be some lens distortion especially with wide angle lenses. We have a rough idea of what the individual parameters should be according to the manufacturer, but due to small variations in the fabrication process the actual values of the parameters can deviate from the given parameters. To verify the given parameters we will use a calibration algorithm on the camera. In this case we will calibrate the camera with the equipped calibration program. This program calculates optimal parameters by solving a nonlinear set of equations, which is determined by at least 5 views of a calibration target observed by both cameras. The calibration target is a checkerboard with known dimensions. Most of the information about the unknown parameters is revealed when viewing the calibration target at different angles, varying the distance from the calibration target to the camera will give no extra information about these parameters.

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The quadtree spline provides a convenient way to use adaptively-sized patches for **motion** estima- tion, while maintaining inter-patch continuity. The question remains how to actually determine the topology of the patches, i.e., which patches get subdivided and which ones remain large. Ideally, we would like each patch to cover a region of the image within which the parametric **motion** **model** is valid. In a real-world situation, this may correspond to planar surface patches undergoing rigid **motion** with a small amount of perspective distortion (bilinear flow is then very close to projective flow). However, usually we are not a priori given the required segmentation of the image. Instead, we must deduce such a segmentation based on the adequacy of the flow **model** within each patch. The fundamental tool we will use here is the concept of residual flow [Irani et al., 1992], recently used by M¨uller et al. [1994] to subdivide affine **motion** patches (which they call tiles). The residual flow is the per-pixel estimate of flow required to register the two images in addition to the flow currently being modeled by the parametric **motion** **model**. At a single pixel, only the normal flow can be estimated,

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There are a number of obstacles that a **stereo** algorithm must overcome in order to produce meaningful disparity values; some of these obstacles are discussed in Section 2.2. For our application, the key and oset images will typically be taken from widely varying viewpoints relative to the depth of objects in the scene, which is a particularly dicult case for current **stereo** approaches for several reasons. First, the disparities between the key and oset images can be large which means that the **stereo** algorithm must investigate a large number of potential matches for each pixel in the key image. Second, corresponding regions in the two images will typically be viewed from dierent directions and thus exhibit varying degrees of foreshortening. This poses a signicant diculty for simple window- based correlation schemes since corresponding image neighborhoods will be scaled dierently. Third, the two images may have very dierent patterns of occlusion | the entire side of a building may be visible in one image but not the other. Again, such a signicant dierence between the images is dicult for a traditional **stereo** algorithm to handle.

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Abstract: In accordance with problems such as difficulty in obtaining aerodynamic parameters of a quad-rotor **model**, the change of **model** parameters with external interference affects the control performances, an aerodynamic parameter **estimation** method and an adaptive attitude control method based on LADRC are designed. Firstly, the **motion** **model**, dynamics **model** and control distribution **model** of quad-rotor are established by using the aerodynamic and Newtonian Euler equations. Secondly, the identification tool CIFER is used to identify the aerodynamic parameters with large uncertainties in frequency domain and a more accurate attitude **model** of the quad-rotor is obtained. Then an adaptive attitude decoupling controller based on LADRC is designed to solve the problem of poor anti-interference ability of the quad-rotor, so that the control parameter b 0 can

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To test different **motion** **estimation** algorithms in the spa- tial domain, they were applied in video compression al- gorithm, as demonstrated in Figure 3. This figure illus- trates the utilizing process of **motion** **estimation** algo- rithm in compression. As can be seen in Figure 3, the difference between the current frame and the previous one, which was compensated with one of these previous, mentioned methods, will be send to the encoder. As a result of using the difference between two sequences, we managed to send the minimum amount of information to the decoder. Then the residual image will be encoded and decoded by the SPIHT method, which is based on wave- let transformation. While the **motion** **estimation** process is being done, the **motion** vectors obtained by these three methods are send to the decoder. In the decoder section, the previous frame which was completely send to the decoder produced the restored image by means of **motion** vectors. This image will be added to the encoders resid- ual image and in this way the main image will be pro- duced. This process is performed for all of the images in a image sequence and all images are restored by this method.

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Image data in an image sequence remains mostly the same between frames along **motion** trajectories. That is the same as saying that the scene content does not change much from frame to frame. For the purpose to exploit the image data redundancy in image sequences there is a need to estimate **motion** in the image sequence so one can process along **motion** trajectories. The main purpose of **motion** **estimation** techniques is to recover this information by analyzing the image content. Efficient and accurate **motion** **estimation** is an essential component in the domains of image sequence analysis, computer vision and video communication. It may worth mentioning that although **motion** **estimation** is also used in many other disciplines such as computer vision, target tracking, and industrial monitoring, the techniques developed particularly for image coding are different in some respects. The goal of image compression is to reduce the total transmission bit rate for reconstructing images at the receiver

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Adequate to the 2D accuracy assessment, 3D mapping accuracy was analyzed for the Innsbruck **stereo** pair with respect to utilization of a different number of GCPs and ICPs as well as with respect to the comparison of shift versus nominator coefficients optimization. The results of this analysis, including mean and standard deviation values of checkpoint residuals, are summarized in Table 6. Again, the utilization of a minimum number of GCPs yields systematic geo-location errors in East, North, and height as manifested through the corresponding mean residual values. Over-determination as exemplarily given, e.g., by utilizing 10 GCPs, reduces these systematic errors to a more or less negligible order of magnitude and yields distinctly improved 3D RMS accuracy values, widely adequate for both optimization scenarios.

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In computer vision, human vision processing is an important aspect, in which a **model** of human eye is created. In such systems, receptive field position disparity **estimation** is very essential because it can be used for depth perception and binocular fusion. The ultimate goal of this paper is to find a method for accurate disparity **estimation** and binocular fusion.

step search algorithm(3SS)[1], new three-step search algorithm(N3SS)[2], diamond search algorithm(DS)[3], hexagon-based search algorithm (HEXBS)[4], and Unsymmetrical-cross Multi-Hexagon-grid Search (UMHexagonS) [5][6] have been proposed. These algorithms use pre defined search patterns to reduce the search points in the search window. If the actual **motion** is not matched with the search pattern then the both speed and quality will decrease. Moreover these algorithms are easily trapped to local minimum and hence sub optimal **motion** vectors are obtained. For the optimal solution with reduced computations, Successive elimination algorithm [7] or modified versions based on SEA [8, 9] are preferred. These algorithms provide the optimal solution as that of full search, but with less operation number by eliminating highly impossible candidate blocks as early as possible to reduce the computational cost. In this paper, we propose both optimal and sub optimal solutions for block **motion** **estimation** based on best initial matching error predictive method. The proposed new fast full search **motion** **estimation** provides optimal solution by utilizing Fast Computing Method (FCM) and Best Initial Matching Error Predictive Methods (BIMEPM). The new fast full search **motion** **estimation** is slightly modified to reduce the computational load further but relaxes optimality. The rest of this paper is organized as follows. Some well-known fast full-search algorithms are reviewed In Section 2. Then, we present the new fast full search **motion** **estimation** algorithm and its modified versions which utilize FCM and BIMEPM methods in section 3. In Section 4, the simulation results for the proposed and the conventional methods are compared to verify the performance of the proposed algorithms. Finally, conclusions are given in Section 5.

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Abstract: Genetic Algorithms are a form of evolutionary computing algorithm which are inspired by Darwin’s theory of evolution and simulate mathematically the evolution of living organisms. Genetic algorithm have a powerful global searching ability, they are quite suitable for dealing with multi-modal optimization problems such as the **motion** **estimation**. **Motion** **estimation** is the key step for image registration. It is the process of quantifying movements in successive images. One of the key elements of video compression schemes is **motion** **estimation**. For a video, sequence consists of a series of frames, to achieve compression; the temporal redundancy between adjacent frames can be exploited. That is, a frame is selected as a reference, and subsequent frames are predicted from the reference using a technique known as **motion** **estimation**. Genetic algorithm is applied to solve rather difficult problems by using simple coding technique and genetic system.

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ABSTRACT: It is evident that the accuracy of **stereo** matching algorithms has continued to increase, based on quantitative evaluations of the resulting disparity maps. Today a number of **stereo** matching algorithms are available to compute disparity maps. These algorithms are mainly classified as Local and Global algorithms. This paper focuses on designing a system for the **estimation** of disparity map using a simulation tool with the help of Local **stereo** matching algorithm. Here the designed system first extract corner feature from input side **stereo** image pair, then a fundamental matrix is calculated to get an epi polar geometry of a **stereo** image pair. Using epipoalar geometry, SSD and sub pixel accuracy distance between best similar points is calculated. Finally using this distances Disparity map is estimated. The system gives good disparity results within lesser time.

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The importance of image sequence processing is constantly growing with the ever increasing use of television and video systems in consumer, commercial, medical, and scientific applications. Image sequences can be acquired by film- based **motion** picture cameras or electronic video cameras. In either case, there are several factors related to imaging sensor limitations that contribute to the graininess (noise) of resulting images. Electronic sensor noise and film grain are among these factors [1]. In many cases, graininess may result in visually disturbing degradation of the image quality, or it may mask important image information. Even if the noise may not be perceived at full-speed video due to the temporal masking e ﬀ ect of the eye, it often leads to unacceptable single-frame hardcopies and to poor-quality freeze-frames that adversely a ﬀ ect the performance of subsequent image analysis [2].

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To estimate MVC, The **motion** vectors of the neighboring are examined and find a proper a group out of five groups given in Fig.2. Then the corresponding shaded block **motion** vectors are averaged and down-scaled to obtain an estimate of MVC, which is the third candidate beside the other candidates that were driven using the previous algorithm. In the case of group E where no **motion** similar **motion** vectors exist, (0,0) is selected as the estimate. Here, e1=ll MV1-MV2 ll, e2= ll MV2-MV3 ll, and e3= ll MV3-MV1 ll, and D is threshold value to examine similarity between the two MVs. A value of 8 for D has been used in our simulations based on this paper.

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, equation (11.1), while equation (11.2) states the causality property, equation (11.3) specifies an approximation of the original signal at coarser resolution, more over all details of the signal is lost when the resolution goes to zero as shown in equation (11.4), and inversely (11.5) the original is recovered as resolution tends to infinity. In the wavelet based SR process, the LR input constitutes the approximation, while the difference is predicted. Wavelet multi-resolution analysis provides a tool for **estimation** of the relative similarity of the differences across scales, useful in predicting the next higher set of unavailable differences for which the input signal is the approximation. The wavelet decomposition of signals into approximation and difference components, results in output wavelets co-efficients with same sample size as the original signal for each component. For two dimensional signals like images, the wavelet transform co-efficients will be four times the original image size, however for perfect reconstruction of the original input, only half of the wavelet co-efficients (in both directions) is required as this can be accomplished using either the set of even or odd co-efficients, this leads to the subsampling of the wavelet co-efficients used in discrete wavelet transform (DWT), as the original output is considered redundant or over-complete for reconstruction. However the output sub- sampled version (DWT), is altogether complete for perfect reconstruction but individually incomplete, as each component suffers from shift variance, due to subsampling, more over within the context of multi- resolution analysis, iterative subsampling greatly reduces the usefulness of co-efficients for inter-scale frequency analysis as the increasing reduction of samples sizes results in increasingly low frequency resolutions completely unsuitable for such analysis. This leads to the adoption of redundant discrete wavelet transform for inter-scale frequency analysis.

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In this paper, we use SURF matching algorithm for collecting interest points from each image because it gives better matching results. The fundamental matrix is obtained by the five pre-mentioned methods and we compare between them from point of view of accuracy of depth **estimation**. The depth estimated is then calculated by getting the median between maximum and minimum disparity resulting from the inlier points in the left image and in the right image. We use the surveyor robot SVS [18], in Fig. 1, in this research paper to get the **stereo** images from different distances from an object. This paper is organized as follows: section II presents the **stereo** vision. Methodology is illustrated in section III. proposed algorithm is shown in section IV. Experimental results are presented in section V. Finally section VI concludes this paper.

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A non-contact system generally consists of two distinct components; tracking and pose **estimation**. Tracking refers to the process of computing correct identification of the subject and possibly limbs between successive frames. Tracking algorithms first require the correct segmentation of the subject from the scene, and can be classified as high or low level tracking [15]. An example of low level tracking is that of edges, in comparison to high level tracking which could be of the head and feet for example. After tracking has been performed the process of estimating the pose and aligning it with the subject’s body can be performed. Systems developed thus far have problems dealing with self-occlusion, where one part of the body obstructs another part with regards to the camera’s view angle. Furthermore, non-contact systems, whilst reasonably accurate, are still not as accurate as commercially available contact marker based tracking systems.

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Background: Restoration of upper limb movements in subjects recovering from stroke is an essential keystone in rehabilitative practices. Rehabilitation of arm movements, in fact, is usually a far more difficult one as compared to that of lower extremities. For these reasons, researchers are developing new methods and technologies so that the rehabilitative process could be more accurate, rapid and easily accepted by the patient. This paper introduces the proof of concept for a new non-invasive FES-assisted rehabilitation system for the upper limb, called smartFES (sFES), where the electrical stimulation is controlled by a biologically inspired neural inverse dynamics **model**, fed by the kinematic information associated with the execution of a planar goal-oriented movement. More specifically, this work details two steps of the proposed system: an ad hoc markerless **motion** analysis algorithm for the **estimation** of kinematics, and a neural controller that drives a synthetic arm. The vision of the entire system is to acquire kinematics from the analysis of video sequences during planar arm movements and to use it together with a neural inverse dynamics **model** able to provide the patient with the electrical stimulation patterns needed to perform the movement with the assisted limb.

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