Zoom Motion Research Articles

Applying Functional Animation to Pictorial Symbols for Supporting P300–Brain–Computer Interface Access to Augmentative and Alternative Communication Devices by Children

Motion techniques, such as highlighting communication items via flip and/or zoom motion, may support BCI-AAC success and comfort for those using P300 brain–computer interface techniques for augmentative and alternative communication device access (BCI-AAC). However, the utilization of motion in P300-BCI-AAC for children is largely unexplored. Therefore, the aims of this investigation are to provide initial insights into the impacts of functional pictorial symbol animation on (a) event-related potentials underlying P300-BCI-AAC performance and (b) children’s user experience. Participants completed two conditions: (1) a static item condition, and (2) a functional animation condition. Following completion, participants completed Likert scale ratings of factors like visual appeal. Animation significantly increased parieto-occipital P200/P300 amplitudes and delayed parieto-occipital N200, P200/P300, and N400 latencies. Further, animation raised ratings of visual appeal to a level approaching significance. Static highlighting significantly increased N400 amplitudes. Findings provide early insights into neural activity underlying P300-BCI-AAC highlighting of pictorial symbols via animation in children and justify further evaluation.

A fast technique to detect copy-move image forgery with reflection and non-affine transformation attacks

Copy-move forgery is one of the most frequently utilized image tampering technique which uses the segment of the same image to produce manipulated image by duplicating or concealing image regions. To remove suspicious traces of forgery, various attacks are applied over the tampered image which make forgery detection process too complicated. We propose a forgery detection technique in which Center Surround Extrema (CenSurE) detector is applied for keypoint detection from images. To compute keypoint descriptors, Local Image Permutation Interval Descriptor (LIPID) is used. Keypoint matching is performed using k-Nearest Neighbor (k-NN) technique with utilization of k-d tree and Best-Bin-First (BBF) search method. Grouping over keypoints is performed using Fuzzy C-Means (FCM) clustering. We apply Random Sample Consensus (RANSAC) algorithm to remove outliers obtained during forgery detection process. Experimental results show that proposed technique can effectively detect forged images containing reflection and non-affine transformation with geometrical attacks. In addition, proposed approach also shows robustness against erosion, dilation, RGB color addition, zoom motion blur, JPEG compression, spread noise addition, and multiple copy-move attacks. Proposed scheme consumes least time in forgery detection as compared to state-of-the-art methods.

Effects of fatigue on steady state motion visual evoked potentials: Optimised stimulus parameters for a zoom motion-based brain-computer interface

Background and ObjectiveIn flicker-based steady-state visual evoked potentials (SSVEP) brain-computer interface (BCI), the system performance decreases due to prolonged repeated visual stimulation. To reduce the performance decrease due to visual fatigue, the zoom motion based steady-state motion visual evoked potentials (SSMVEPs) paradigm had been proposed. In this study, the stimulation parameters of the paradigm are optimised to mitigate the decrease in detection accuracy for SSMVEP due to visual fatigue. MethodsEight zoom motion-based SSMVEP paradigms with different stimulation parameters were compared. The graph size, luminance, colour, and shape, as well as the frequency range and interval of the stimulation and refresh rate of the screen was changed to determine the optimal paradigm with high recognition accuracy and reduced fatigue effects. The signal-to-noise ratio (SNR) of SSMVEP was also calculated for four fatigue levels. Moreover, the power spectral density of electroencephalograph (EEG) alpha and theta bands during ongoing activity was calculated for the stimulation experiment to evaluate fatigue at the start and end of the stimulation task. ResultsAll stimulation SSMVEP paradigms exhibited high accuracies. Changes in luminance, colour, and shape did not impact the recognition accuracy, nor did a higher stimulation frequency or lower frequency interval of each stimulation block. However, the paradigm with smaller stimulus achieved the highest average target selection accuracy of 97.2%, compared to 94.9% for the standard paradigm. Furthermore, it exhibited almost zero reduction in recognition accuracy due to fatigue. From fatigue level 1 to level 4, the smaller zoom motion-based SSMVEP exhibited a lower decrease in the SNR of SSMVEP and a lower alpha/theta ratio decrease during ongoing stimulation activity compared to the standard paradigm. ConclusionsFor a zoom motion-based SSMVEP paradigm, changing multiple stimulation parameters can lead to the same high performance as the standard paradigm. Moreover, using a smaller stimulus can reduce the accuracy decrease caused by fatigue because the SNR decrease in the evoked SSMVEP state was negligible and the alpha/theta index decrease during ongoing activity was lower than that for the standard paradigm.

Zoom motion estimation for color and depth videos using depth information

In this paper, two methods of zoom motion estimation for color and depth videos by using depth information are proposed. Color and depth videos are independently estimated for zoom motion. Zoom for color video is scaled by spatial domain, and depth video is scaled by both spatial and depth domains. For color video, instead of existing methods of zoom motion estimation that apply all of possible zoom ratios for a current block, the zoom ratio of the proposed method is determined as the ratio of the average depth values of the current and reference blocks. Then, the reference block is resized by multiplying the zoom ratio and the reference block is mapped to the current block. For depth video, the reference block is first scaled in the spatial direction by the same methodology used for color video and then scaled by a distance ratio from a camera to the objects. Compared to the conventional motion estimation method, the proposed method reduces MSE by up to about 30% for the color video and up to about 85% for the depth video.

A Radial Zoom Motion-Based Paradigm for Steady State Motion Visual Evoked Potentials.

Background: In steady state visual evoked potential (SSVEP)-based brain-computer interfaces, prolonged repeated flicker stimulation would reduce the system performance. To reduce the visual discomfort and fatigue, while ensuring recognition accuracy, and information transmission rate (ITR), a novel motion paradigm based on the steady-state motion visual evoked potentials (SSMVEPs) is proposed.Methods: The novel SSMVEP paradigm of the radial zoom motion was realized using the sinusoidal form to modulate the size of the stimuli. The radial zoom motion-based SSMVEP paradigm was compared with the flicker-based SSVEP paradigm and the SSMVEP paradigm based on Newton's ring motion. The canonical correlation analysis was used to identify the frequency of the eight targets, the recognition accuracy of different paradigms with different stimulation frequencies, and the ITR under different stimulation durations were calculated. The subjective comfort scores and fatigue scores, and decrease in the accuracy due to fatigue was evaluated.Results: The average recognition accuracy of the novel radial zoom motion-based SSMVEP paradigm was 93.4%, and its ITR reached 42.5 bit/min, which was greater than the average recognition accuracy of the SSMVEP paradigm based on Newton's ring motion. The comfort score of the novel paradigm was greater than both the flicker-based SSVEP paradigm and SSMVEP paradigm based on Newton's ring motion. The decrease in the recognition accuracy due to fatigue was less than that of the SSSMVEP paradigm based on Newton's ring motion.Conclusion: The SSMVEP paradigm based on radial zoom motion has high recognition accuracy and ITR with low visual discomfort and fatigue scores. The method has potential advantages in overcoming the performance decline caused by fatigue.

Compressed domain zoom motion classification using local tetra patterns

In this paper, a novel compressed domain method for classifying zooming motion is presented. Camera zoom motion classification is an important problem in video analysis wherein the task is to recognize and separate zooming-in camera from zooming-out camera. In our study, we address this problem utilizing local tetra patterns which has earlier found applications in image texture analysis and content-based image retrieval. Towards this goal we model the motion vector orientation and magnitude using local tetra patterns followed by histogram formation. Since the feature dimension is large, uniform pattern-based feature reduction is applied on the histograms to form the feature vector which is fed to the C-SVM classifier for training/testing purposes. Experimental testing utilizing standard video sequences with block motion vectors coming from exhaustive search motion estimation algorithm as well as H.264 obtained block motion vectors along with comparative analysis carried out with existing techniques shows superior performance for the proposed method.

An Efficient Four-Parameter Affine Motion Model for Video Coding

In this paper, we study a simplified affine motion model-based coding framework to overcome the limitation of a translational motion model and maintain low-computational complexity. The proposed framework mainly has three key contributions. First, we propose to reduce the number of affine motion parameters from 6 to 4. The proposed four-parameter affine motion model can not only handle most of the complex motions in natural videos, but also save the bits for two parameters. Second, to efficiently encode the affine motion parameters, we propose two motion prediction modes, i.e., an advanced affine motion vector prediction scheme combined with a gradient-based fast affine motion estimation algorithm and an affine model merge scheme, where the latter attempts to reuse the affine motion parameters (instead of the motion vectors) of neighboring blocks. Third, we propose two fast affine motion compensation algorithms. One is the one-step sub-pixel interpolation that reduces the computations of each interpolation. The other is the interpolation-precision-based adaptive block size motion compensation that performs motion compensation at the block level rather than the pixel level to reduce the number of interpolation. Our proposed techniques have been implemented based on the state-of-the-art high-efficiency video coding standard, and the experimental results show that the proposed techniques altogether achieve, on average, 11.1% and 19.3% bits saving for random access and low-delay configurations, respectively, on typical video sequences that have rich rotation or zooming motions. Meanwhile, the computational complexity increases of both the encoder and the decoder are within an acceptable range.

Merge Mode for Deformable Block Motion Information Derivation

Motion estimation (ME) plays an important role in most video coding standards since it can improve the coding performance significantly. Block matching ME (BMME) based on translation motion model is employed in both H.264/Advanced Video Coding and High Efficiency Video Coding (HEVC), which assumes that all the pixels in a block undergo the same motion. However, BMME cannot represent the complex motion accurately, such as rotation, zoom, and deformation motion. To resolve the problem, a merge mode for deformable block motion information derivation is proposed, which considers bilinear interpolation model, six-parameter affine model, and four-parameter affine model simultaneously. The sum of absolute transformed differences criterion is used to select the best model for a block. In the proposed merge mode, the motion information of the current block is derived from the motion information of its neighbors. It does not need to perform ME and transmit motion parameters. The proposed merge mode is integrated into HEVC test model (HM) 14.0. The simulation results show that compared with HM14.0, the proposed merge mode can bring 1.3%, 1.7%, and 2.7% Bjontegaard delta (BD) bitrate savings on average for test sequences of HEVC, and 11.0%, 15.2%, and 17.4% BD bitrate savings for deformation sequences in random access main, low-delay B main, and low-delay P main test conditions, respectively, while the increased encoding and decoding complexities are moderate.

Memorias de la bomba atómica en su cuna, Nuevo México

On July 16, 1945 the « Land of Enchantment » acquired a new identity as the cradle of the nuclear age when the world’s first atomic bomb exploded in the Jornada del Muerto desert. New Mexico underwent a phenomenal transformation as a result of the arrival of atomic science in its remotest lands. The Trinity date does not hold the same place in collective memory as Hiroshima and Nagasaki but its historical significance takes various shapes on different memory scales. Therefore, this article addresses the meaningfulness of the bomb in national and local memories, focusing on the voices which contribute to the expression of its legacy and on the way this legacy is staged. A zooming motion from collective to individual perspectives emphasizes the authority of place on the construction of memory, and shows how the story of nuclear New Mexico exemplifies the interaction of world and local histories when most isolated populations and places get affected by incidents of an international range.

An Aerial Video Stabilization Method Based on SURF Feature

The video captured by Micro Aerial Vehicle is often degraded due to unexpected random trembling and jitter caused by wind and the shake of the aerial platform. An approach for stabilizing the aerial video based on SURF feature and Kalman filter is proposed. SURF feature points are extracted in each frame, and the feature points between adjacent frames are matched using Fast Library for Approximate Nearest Neighbors search method. Then Random Sampling Consensus matching algorithm and Least Squares Method are used to remove mismatching points pairs, and estimate the transformation between the adjacent images. Finally, Kalman filter is applied to smooth the motion parameters and separate Intentional Motion from Unwanted Motion to stabilize the aerial video. Experiments results show that the approach can stabilize aerial video efficiently with high accuracy, and it is robust to the translation, rotation and zooming motion of camera.

가변 블록크기의 신축 움직임 추정 방법

!./01234%&'())56.7489:; ?, #$%&'()@ABC.DE;<. F@ GA !H#$%&'I()1A* I JB<. JK* LMNOPQHRSTMN)UHR34VWXYZI[\1E, VW@]G^MN)U.N01_#$I/01`, YZL#$I/01aA<. bB, VW3c@A%&'())56XdefA%&'g.8@Eh1_ij/kH !lm noK<. lpqIrskH JK* %&'())56X%&'g:.t)1uk`, v#$%&'()* @w1_xB%&'())56.y1 2G%&'g: z{I5|1u<.AbstractIt is possible to improve the accuracy of the motion estimation for a video by applying a variable block size. However, it has limits in the zoom motion estimation. In this paper, we propose a method for estimating the zoom motion with variable block size. The proposed method separates the background within the object picture by depth information obtained from a depth camera, and only the object regions are applied to zoom scale, but the background is not applied. In addition, the object regions select efficiently variable block size mode in consideration of the generated motion vectors and the accuracy of motion estimation at the same time. Simulation results show the accuracy of the motion estimation and the number of motion vectors for the proposed method. It is verified that the proposed method can reduce the number of motion while maintaining the similar accuracy of motion estimation than the conventional motion estimation methods. Keyword : Zoom Motion, Variable Block-size, Depth Information, Motion Estimation

Joint Image Registration and Super-Resolution From Low-Resolution Images With Zooming Motion

This paper proposes a new framework for joint image registration and high-resolution (HR) image reconstruction from multiple low-resolution (LR) observations with zooming motion. Conventional super-resolution (SR) methods typically formulate the SR problem as a two-stage process, namely, image registration followed by HR reconstruction. An important step in image SR is the effective estimation of motion parameters. However, the registration algorithms in these two-stage processes experience various degrees of errors. This could degrade the quality of subsequent HR reconstruction. In view of this, this paper presents a new approach that performs joint image registration and SR reconstruction. The proposed iterative SR framework enables the HR image and motion parameters to be estimated simultaneously and progressively. This could increase the potential SR improvement as more accurate estimates of motion parameters could be obtained iteratively. Experimental results show that the proposed method is effective in performing image registration and SR for simulated and real-life images and videos.

깊이 정보를 이용한 줌 움직임 추정 방법

Zoom motion estimation of video sequence is very complicated for implementation. In this paper, we propose a method to implement the zoom motion estimation using together the depth camera and color camera. Depth camera obtains the distance information between current block and reference block, then zoom ratio between both blocks is calculated from this distance information. As the reference block is appropriately zoomed by the zoom ratio, the motion estimated difference signal can be reduced. Therefore, the proposed method is possible to increase the accuracy of motion estimation with keeping zoom motion estimation complexity not greater. Simulation was to measure the motion estimation accuracy of the proposed method, we can see the motion estimation error was decreased significantly compared to conventional block matching method.

Zoom Motion Estimation Using Block-Based Fast Local Area Scaling

Block-based motion estimation (ME) has been widely used in various video coding standards due to its effectiveness in removing temporal redundancy. However, this traditional ME technique suffers the limitation that it can only compensate for a pure parallel translation between frames. Various algorithms have been proposed for estimating other motion models, such as zooming, rotating, tilting, and warping. In spite of good performance for these algorithms, they are not used in the current industrial world because of their high computational complexity and incompatibility with current block-based video coding standards that limit their practical usage. In this paper, we present a novel zoom motion estimation (ZME) and motion compensation algorithm using the local area scaling technique. To represent and encode the zoom motion, we introduce a zoom vector (ZV) that either increases or decreases a reference block size. The resized reference block is then interpolated to the size of the current block in order to perform block matching. For fast ZME, we have also designed a 3-D diamond pattern search to reduce the number of unnecessary search points for the ZV. We have integrated the proposed algorithm into H.264/AVC JM reference software. Experimental results show that an average bit-rate savings of 11.01% (up to 16.87%) can be achieved for high-definition size sequences with an average 0.20 dB gain in peak signal-to-noise ratio and a less than 10% time increment in the hierarchical B structure.

Affine Model Based Motion Compensation Prediction for Zoom

Zoom motion is classified into two categories, i.e., global zoom motion and local zoom motion. A simple affine motion model with four parameters is utilized to describe zoom motion efficiently based on the analyses of camera imaging principles. Based on the motion model, a basic candidate motion vector (BCMV) of a block could be derived when model parameters are confirmed. Then a set of candidate motion vectors (CMVs) could be obtained by modifying the BCMV. Thereafter, template matching is used to choose the optimal CMV (OCMV). Finally, the block is coded with the optimal CMV as an independent mode, and a rate distortion (RD) criterion is used to determine whether to use the mode or not. Experimental results demonstrate that by implementing the proposed method into Key Technology Area test platform version 2.6r1 (KTA2.6r1), a maximum -21.99% and average -8.72% bit rate savings can be achieved for videos involving zoom motion, while maintaining the same quality (evaluated by PSNR) of reconstructed videos when IPPPP coding structure is used. When Hierarchical B coding structure is employed, the maximum and average bit rate savings are - 10.48% and - 5.575% when the qualities of reconstructed videos remain unchanged. Besides, for videos involving camera rotation, translation, etc., an average -2.04% bit rate savings could also be achieved; while for videos containing common motions, an average -1.19% bit rate saving could be achieved at the same quality of reconstructed videos.

Content-Based Keyframe Clustering Using Near Duplicate Keyframe Identification

In this paper, the authors propose an effective content-based clustering method for keyframes of news video stories using the Near Duplicate Keyframe (NDK) identification concept. Initially, the authors investigate the near-duplicate relationship, as a content-based visual similarity across keyframes, through the Near-Duplicate Keyframe (NDK) identification algorithm presented. The authors assign a near-duplicate score to each pair of keyframes within the story. Using an efficient keypoint matching technique followed by matching pattern analysis, this NDK identification algorithm can handle extreme zooming and significant object motion. In the second step, the weighted adjacency matrix is determined for each story based on assigned near duplicate score. The authors then use the spectral clustering scheme to remove outlier keyframes and partition remainders. Two sets of experiments are carried out to evaluate the NDK identification method and assess the proposed keyframe clustering method performance.

Subsampled Block-Matching for Zoom Motion Compensated Prediction

Motion compensated prediction plays a vital role in achieving enormous video compression efficiency in advanced video coding standards. Most practical motion compensated prediction techniques implicitly assume pure translational motions in the video contents for effective operation. Some attempts aiming at more general motion models are usually too complex requiring parameter estimation in practical implementation. In this paper, zoom motion compensation is investigated to extend the assumed model to support both zoom and translation motions. To accomplish practical complexity, a novel and efficient subsampled block-matching zoom motion estimation technique is proposed which makes use of the interpolated reference frames for subpixel motion estimation in a conventional hybrid video coding structure. Specially designed subsampling patterns in block matching are used to realize the translation and zoom motion estimation and compensation. No zoom parameters estimation and additional frame buffers are required in the encoder implementation. The complexity of the decoder is similar to the conventional hybrid video codec that supports subpixel motion compensation. The overall increase in memory requirement and computational complexity is moderate. Experimental results show that the new technique can achieve up to 9.89% bitrate reduction using KTA2.2r1 reference software implementation.

Model Based Motion Vector Predictor for Zoom Motion

As zoom motion is common in video applications, a linear motion model is derived to describe zoom motion based on the analyses of camera imaging principles. Based on the motion model, a motion vector predictor for videos involving zoom motion is proposed. A rate distortion (RD) criterion is used to choose the optimal motion vector predictor between the one utilized in H.264/AVC and the one derived from the linear motion model. Experimental results demonstrate that by implementing the proposed method into Key Technology Area test platform version 2.2r1(KTA2.2r1), the maximum and average bit rate savings can be achieved as high as 7.66% and 4.90% respectively, while maintaining the same quality of reconstructed videos.

Slow Motion and Zoom in HD Digital Videos Using Fractals

Slow motion replay and spatial zooming are special effects used in digital video rendering. At present, most techniques to perform digital spatial zoom and slow motion are based on interpolation for both enlarging the size of the original pictures and generating additional intermediate frames. Mainly, interpolation is done either by linear or cubic spline functions or by motion estimation/compensation which both can be applied pixel by pixel, or by partitioning frames into blocks. Purpose of this paper is to present an alternative technique combining fractals theory and wavelet decomposition to achieve spatial zoom and slow motion replay of HD digital color video sequences. Fast scene change detection, active scene detection, wavelet subband analysis, and color fractal coding based on Earth Mover's Distance (EMD) measure are used to reduce computational load and to improve visual quality. Experiments show that the proposed scheme achieves better results in terms of overall visual quality compared to the state-of-the-art techniques.

Video de-interlacing by adaptive 4-field global/local motion compensated approach

A de-interlacing algorithm using adaptive 4-field global/local motion compensated approach is presented. It consists of block-based directional edge interpolation, same-parity 4-field motion detection, global/local motion estimation and compensation. The edges are sharper when the directional edge interpolation is adopted. The same parity 4-field motion detection and the 4-field local motion estimation detect the static areas and fast motion by four reference fields, and the global motion estimation detects the camera panning and zooming motions. The global and local motion compensation recover the interlaced videos to the progressive ones. Experimental results show that the peak signal-to-noise ratio of our proposed algorithm is 2/spl sim/3 dB higher than that of previous studies and attain the best quality of subjective view.