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Scholars worldwide have shown considerable interest in the industrial sector, mainly due to its abundant resources, which have facilitated the adoption of conveyor belt technologies like Robotic Arm-Based Conveyor Belts (RACBs). RACBs rely on four primary movements: (i.e., joint, motor, gear, and sensor), which can have a significant impact on the overall motions and motion estimation. To optimize these operations, an assistive algorithm has been developed to enhance the effectiveness of motion by achieving favorable gains. However, each motion requires specific criteria for Fractional Order Proportional Integral Derivative (FOPID) controller gains, leading to various challenges. These challenges include the existence of multiple evaluation and selection criteria, the significance of these criteria for each motion, the trade-off between criterion performance for each motion, and determining critical values for the criteria. As a result, the evaluation and selection of the Proposed Jaya optimization algorithm for RACB motion control becomes a complex problem. To address these challenges, this study proposes a novel integrated approach for selecting the Jaya optimization algorithm in different RACB motions. The approach incorporates two evaluation methods: the Nonlinear Autoregressive Moving Average with exogenous inputs (NARMA-L2) controller for Neural Network (NN) weighting of the criteria, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for selecting the Jaya optimization algorithm. The approach consists of three main phases: RACB-based NARMA-L2 Controller Identification and Pre-processing, Development of NARMA-L2 controller-based NARMA(L2)-FO(ANFIS)PD-I, and Evaluation of FOPID criteria based on JOA. The proposed approach is evaluated based on NARMA(L2)- FO(ANFIS)PD-I that given 0.4074, 0.3156, 0.3724, 0.1898 and 0.2135 for K_p_joint, K_i_motor, K_d_sensor, λ_gear, and µ_N respectively, which verifies the soundness of the proposed methodology. [ABSTRACT FROM AUTHOR]

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It is a challenge work to estimate the 3D human motion from image sequence. There are some problems, such as unsatisfactory estimation error, ambiguous matching and transient occlusion. Although the prior information of learning large‐scale samples exists, these problems are still difficult to be solved. How to extract the feature of the high‐dimensional (HD) sample of 3D human motion and find the desired one will become the key to solve these problems above. Some dimension reduction methods can extract the sample features and build the low‐dimensional (LD) space to view their LD features, but how to search the relevant valid and desired LD samples remains the bottleneck problem, which can be used to reconstruct the 3D human motions denoted by the corresponding high‐dimensional samples. Thus, a new method called dynamic manifold Boltzmann optimization (DMBO) is proposed to estimate the 3D human motion from multi‐view images. DMBO can find the best matching 3D human motion model by the help of the self‐supervised learning from Gaussian incremental dimension reduction model (GIDRM). DMBO can avoid the local optimum during searching and solve the problems above, so that the generation of the accurate 3D human motion corresponding to multi‐view images can be achieved. [ABSTRACT FROM AUTHOR]

In the contemporary video surveillance system, there have been many efforts made to maintain high security and extend the coverage areas in most of the countries around the world. The deployment of many surveillance cameras and sensors capable of detecting abnormal and meaningful events on the territories' streets and airports is an aspect of internal security. There are two main problems that affect the homeland security system, and the security cameras and sensors are not enough to cover all areas in the country. This is because of the high cost of the video surveillance cameras and the sensor installations, and the non‐standardisation of security cameras and sensors manufacturing, which is due to the differentiated infrastructure of companies or organizations that provide home security. The authors introduce a design and hardware implementation of a motion estimation (ME) co‐processor that can be used for video surveillance cameras in homeland security. The proposed ME co‐processor, if adopted in video surveillance cameras, can be connected utilising an internet of video things infrastructure (IoVT). The proposed co‐processor is suited for high‐efficiency encoding video surveillance systems (H.265/HEVC). Furthermore, to reduce the memory I/O, data reuse Level A and Level B have been used in the proposed architecture while taking full advantage of the hardware resources. Moreover, an effective local memory has been used to reuse the data during the process of loading both the search area and the current block into the processing element array (PE array). The performance of the proposed architecture has been calculated using subjective and quantitative measures techniques and compared to the full search block‐based motion estimation (FSBB‐ME) algorithm. Moreover, the proposed architecture achieves a very high video resolution accuracy that is similar to the accuracy of the FSBB‐ME algorithm. Modelism‐version10.4a has been used for simulation and time verification testing proposes. The proposed ME co‐processor can be embedded in the compressing decompressing and high definition broadcast for video surveillance systems. [ABSTRACT FROM AUTHOR]

Fire hazards result in huge damage to the natural resources, public properties, and loss of human and animals lives. Huge fire catches up in remote areas and expands suddenly to nearby areas and eventually becomes uncontrollable. Though sensor based fire detectors are available in market, its function is dependent on the proximity of the sensor to the fire incident, and may not be useful for detecting fire in a remote place where a fire incident is not expected. Hence electronic fire detection systems are inefficient by its nature of operation. Therefore, this article proposes a method for effective fire detection, regardless of the distance of the fire sensing system. The fire detection mechanism is based on the motion occurring between the blocks of a reference frame and the current frame in the test video. This motion estimation based method is capable of detecting the presence of fire at a distance that is better than fire detection systems based on fire sensors. Fire detection performance analysis of the system is found varying based on the block size chosen in motion estimation algorithm. Similarly, detection performance analysis had been found based on the distance of the fire measured from location of the camera. The results obtained in this paper show that the proposed algorithm is suitable for fire detection with a moderate coverage distance with scope for future improvement. [ABSTRACT FROM AUTHOR]

In order to study the motion recovery and segmentation of biomedical microscopic cell images, firstly, the accuracy of motion estimation of the model was observed by selecting the conscious mice with limited brain. The speed was embedded in the HMM (Hidden Markov Model) to construct the motion recovery model of microscopic image and simulate it. Then, a high-resolution cell image database was set up for the images observed, and then a cell image segmentation model was constructed by using convolutional neural network, and it was simulated and evaluated. The results show that in the quantitative analysis of motion recovery by HMM and SEHMM (Hidden Markov Model with Speed Embedding), SEHMM can reduce the number of inconsistencies to a greater extent and its accuracy is higher than HMM. But when the speed is closer to zero from k-1 to k, it is basically a static stage, and then there is no significant difference between SEHMM and HMM. In the analysis of Unet network test, it is found that both the segmentation accuracy curve and the error curve can achieve high accuracy and small error when epoch is small. Whereas, with the increase of epoch, the accuracy and error will not change significantly, and it is likely that the network will be over-fitted and the computational burden of the software will increase. In the test and analysis of network model with different convolution layers, it is found that when the number of convolution layers is set to 4, the accuracy of image segmentation is the highest and the error is the lowest, and the effect is the most obvious. Therefore, through the research, it is found that the SEHMM model can be built by embedding speed into HMM to achieve better results. In image segmentation, high accuracy and low error can be achieved by reasonably arranging the number of convolution layers in the convolution neural network model. Although there are some problems in the research process, it still provides experimental basis for solving the problems of cell segmentation in later biomedical microscopy, and has important research significance. [ABSTRACT FROM AUTHOR]

4-Dimensional cone-beam computed tomography (4D-CBCT) offers several key advantages over conventional 3D-CBCT in moving target localization/delineation, structure de-blurring, target motion tracking, treatment dose accumulation and adaptive radiation therapy. However, the use of the 4D-CBCT in current radiation therapy practices has been limited, mostly due to its sub-optimal image quality from limited angular sampling of cone-beam projections. In this study, we summarized the recent developments of 4D-CBCT reconstruction techniques for image quality improvement, and introduced our developments of a new 4D-CBCT reconstruction technique which features simultaneous motion estimation and image reconstruction (SMEIR). Based on the original SMEIR scheme, biomechanical modeling-guided SMEIR (SMEIR-Bio) was introduced to further improve the reconstruction accuracy of fine details in lung 4D-CBCTs. To improve the efficiency of reconstruction, we recently developed a U-net-based deformation-vector-field (DVF) optimization technique to leverage a population-based deep learning scheme to improve the accuracy of intra-lung DVFs (SMEIR-Unet), without explicit biomechanical modeling. Details of each of the SMEIR, SMEIR-Bio and SMEIR-Unet techniques were included in this study, along with the corresponding results comparing the reconstruction accuracy in terms of CBCT images and the DVFs. We also discussed the application prospects of the SMEIR-type techniques in image-guided radiation therapy and adaptive radiation therapy, and presented potential schemes on future developments to achieve faster and more accurate 4D-CBCT imaging. [ABSTRACT FROM AUTHOR]

Recently, the diverse virtual reality devices are developed and utilized. Particularly, the devices that recognize the motions of users such as griping hands and opening hands are issued to utilize the motions of the users as input methodology. Traditional research about motion recognition suggests user's motion estimation methods by calculating Bayesian probability after measuring the orientation of the motions by a Myo, which is one of contact-type motion recognition devices. However, the motion estimation methods have the problem of low motion estimation accuracy, given that orientation is defined by x, y, and z, which are calculated separately only considering the values of the corresponding axis. In order to improve motion estimation accuracy, motions should be estimated by considering the values of all axis. This paper proposes a method using genetic algorithm to calculate weights, which are applied to estimate motions through Bayesian probability by considering the values of all axis after measuring user's motions with a Myo. The proposed method consists of three steps. First, the Bayesian probability is calculated by considering the correlations of x, y, and z of the orientation of a Myo. Second, weights are determined by applying genetic algorithm. Third, motions are estimated through the Bayesian probability with the determined weights. Experiments were conducted to compare the Bayesian probability between the traditional method based on min/max and the proposed method, which showed that the proposed method had reduced the difference of the orientations by 32%. [ABSTRACT FROM AUTHOR]

Abstract Video compression is the process of reducing the amount of data required to represent digital video while preserving an acceptable video quality. Recent studies on video compression have focused on multimedia transmission, videophones, teleconferencing, high definition television, CD-ROM storage, etc. The idea of compression techniques is to remove the redundant information that exists in the video sequences. Motion compensation predictive coding is the main coding tool for removing temporal redundancy of video sequences and it typically accounts for 50–80% of video encoding complexity. This technique has been adopted by all of the existing International Video Coding Standards. It assumes that the current frame can be locally modelled as a translation of the reference frames. The practical and widely method used to carry out motion compensated prediction is block matching algorithm. In this method, video frames are divided into a set of non-overlapped macroblocks and compared with the search area in the reference frame in order to find the best matching macroblock. This will carry out displacement vectors that stipulate the movement of the macroblocks from one location to another in the reference frame. Checking all these locations is called Full Search, which provides the best result. However, this algorithm suffers from long computational time, which necessitates improvement. Several methods of Fast Block Matching algorithm are developed to reduce the computation complexity. This paper focuses on a survey for two video compression techniques: the first is called the lossless block matching algorithm process, in which the computational time required to determine the matching macroblock of the Full Search is decreased while the resolution of the predicted frames is the same as for the Full Search. The second is called lossy block matching algorithm process, which reduces the computational complexity effectively but the search result's quality is not the same as for the Full Search. [ABSTRACT FROM AUTHOR]

The high efficiency video coding (HEVC) standard has opened the door to high-quality multimedia contents and new formats such as ultra-high definition as a result of the unceasing demands of the market. This standard is able to outperform prior standards by up to 50% in terms of perceptual video quality, but at the cost of extremely large computational complexities. For this reason, the development of fast coding algorithms is now a requirement to make HEVC an adequate candidate for real-world scenarios. In this regard, this paper proposes a collaborative CPU + GPU coding architecture for this standard, in which the CPU performs a coarse-grained parallelization of the encoder, while the GPU carries out a fast motion estimation. Given that the GPU algorithm can work together with a wide variety of parallel algorithms, this paper evaluates two of them: tiles, defined in the standard, and slices, already present in previous standards. Results indicate that slices are more adequate in terms of parallel efficiency (10.75 × speedup on average using 12 threads), while tiles achieve better coding efficiency. [ABSTRACT FROM AUTHOR]

ITU-T Video Coding Expert Group and ISO/IEC Moving Picture Expert Group are studying the potential need for standardization of the future video coding technology with a compression capability that significantly exceeds that of the current High Efficiency Video Coding (HEVC) standard, including its current extensions. Both groups are working together on this exploration activity in a collaboration effort known as Joint Video Exploration Team to evaluate compression technology designs proposed by their experts in this area. Preliminary results show that the new model achieves 25% bitrate reduction, but at a cost of extremely high computational complexity (11 × ) with respect to HEVC. This paper proposes a pre-analysis algorithm designed to extract motion information of a frame, which is later used in the Motion Estimation (ME) module to speed up the encoder, showing that around 27% of the reference frames can be skipped and that more than 62% of the time is saved in the integer ME operation with a negligible impact of 0.11% in BD-rate. [ABSTRACT FROM AUTHOR]

BACKGROUND: In computed tomography (CT), a patient motion would result in degraded spatial resolution and image artifacts. OBJECTIVE: To eliminate motion artifacts, this study presents a method to estimate the motion parameters from sinograms based on extended difference function. METHODS: Based on our previous work, we first divide the projection data into two parts according to view angles and take Radon transform. Then, we calculate the extended difference functions and search for the minimum points. The relative displacements can be determined by the minimum points, and the motion can be estimated by the relationships between the relative displacements and motion parameters. Finally, we introduce the estimated parameters into the reconstruction process to compensate for the motion effects. RESULTS: The simulation results show that the running times can reduce by about 30% than our previous work. In phantom experiments, the relative mean rotation excursion (RMRE) and relative mean translation excursion (RMTE) of the new method are lower than the conventional Helgason–Ludwig consistency condition (HLCC) based method and comparable to our previous work. Compare with the HLCC method, the root mean square error (RMSE) of the new method also reduces, while the Pearson correlation coefficient (CC) and mean structural similarity index (MSSIM) increase. CONCLUSIONS: The proposed new method yields the improved performance on accuracy of motion estimation with higher computational efficiency, and thus it can produce high-quality images. [ABSTRACT FROM AUTHOR]

Abstract Dense motion estimations obtained from optical flow techniques play a significant role in many image processing and computer vision tasks. Remarkable progress has been made in both theory and its application in practice. In this paper, we provide a systematic review of recent optical flow techniques with a focus on the variational method and approaches based on Convolutional Neural Networks (CNNs). These two categories have led to state-of-the-art performance. We discuss recent modifications and extensions of the original model, and highlight remaining challenges. For the first time, we provide an overview of recent CNN-based optical flow methods and discuss their potential and current limitations. Highlights • Introducing optical flow: the basic concepts, the characteristics of the variational and CNN-based techniques, and the evaluation measures. • Discussing developments of the variational method, analyzing the challenges and illustrating the corresponding treating strategies of it. • Describing the conception of the CNN-based technique, and give a detailed discussion of the issues of this technique. [ABSTRACT FROM AUTHOR]

A residue number system (RNS) has an inherent parallel structure that can be utilized for improving computer hardware systems. An RNS represents large integer numbers as a smaller integer set, or residues of a modulo set, without carry propagation between them. Hence mathematical operations, such as addition or subtraction, can be performed on the residues independently. This paper proposes an RNS implementation of motion estimation for the latest video coding standard known as high-efficiency video coding (HEVC) or H.265. Since motion estimation is the most computationally intensive task in video coding, several simplified algorithms are proposed for mitigating the problem, but the majority of them result in a worsening peak signal-to-noise ratio (PSNR) or bit-rate performance, or sometimes both. This paper also proposes a modified algorithm based on a test-zone (TZ) search algorithm, a widely used fast-search algorithm with good rate-distortion performance, suitable for hardware implementation for encoding ultra-high-definition videos in real time. The results show that worst-case PSNR degradation and bit-rate increases compared with the TZ search in the HEVC reference software implementation are negligible, and the hardware gate count is less than for many other designs in the literature. [ABSTRACT FROM AUTHOR]

Frame rate up-conversion (FRUC) is a technology for generating a higher frame rate video from one with a lower frame rate. The multi-view plus depth map (MVD) system has been recently considered a promising solution for three-dimensional video systems. Accordingly, an FRUC efficient solution for the MVD system is greatly needed. In this paper, we thus propose an FRUC approach that considers the MVD system characteristics. It divides a frame into multiple layers using a depth map, and it reconstructs occluded regions using neighboring views and depth map information. With reconstructed information, it accurately performs motion estimation when a region occlusion occurs. Experimental results demonstrated that the proposed method significantly improved the quality of interpolated frames, and it can effectively complement other conventional FRUC approaches. [ABSTRACT FROM AUTHOR]

Presents the front cover for this issue of the publication. [ABSTRACT FROM AUTHOR]

Combining diverging ultrasound waves and microbubbles could improve contrast-enhanced echocardiography (CEE), by providing enhanced temporal resolution for cardiac function assessment over a large imaging field of view. However, current image formation techniques using coherent summation of echoes from multiple steered diverging waves (DWs) are susceptible to tissue and microbubble motion artifacts, resulting in poor image quality. In this study, we used correlation-based 2-D motion estimation to perform motion compensation for CEE using DWs. The accuracy of this motion estimation method was evaluated with Field II simulations. The root-mean-square velocity errors were 5.9% ± 0.2% and 19.5% ± 0.4% in the axial and lateral directions, when normalized to the maximum value of 62.8 cm/s which is comparable to the highest speed of blood flow in the left ventricle (LV). The effects of this method on image contrast ratio (CR) and contrast-to-noise ratio (CNR) were tested in vitro using a tissue mimicking rotating disk with a diameter of 10 cm. Compared against the control without motion compensation, a mean increase of 12 dB in CR and 7 dB in CNR were demonstrated when using this motion compensation method. The motion correction algorithm was tested in vivo on a CEE data set acquired with the Ultrasound Array Research Platform II performing coherent DW imaging. Improvement of the B-mode and contrast-mode image quality with cardiac motion and blood flow-induced microbubble motion was achieved. The results of motion estimation were further processed to interpret blood flow in the LV. This allowed for a triplex cardiac imaging technique, consisting of B mode, contrast mode, and 2-D vector flow imaging with a high frame rate of 250 Hz. [ABSTRACT FROM AUTHOR]

Objective: Intra-interventional respiratory motion estimation is becoming a vital component in modern radiation therapy delivery or high intensity focused ultrasound systems. The treatment quality could tremendously benefit from more accurate dose delivery using real-time motion tracking based on magnetic-resonance (MR) or ultrasound (US) imaging techniques. However, current practice often relies on indirect measurements of external breathing indicators, which has an inherently limited accuracy. In this work, we present a new approach that is applicable to challenging real-time capable imaging modalities like MR-Linac scanners and 3D-US by employing contrast-invariant feature descriptors. Methods: We combine GPU-accelerated image-based realtime tracking of sparsely distributed feature points and a dense patient-specific motion-model for regularisation and sparse-to-dense interpolation within a unified optimization framework. Results: We achieve highly accurate motion predictions with landmark errors of $\approx$ 1 mm for MRI (and $\approx$ 2 mm for US) and substantial improvements over classical template tracking strategies. Conclusion: Our technique can model physiological respiratory motion more realistically and deals particularly well with the sliding of lungs against the rib cage. Significance: Our model-based sparse-to-dense image registration approach allows for accurate and realtime respiratory motion tracking in image-guided interventions. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive pattern selection strategy for diamond search (DS) algorithm is proposed. DS is one of state-of-the-art motion estimation algorithms, while the fixed search strategy and the singular termination strategy lead to lots of redundancy of search points. The proposed search strategy is based on the observation that more than 75% motion vector differences are around the initial predicted search centre in the range of 1. In our search strategy, small diamond search pattern and large diamond search pattern are adaptively used according to the distribution of motion vector differences and the matching error information of initial search centre. Our search strategy focuses on how to use small diamond search pattern and large diamond search pattern more efficiently than diamond search algorithm without introducing additional search patterns. Experimental results show that the proposed algorithm can save about 10.81 search points and achieve 0.12 dB higher PSNR on average compared to DS. [ABSTRACT FROM AUTHOR]

Due to the popularity of 3D videos, 2D-to-3D video conversion has become a hot research topic for the past few years. The most critical issue in 3D video synthesis is the estimation of depth maps for the video frames. Numerous efforts have been devoted in fully automatic and semi-automatic depth estimation approaches, although the discontinuity of depth field and the ambiguity of motion boundary are still the main challenges in depth estimation. This paper proposes a semi-automatic structure-aware sparse-to-dense depth estimation method, which leverages the tensor voting at two different levels to propagate depth across frames. In the first level, a 4D tensor voting is performed to remove outliers caused by inaccurate motion estimation. Noticing that the 4D tensors of correctly matched points should lie on the smooth layer in the manifold, we utilize the variety saliency defined by the eigen-system of the tensor for outlier removal. In the second level, a high-dimensional tensor voting algorithm, incorporating spatial location, motion, and color into the tensor representation, is devised to propagate the depth from the sparse points to the entire image domain. By projecting the input feature into the tangent space, the relation between the location, motion, color, and the depth can be established by voting process. Extensive experiments on public data set validate the effectiveness of the proposed method in comparison with state-of-the-art depth estimation approaches. [ABSTRACT FROM AUTHOR]

This paper presents a dense method for estimating planar motion with a laser scanner. Starting from a symmetric representation of geometric consistency between scans, we derive a precise range flow constraint and express the motion of the scan observations as a function of the rigid motion of the scanner. In contrast to existing techniques, which align the incoming scan with either the previous one or the last selected keyscan, we propose a combined and efficient formulation to jointly align all these three scans at every iteration. This new formulation preserves the advantages of keyscan-based strategies but, is more robust against suboptimal selection of keyscans and the presence of moving objects. An extensive evaluation of our method is presented with simulated and real data in both static and dynamic environments. Results show that our approach is one order of magnitude faster and significantly more accurate than existing methods in all the conducted experiments. With a runtime of about one millisecond, it is suitable for those robotic applications that require planar odometry with low computational cost. The code is available online as a ROS package. [ABSTRACT FROM AUTHOR]

We present a novel and effective learning-based frame rate upconversion (FRUC) scheme, using linear mapping. The proposed learning-based FRUC scheme consists of: 1) a new hierarchical extended bilateral motion estimation (HEBME) method; 2) a light-weight motion deblur (LWMD) method; and 3) a synthesis-based motion-compensated frame interpolation (S-MCFI) method. First, the HEBME method considerably enhances the accuracy of the motion estimation (ME), which can lead to a significant improvement of the FRUC performance. The proposed HEBME method consists of two ME pyramids with a three-layered hierarchy, where the motion vectors (MVs) are searched in a coarse-to-fine manner via each pyramid. The found MVs are further refined in an enhanced resolution of four times by jointly combining the MVs from the two pyramids. The HEBME method employs a new elaborate matching criterion for precise ME which effectively combines a bilateral absolute difference, an edge variance, pixel variances, and an MV difference among two consecutive blocks and its neighboring blocks. Second, the LWMD method uses the MVs found by the HEBME method and removes the small motion blurs in original frames via transformations by linear mapping. Third, the S-MCFI method finally generates interpolated frames by applying linear mapping kernels for the deblurred original frames. In consequence, our FRUC scheme is capable of precisely generating interpolated frames based on the HEBME for accurate ME, the S-MCFI for elaborate frame interpolation, and the LWMD for contrast enhancement. The experimental results show that our FRUC significantly outperforms the state-of-the-art non-deep learning-based schemes with an average of 1.42 dB higher in the peak signal-to-noise-ratio and shows comparable performance with the state-of-the-art deep learning-based scheme. [ABSTRACT FROM AUTHOR]

Selfie photography from the hand-held camera is becoming a popular media type. Although being convenient and flexible, it suffers from low camera motion stability, small field of view, and limited background content. These limitations can annoy users, especially, when touring a place of interest and taking selfie videos. In this paper, we present a novel method to create what we call a BiggerSelfie that deals with these shortcomings. Using a video of the environment that has partial content overlap with the selfie video, we stitch plausible frames selected from the environment video to the original selfie frames and stabilize the composed video content with a portrait-preserving constraint. Using the proposed method, one can easily obtain a stable selfie video with expanded background content by merely capturing some background shots. We show various results and several evaluations to demonstrate the applicability of our method. [ABSTRACT FROM AUTHOR]

In the context of motion estimation for video coding, combining a successive elimination algorithm (SEA) with a motion estimation algorithm reduces the number of computed cost functions without impact on rate or distortion. The SEAs use the sum of absolute differences to eliminate motion vector candidates in the search area that cannot improve the current minimum. The novelty in this paper is that instead of relying on a static geometric pattern (i.e., like a spiral), we proposed a dynamic algorithm that creates a cost-based search orderings. A dynamic cost-based search ordering not only improves elimination but also allows for early termination which removes, on average, 61% of the block-matching loop iterations performed by the rate-constrained successive elimination algorithm (RCSEA). Our experiments show that the proposed solution is 5 times faster than the high efficiency video coding (HEVC) HM encoder software in full search mode with a 0.02% impact on BD-Rate. This is twice the speed of the HEVC HM software encoder using only the RCSEA. [ABSTRACT FROM AUTHOR]

Pedestrian detection is a challenging area in the intelligent vehicles domain. During the last years, many works have been proposed to efficiently detect motion in images. However, the problem becomes more complex when it comes to detecting moving areas while the vehicle is also moving. This paper presents a variational optical flow-based method for motion estimation in vehicular traffic scenarios. We introduce a framework for detecting motion areas with small and large displacements by computing optical flow using a multilevel architecture. The flow field is estimated at the shortest level and then successively computed until the largest level. We include a filtering parameter and a warping process using bicubic interpolation to combine the intermediate flow fields computed at each level during optimization to gain better performance. Furthermore, we find that by including a penalization function, our system is able to effectively reduce the presence of outliers and deal with all expected circumstances in real scenes. Experimental results are performed on various image sequences from Daimler Pedestrian Dataset that includes urban traffic scenarios. Our evaluation demonstrates that despite the complexity of the evaluated scenes, the motion areas with both moving and static camera can be effectively identified. [ABSTRACT FROM AUTHOR]

Abstract This work describes an efficient technique to sequentially combine estimates resulting from individual sets of measurements provided by a network of satellites. The prescribed method is especially effective to estimate motion states of an uncooperative space object using range image data. The technique, which is fast and suitable for on-line applications, could also be effective to capture stray objects or those satellites that require periodic servicing. Such missions call for high degree of precision and reliable estimation methods. In fact, the proposed estimation architecture consists of a network of synchronized platforms, i.e., Observer Satellites (OS), each with processing power and transmission capability, that are observing a common Target Space Object (TSO). All OSs are expected to have suitable measuring devices, such as active vision sensor, that provide sensory range image data. Each platform could also independently estimate its objective based on its own observations. The estimates are then transmitted to a fusion center to assimilate the fused estimate that is more accurate than any individual estimates. As a specific example, we show exploiting efficient algorithms in processing of range image data, filtering, and fusion of estimates enables the proposed method to be especially effective for active debris removal. Different case studies confirm that the method is capable of processing measured data fairly quickly and producing fused estimates with a tangible decrease in estimation error. [ABSTRACT FROM AUTHOR]

Owing to the limited response time of liquid crystal displays (LCDs), the motion-blurring effect is a critical problem to overcome for LCD applications. Among the available motion-blurring reduction methods, frame-rate upconversion (FRUC) presents an outstanding performance; however, it also yields a high computational load, which causes a design challenge for a hardware-efficient FRUC engine running advanced specifications. In this paper, a hardware-efficient FRUC algorithm and architecture were proposed, comprising three core techniques: bidirectional 3-D recursive search (BD-3DRS), spatial and temporal motion vector refinement (ST-MVR), and spatial and temporal overlapped-block motion compensation (ST-OBMC). BD-3DRS provides an initial MV with minor computational overhead. Subsequently, ST-MVR refines the initial MV to generate a more accurate final MV. With this final MV, ST-OBMC has finely processed the interpolated block, especially for the textural smoothness across block boundaries. The experiment results indicated that compared with other sophisticated FRUC methods, computation was reduced by 80% with a minor degradation in a peak signal-to-noise ratio of less than 2%. The hardware architecture was realized using the CMOS technology, and the chip size was $1.20 \times 1.20$ mm2. The working frequency was 270 MHz with a power consumption of 128.08 mW. The maximum throughput was as high as 508 Mpixels/s, which can support a resolution of $3840 \times 2160$ from 60 to 120 Hz. Compared with other FRUC architectures, this paper has demonstrated superior hardware efficiencies in terms of area usage and energy consumption. [ABSTRACT FROM AUTHOR]

There are diverse types of devices based on natural user interface/experience for humanized computing. One such device, the MYO allows the measurement of arm motions and uses them as an interface based on gestures. There are several research works for measuring the arm motions using MYOs. For example, one of the studies defines two types of motions for a forearm and for an upper arm, respectively. The orientations of the two types are measured by two MYOs. Bayesian probabilities are calculated based on the measured orientations and are utilized to estimate the orientations of the upper arm that is not being measured. However, because the orientation of the MYO can be expressed by one quaternion, the Bayesian probability by quaternions is more accurate than the Bayesian probability by each element of quaternions. This paper proposes a motion estimation method to increase the accuracy of motion estimation. The orientations obtained from MYO are expressed by one quaternion and are clustered by K-means. In the experiments, the performance of the proposed method was validated by analyzing the difference between estimated motion quaternions and measured motion quaternions, which showed enhanced performance. [ABSTRACT FROM AUTHOR]

Context: Encoding of video frames in a traditional video coding architecture involves exhaustive computations due to the motion estimation (ME) task. Hence, it requires a considerable amount of computing aid, battery power, and resource memory. These codecs are not effective and reliable for applications like surveillance systems, wireless sensor networks, wireless camcorders, having scarcity in the availability of resources and computing ability. Therefore, in such scenarios, distributed video coding (DVC) represents a viable solution for power-constrained hand-held devices. DVC empowers the adaptability in distributing the complexity between the encoder and the decoder. Objective: Like any other building block, the decoder driven side information (SI) generation module plays a key role in a DVC codec. The efficacy of a DVC codec firmly relies on the quality of the SI generated at the decoder. SI is considered to be the facsimile of the original Wyner-Ziv (WZ) frame. Hence, the superior the quality of SI, improved is the efficiency of the codec. The primary objective of the present work is to enhance the quality of the SI frame so that the overall performance of the DVC is improved. To achieve this objective, this article deals with a hybrid SI generation scheme utilizing the principles of discrete wavelet transform (DWT) and extreme learning machine (ELM) algorithm in a transform domain-based DVC framework. Results: Exhaustive simulations have been carried out for some standard video sequences with the proposed and benchmark schemes. The proposed scheme is evaluated with respect to different performance metrics such as rate-distortion (RD), SI peak-signal-to-noise-ratio (PSNR) vs frame number, number of parity requests per SI frame, and so on. Experimental results and its analyses corroborate that the performance of the proposed technique surpasses as that of the benchmark schemes. [ABSTRACT FROM AUTHOR]

On-site multiview video viewing system can provide people with more immersive viewing experience on various live video shows, such as sports and concerts. To address the high computational complexity of multi-view video coding, several researches have worked on fast motion estimation, fast disparity estimation, and/or fast mode decision. In this paper, we propose a low-complexity encoding scheme that can further exploit inter-view dependencies and depth information. A fast mode selection with threshold-based early termination is presented to select a minimal set of mode candidates according to the reference macroblocks of neighbouring views and expedite the mode decision process. Further, this study also demonstrates that disparity estimation computation and search ranges of both motion estimation and disparity estimation can be significantly refined by using inter-view dependencies and depth information. The experimental results show that the proposed encoding method reduces the coding time by 80% with a negligible PSNR loss. [ABSTRACT FROM AUTHOR]

Abstract It poses great challenges to model-free trackers that the object undergoes large appearance variations due to motion, shape deformation, occlusion and surrounding environments. In this paper, we investigate a novel method for modeling and locating the object by being aware of the weak structures of discriminative parts of both the object and its surroundings. The discriminative parts are modeled based on keypoints and feature descriptors. We separate the discriminative parts into two sets corresponding to object and background, and model their spatial structure relationship with the object. While tracking, the successfully localized parts will contribute to potential centers of the object. Aware of the weak structures, we further cluster potential centers to locate the object. The object scale is also updated adaptively. To increase the accuracy of this weak-structure-aware location inference, we fully explore context in both bottom-up and top-down procedures. In the bottom-up stage, we explore the local motion estimation of low-level pixels. The bottom-up information produces consistent tracking of discriminative parts. In the top-down stage, we build a superpixel kernel model to roughly distinguish the object from its surroundings, which provides guided information for location inference and model update. The effectiveness of the proposed method is verified by evaluation on a popular benchmark and comparison with recent tracking methods. Highlights • A weak-structure-aware solution is proposed for part-based object tracking. • Two sets of parts are combined complementarily to find the object location and scale adaptively. • A bottom-up procedure is introduced to explore pixel-level context for consistent tracking. • A top-down procedure is devised to explore object-level context for effective model support. • The proposed method is tested in extensive experiments to demonstrate its excellent performance. [ABSTRACT FROM AUTHOR]

Graphical abstract Abstract The detection of moving objects and the subtraction of the scene background are significant tasks for intelligent video surveillance systems as it is one among the fundamental steps. Inspired by the challenging cases yet to be resolved in Moving Object Detection (MOD), a new formulation is done to detect moving objects from video sequences based on Robust Principal Component Analysis (RPCA) principle by adopting the regularization of Total Variation (TV) norm using a convergent convex optimization algorithm. While the nuclear norm exploits the low-rank property of background, the sparsity is enhanced by the l 1 -norm and the foreground spatial smoothness is explored by TV regularization. The goodness of this method lies in the reduced computational complexity, quickness and on the superiority acquired in quantitative evaluation based on F-measure, Recall and Precision with respect to the state of the art methods. [ABSTRACT FROM AUTHOR]

The paper presents an efficient VLSI architecture for fast Motion Estimation in video codec using modified Adaptive Rood Pattern Search Algorithm. The proposed architecture uses an interleaved memory arrangement and an early check technique to compute the Sum of Absolute Differences. The proposed design can process High Definition (1080p) video frames in real time while optimizing the hardware area. The architecture has been implemented in verilog HDL and mapped to 45 nm FPGA. It uses only 6.8K gates for the implementation of the datapath and the controller. It achieves a maximum frequency of 120 MHz. However, working at 100 MHz, it is able to process 60 HD (1920×1080) frames per second while consuming 39 mW of power. The proposed architecture achieves premium speed with an optimum power and area requirements and can be suitably incorporated in light-weight video-intensive devices like smart-phones, tablet computers. [ABSTRACT FROM AUTHOR]

A professional-image-quality video encoder LSI for broadcasting and content distribution has been developed with single-chip 4K 60-fps 4:2:2 high-efficiency video coding (HEVC) encoding capability and 8K ultrahigh definition television scalability. Edge-based intramode pruning and statistically adaptive multiblock-size motion estimation (ME) efficiently reduce HEVC’s high computational complexity for real-time processing, while a deeply centralized-mode decision framework maintains high compression performance. Internal reference image caches with 797-Gb/s image feed capability support the 768-GOPS ME computation of distributed motion vector search. Interchip pictures and data exchange features with high-speed data buses are also used for multichip 8K encoding configuration. A subjective evaluation showed that the proposed encoder LSI maintained the same visual quality as an encoder LSI complying with the previous standard while reducing bit rate by 40%. The chip was designed and fabricated in a 28-nm CMOS technology and used to build industry-leading 4K and 8K broadcasting systems. [ABSTRACT FROM AUTHOR]

Rotational coronary angiography using C-arm angiography systems enables intra-procedural 3-D imaging that is considered beneficial for diagnostic assessment and interventional guidance. Despite previous efforts, rotational angiography was not yet successfully established in clinical practice for coronary artery procedures due to challenges associated with substantial intra-scan respiratory and cardiac motion. While gating handles cardiac motion during reconstruction, respiratory motion requires compensation. State-of-the-art algorithms rely on 3-D / 2-D registration that requires an uncompensated reconstruction of sufficient quality. To overcome this limitation, we investigate two prior-free respiratory motion estimation methods based on the optimization of: 1) epipolar consistency conditions (ECCs) and 2) a task-based auto-focus measure (AFM). The methods assess redundancies in projection images or impose favorable properties of 3-D space, respectively, and are used to estimate the respiratory motion of the coronary arteries within rotational angiograms. We evaluate our algorithms on the publicly available CAVAREV benchmark and on clinical data. We quantify reductions in error due to respiratory motion compensation using a dedicated reconstruction domain metric. Moreover, we study the improvements in image quality when using an analytic and a novel temporal total variation regularized algebraic reconstruction algorithm. We observed substantial improvement in all figures of merit compared with the uncompensated case. Improvements in image quality presented as a reduction of double edges, blurring, and noise. Benefits of the proposed corrections were notable even in cases suffering little corruption from respiratory motion, translating to an improvement in the vessel sharpness of (6.08 ± 4.46)% and (14.7 ± 8.80)% when the ECC-based and the AFM-based compensation were applied. On the CAVAREV data, our motion compensation approach exhibits an improvement of (27.6 ± 7.5)% and (97.0 ± 17.7)% when the ECC and AFM were used, respectively. At the time of writing, our method based on AFM is leading the CAVAREV scoreboard. Both motion estimation strategies are purely image-based and accurately estimate the displacements of the coronary arteries due to respiration. While current evidence suggests the superior performance of AFM, future work will further investigate the use of ECC in the context of angiography as they solely rely on geometric calibration and projection-domain images. [ABSTRACT FROM AUTHOR]

For motion estimation Search point pattern based is used as a fast block motion estimation algorithms, it provides significant speedup for motion estimation. the main problem is it suffers from being easily trapped in local minima. This may lead to low robustness in prediction accuracy particularly for image or video sequences wherein the video there are complex motions. The problem introduced especially in one-at-a-time search (OTS) and block-based gradient descent search (BBGDS). Which provide very high speedup ratio. To improve this, the multipath search using more than one search path has been proposed to improve the robustness of BBGDS. It requires more computation, in this the computational requirement is much increased. To remove this drawback, a line searches search (LS) algorithm using multiple OTSs and gradient descent searches are reviewed or proposed in this letter. Also, we introducing a new methodology to reduce computational complexity and improve the PSNR and reduces the number of searches points. The search point patterns in each stage depend on the minima found in these different directions after finding minima we apply the three-step search to find the minima in the particular direction and thus the global minimum can be easily traced more efficiently. Experimental results show that TWO STEP QUADRANT BASED GRADIENT DESCENT SEARCH (TQBGDS) reduces computation load significantly compared with the well-known fast block motion estimation algorithms. [ABSTRACT FROM AUTHOR]

Abstract: We address the problem of autonomous landing of a quadrotor onto a heaving (moving vertically) platform in this paper. A control architecture that consists of a motion estimation module, a trajectory generation module and a tracking control module is proposed. The motion estimation module estimates the absolute motion of the platform and the quadrotor with the measurements from an on‐board accelerometer and vision measurements. Based on these estimates, the trajectory generation module generates a time‐optimal reference trajectory. With the reference trajectory and motion estimation, the tracking control module synthesizes a control command that enables robust tracking of the reference trajectory. Experimental results and comparison with a state‐of‐the‐art landing controller demonstrate the effectiveness of the proposed control architecture. [ABSTRACT FROM AUTHOR]

Accurate estimation of myocardial motion can help to better understand the pathophysiological processes of ischemic heart diseases. However, because of partial and noisy image-derived measurements on the cardiac kinematics, the performance of model-based motion estimation relies heavily on the assumption of noise distribution on the measurement data. While existing studies of model-based motion estimation have often adopted the ℋ2 criteria (e.g. least square error) based on fixed model constraints from mathematical or mechanical nature, we present a robust estimation framework with an adaptive biomechanical model constraint using dual ℋ∞ criteria for the first time. Comparing to the minimization of average gaussian error in ℋ2 criteria, our ℋ∞ criteria aims to minimize the maximum error without regarding the noise distribution. In this work, our dual estimation framework consists of two iterative ℋ∞ filters: One filter for the kinematics estimation and another one for the elasticity estimation. At each time step, heart kinematics is estimated with sub-optimal fixed material parameters, followed by an elasticity property recovering given these sub-optimal kinematic state estimates. Such coupled estimation processes are iteratively repeated as necessary until convergence. We evaluate the performance of dual estimation framework on synthetic data, cine image sequences, and human image sequence. Our dual estimation framework shows a higher tolerance of noise than the conventional extended Kalman filter. The results obtained by both synthetic data of varying noises and magnetic resonance image sequences demonstrate the accuracy and robustness of the proposed strategy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

High Efficiency Video Coding (HEVC), the latest video coding standard, aims to provide coding performance that is much superior to that of its predecessor, H.264, especially for high definition video. To fulfill this goal, the inter-prediction unit (PU) partitions of HEVC are more complex, and the search range of motion estimation (ME) is much larger. As a result, ME becomes a bottleneck in the design of the HEVC inter predictor. In response to this challenge, we developed a hardware-oriented integer ME algorithm and the related hardware implementation. Our proposed algorithm led to a decrease in terms of the Bjontegaard Delta rate when compared with the HEVC test model 15.0. The corresponding hardware solution benefitted from 2-D data reuse supported by horizontal and vertical reference SRAMs, on-chip memory reduction supported by $4\times4$ block compression, and a low-power sum of absolute difference (SAD) tree supported by PU-level chip selection. When adopting a $32\times32$ SAD tree, the minimum and maximum required working frequency for 4K $\times2\text{K}$ at 30 frames/s videos was [375, 500] MHz. These results demonstrated that our proposed solution offered desirable improvement in both coding speed and coding performance. [ABSTRACT FROM AUTHOR]

Accurate indoor 3D models have become a key prerequisite for various applications. Through state-of-the-art image processing techniques, 3D models can be generated from high quality images captured by off-the-shelf digital cameras. To acquire redundant data and produce real scale models, a multi-camera system can be used. However, dedicated approaches for image-based 3D reconstruction using mapping platforms equipped with multiple cameras have not been fully addressed. Assuming the availability of prior information regarding the platform trajectory, this paper presents a new approach for reliable estimation of system motion parameters between different data acquisition epochs of a multi-camera system. This approach, which assumes planar motion of the utilized platform, provides a three-point closed-form solution. The derived solutions are then incorporated within a modified RANSAC framework for outlier detection/removal. It is worth noting that, different from the existing General Camera Model (GCM)-based solutions, the proposed approach is based on a modified co-planarity model, which is essentially a direct extension of the classic stereo-based relative orientation. Moreover, since the proposed approach only provides a maximum number of four possible solutions for system motion parameters over different epochs, it has better computational efficiency when compared to other existing algorithms. Experimental results from real datasets acquired with different configurations have demonstrated the reliability of the proposed approach in motion parameter estimation for indoor multi-camera mapping systems. [ABSTRACT FROM AUTHOR]

In this paper, a novel low bit-depth representation-based motion estimation approach with its hardware architecture is presented. The low bit-depth representation of pixels in the proposed method is constructed by choosing them from the gray-coded bit planes in an efficient way. Additionally, a new matching creation is also presented to improve the motion estimation accuracy. Thanks to the proposed binarization approach, the computation load is considerably reduced compared to filtering-based low bit-depth representation motion estimation methods. A novel hardware architecture for the proposed method is also presented in this paper. Experimental results revealed that the proposed motion estimation method and its hardware architecture provides a good balance between motion estimation accuracy and hardware resources especially for consumer electronics applications. [ABSTRACT FROM AUTHOR]

The quality of a 3D video display depends on virtual view synthesis process which is affected by the bit allocation criterion. The performance of a bit allocation algorithm is dependent on various encoding parameters like quantization parameter, motion vector, mode selection, and so on. Rate-distortion optimization (RDO) is used to efficiently allocate bits with minimum distortion. In 3D video, rate-distortion (RD) property of synthesized view is used to assign bits between texture video and depth map. Existing literature on bit allocation methods use mean square error (MSE) as distortion metric which is not suitable for measuring perceptual quality. In this paper, we propose structural similarity (SSIM)-based joint bit allocation scheme to enhance visual quality of 3D video. Perceptual quality of a synthesized view depends on texture and depth map quality. Thus, SSIM-based RDO is performed on both texture and depth map where SSIM is used as distortion metric in mode decision and motion estimation. SSIM-based distortion model for synthesized view is determined experimentally. As SSIM cannot be related to quantization step, SSIM-MSE relation is used to convert distortion model in terms of MSE. The Lagrange multiplier method is used to solve the bit allocation problem. The proposed algorithm is implemented using 3DV-ATM as well as HEVC. RD curves show reduction in bitrate with an improvement in SSIM of synthesized view. [ABSTRACT FROM AUTHOR]

This paper develops a method to estimate the relative pose, motion, and center of mass of an unknown tumbling target using stereoscopic vision. First, the positions and velocities of detected feature points on the spacecraft are tracked and estimated by the measurements of optical flow and photogrammetry. Random observation noises are included in the process. Second, the angular velocity and attitude of the target are estimated by the least square method and q-method, respectively. Finally, the position and velocity of the center of mass of the spacecraft are recovered based on the relative kinematics of the target by successive images over a predefined observation period. Numerical simulations are conducted to demonstrate the effectiveness and precision of the proposed method. [ABSTRACT FROM AUTHOR]

Motion estimation (ME) plays an important part in the functioning of the video codec by identifying and reducing the temporal redundancies in between successive frames of a video sequence. Block matching algorithm (BMA) has been accepted as one of the finest approaches for motion estimation due to its efficiency and ease of implementation. This paper presents a new and improved iterative and adaptive search strategy for block-based motion estimation along with its efficient hardware implementation. Since it is expected that there will be more demand for streaming video services on mobile devices, designing fine tuning algorithm with dedicated efficient hardware would provide significant benefits. The present motion estimation algorithm is adaptive in nature that takes into consideration the motion content of the current frame while predicting the motion vector. The adaptive nature of the search eases the complexity of motion estimation and the algorithm makes use of the correlation present among the motion vectors of the neighboring blocks to lower the number of search position. Traditionally, such adaptive algorithms are executed by CPU cores running a software stack. Since software involves a significant amount of overheads like fetching into cache, branches, stalls etc., the efficiency of the proposed algorithm can be overshadowed by the hardware platform. To avoid this, compact hardware architecture was developed which stands ahead of other existing architectures as shown in comparison. The VLSI design for the proposed algorithm presented in this work deals with the generation of the adaptive search pattern and use of interleaved memory organization fasten the operational speed. A profitable data re-use scheme and involvement of minimum processing elements required for parallelization reduce the on-chip area. Working at a frequency of 243 MHz, the proposed design can process 66 720p HD (1280 × 720) frames in one second consuming an area of 38.2 K gate equivalent. Hence, the proposed design can be incorporated in video codecs to be used in commercial devices like camcorders, smart phones and other portable, battery-powered video consumer devices. The proposed research method achieves significantly improved results both in terms of algorithmic metrics (PSNR) as well hardware performance (speed, area). [ABSTRACT FROM AUTHOR]

Cardiac X-ray computed tomography (CT) imaging is still challenging due to the cardiac motion during CT scanning, which leads to the presence of motion artifacts in the reconstructed image. In response, many cardiac X-ray CT imaging algorithms have been proposed, based on motion estimation (ME) and motion compensation (MC), to improve the image quality by alleviating the motion artifacts in the reconstructed image. However, these ME/MC algorithms are mainly based on an axial scan or a low-pitch helical scan. In this paper, we propose a ME/MC-based cardiac imaging algorithm for the data set acquired from a helical scan with an ordinary pitch of around 1.0 so as to obtain the whole cardiac image within a single scan of short time without ECG gating. In the proposed algorithm, a sequence of partial angle reconstructed (PAR) images is generated by using consecutive parts of the sinogram, each of which has a small angular span. Subsequently, an initial 4-D motion vector field (MVF) is obtained using multiple pairs of conjugate PAR images. The 4-D MVF is then refined based on an image quality metric so as to improve the quality of the motion-compensated image. Finally, a time-resolved cardiac image is obtained by performing motion-compensated image reconstruction by using the refined 4-D MVF. Using digital XCAT phantom data sets and a human data set commonly obtained via a helical scan with a pitch of 1.0, we demonstrate that the proposed algorithm significantly improves the image quality by alleviating motion artifacts. [ABSTRACT FROM AUTHOR]

Sub-pixel motion estimation plays a vital role in a multitude of video applications, including encoding, audiovisual archiving/heritage and super-resolution enhancement. Most existing block-based methods rely on the implicit assumption that blocks can be accurately predicted through appropriate shifts. In particular, shifted blocks in the target frame are estimated from the associated anchor frame blocks. The present paper introduces a different strategy, which discards this assumption and treats anchor and target frame blocks equally, as sub-pixel shifted versions of an unavailable implied block. The new method attempts to construct this implied block and, by calculating the “imaginary” motion vectors that relate it to the two existing blocks, it estimates the wanted motion vectors more accurately. This approach aims at extracting motion vectors that more accurately represent the actual movements of objects, minimizing the interpolation error that is associated with sub-pixel shifting, which manifests as blurring and a lowering of contrast. The new method focuses on accurate motion estimation, paying less attention to the associated computational load. Hence, the approach is both inspired from, and proposed for, super-resolution enhancement scenarios, where higher definition motion image sequences are estimated from their available lower definition counterparts. In order to implement the new strategy, an algorithm for reversing the bilinear sub-pixel shift of a block (unshifting) is implemented and validated. Comparisons between original blocks of images and blocks that have been shifted and unshifted back to their original coordinates showcase the accuracy of the unshifting process. The proposed motion estimation method is evaluated through a number of different experimental assessment procedures and metrics, comparing it to existing high-accuracy state-of-the-art motion estimation methods. [ABSTRACT FROM AUTHOR]

Block-based motion estimation is widely used in video compression for reducing the temporal data redundancy. However, it is still a main problem to effectively reduce the computational complexity of motion estimation. The median predictor is usually used for initial search center prediction, however it is not always accurate enough, especially for fast motion sequences. In this paper, a novel dynamic initial search pattern algorithm for fast block-based motion estimation is proposed. Based on the observation that the components of the current motion vector are very similar to the corresponding components of its neighboring motion vectors, Cartesian product of neighboring motion vectors is introduced to generate the proposed dynamic initial search pattern (DISP). And then the cross search pattern is employed to search for the best matching block. The number of search points of the proposed DISP is adaptive to the neighboring correlation of the current block. In fact, the proposed DISP can be considered as a generalization of median prediction scheme and it performs better in capturing the best matching block than median prediction. Experiment results show that the proposed DISP method with small cross search pattern can save about 1.71 search points on average compared with adaptive rood pattern search (ARPS) algorithm and can achieve the similar PSNR to full search (FS) algorithm by combining large cross search pattern. [ABSTRACT FROM AUTHOR]

Background and aim: Understanding the maturation of upper limb (UL) movement characteristics in typically developing (TD) children is key to explore UL deficits in those with neurodevelopmental disorders. Three-dimensional motion analysis (3DMA) offers a reliable tool to comprehensively evaluate UL motion. However, studies thus far mainly focused on specific pre-defined parameters extracted from kinematic waveforms. Here, we investigated age-related differences in UL movement characteristics over the entire movement cycle in TD children. Participants and methods: We assessed the non-dominant UL of 60 TD children (mean age 10y3m±3y1m) using 3DMA during eight tasks: reaching (forwards (RF), upwards (RU), sideways (RS)), reach-to-grasp (sphere (RGS), vertical cylinder (RGV)) and activities-of-daily-living mimicking tasks (hand-to-head (HTH), hand-to-mouth (HTM), hand-to-shoulder (HTS)). We investigated differences between four age-groups (5-7y, 8-10y, 11-12y, 13-15y) in: (1) spatiotemporal parameters (movement duration, peak velocity, time-to-peak velocity and trajectory straightness), and (2) 12 UL joint angles, using Statistical Parametric Mapping (SPM). Results: We found that the 5-7y children moved with lower peak velocity and less straight trajectories compared to the 11-12y group (peak velocity: RS, HTS, p<0.01; trajectory: RU, RS, RGV, HTS, p<0.01) and the 13-15y group (peak velocity: RF, RS, RGS, RGV, HTH, HTS, p<0.01; trajectory, all tasks, p<0.01). The 5-7y children showed increased scapular protraction compared to older children (8-10y and 11-12y, HTS), as well as increased scapular medial rotation compared to the 13-15y group (RGS). During RU, the 5-7y children moved more towards the frontal plane (shoulder), unlike the 13-15y group. Lastly, the 5-7y group used less elbow flexion than older children (11-12y and 13-15y) during HTH and HTS. Discussion and conclusion: In conclusion, our results point toward a maturation in UL movement characteristics up to age 11-12y, when UL motion seemed to reach a plateau. The reference values provided in this study will help to further optimize the interpretation of UL deficits in children with neurodevelopmental disorders. [ABSTRACT FROM AUTHOR]