Matching Distortion Research Articles

WPMAVM: Weighted plus-and-minus allowance variance minimization algorithm for solving matching distortion

• A weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed. • WPMAVM takes the plus-and-minus allowance in the objective function of VMM into account. • The distance function is introduced to suppress the distortion caused by the abnormal allowance. • WPMAVM has better robustness to minus allowance and abnormal point clouds compared to ICP and VMM. • The proposed algorithm is suitable for calibration and measurement of complex curved surfaces. Point cloud matching is widely used in the calibration and measurement of complex curved surfaces, and the matching accuracy affects both the subsequently machined surface quality and measurement results. In the actual application, the existing iterative closest point (ICP) algorithm is prone to matching distortion when the measurement point clouds have inherent defects such as unclosed shape, uneven density and Gaussian noise . Despite this problem can be solved by the variance-minimization matching (VMM) algorithm, but the point clouds match distortedly both for ICP and VMM particularly when the workpieces behave with complex and unknown allowance distribution. For examples, the car bodywork is characterized by remarkable flexibility with local deformation, resulting in abnormal allowance, while the machined engine blade shows over- and less-cutting induced minus and abnormal allowances in high and low curvature areas, respectively. In this paper, a weighted plus-and-minus allowance variance minimization (WPMAVM) algorithm is proposed to overcome these problems. Specifically, the plus-and-minus allowance in the objective function of VMM algorithm is subdivided into the sum of plus-and-minus allowance variance minimization to suppress the distortion caused by the plus-and-minus allowance. The weighted term w , defined as the distance function from the point to the weighted plus-and-minus mean, is introduced to suppress the distortion caused by the abnormal allowance. The simulation and experimental comparisons on three typical workpieces show that the proposed algorithm has better robustness to minus allowance and abnormal point clouds compared to the existing ICP and VMM algorithms.

Rigid Shape Matching for 3-D Robotic Grinding Measurement With Applications to Blades

Robotic grinding is a promising method to form the surfaces of industrial components, which improves product quality, lowers machining costs, and reduces processing time. Allowance optimization is a surface matching process, which is important to grinding measurement. The grinding efficiency highly depends on the results of the surface matching between measured points and the design model. However, existing matching methods are usually based on distance and variance minimization, ignoring the grinding requirements to different surfaces, which leads to high processing time and unstable grinding quality. In this article, we proposed a novel surface matching method for robotic grinding. The objective function is constructed via time minimization and involving weights for allowance requirements to different surfaces. This method also balances all measured points’ contributions, avoiding matching distortion due to uneven points’ distribution. Experimental results and comparisons verify the effectiveness of our methods. Comparing to existing matching methods, our method is robust to uneven point distribution and has advantages in reducing the grinding time.

A Strategy for the Linear Time Implementation of the SMVQ Algorithm

This paper proposes a linear time implementation of the Side Match Vector Quantization (SMVQ) algorithm. It maps the side match distortion values between codewords and a boundary vector onto an order-preserving integer sequence. Hence the SMVQ algorithm’s state codebook generation step can exploit any integer sorting algorithm to improve its overall runtime complexity. The proposed SMVQ implementations utilize the bucket sort algorithm to demonstrate this concept. The experimental results show a percentage change of −84.26% for N = 1024 (46.052–7.247 s) and −94.82% for N = 4096 (998.355–51.710 s) in the average execution time of the SMVQ algorithm.

Lossy and Lossless Video Frame Compression: A Novel Approach for High-Temporal Video Data Analytics

The smart city concept has attracted high research attention in recent years within diverse application domains, such as crime suspect identification, border security, transportation, aerospace, and so on. Specific focus has been on increased automation using data driven approaches, while leveraging remote sensing and real-time streaming of heterogenous data from various resources, including unmanned aerial vehicles, surveillance cameras, and low-earth-orbit satellites. One of the core challenges in exploitation of such high temporal data streams, specifically videos, is the trade-off between the quality of video streaming and limited transmission bandwidth. An optimal compromise is needed between video quality and subsequently, recognition and understanding and efficient processing of large amounts of video data. This research proposes a novel unified approach to lossy and lossless video frame compression, which is beneficial for the autonomous processing and enhanced representation of high-resolution video data in various domains. The proposed fast block matching motion estimation technique, namely mean predictive block matching, is based on the principle that general motion in any video frame is usually coherent. This coherent nature of the video frames dictates a high probability of a macroblock having the same direction of motion as the macroblocks surrounding it. The technique employs the partial distortion elimination algorithm to condense the exploration time, where partial summation of the matching distortion between the current macroblock and its contender ones will be used, when the matching distortion surpasses the current lowest error. Experimental results demonstrate the superiority of the proposed approach over state-of-the-art techniques, including the four step search, three step search, diamond search, and new three step search.

Unconditional or Conditional Logistic Regression Model for Age-Matched Case-Control Data?

Matching on demographic variables is commonly used in case–control studies to adjust for confounding at the design stage. There is a presumption that matched data need to be analyzed by matched methods. Conditional logistic regression has become a standard for matched case–control data to tackle the sparse data problem. The sparse data problem, however, may not be a concern for loose-matching data when the matching between cases and controls is not unique, and one case can be matched to other controls without substantially changing the association. Data matched on a few demographic variables are clearly loose-matching data, and we hypothesize that unconditional logistic regression is a proper method to perform. To address the hypothesis, we compare unconditional and conditional logistic regression models by precision in estimates and hypothesis testing using simulated matched case–control data. Our results support our hypothesis; however, the unconditional model is not as robust as the conditional model to the matching distortion that the matching process not only makes cases and controls similar for matching variables but also for the exposure status. When the study design involves other complex features or the computational burden is high, matching in loose-matching data can be ignored for negligible loss in testing and estimation if the distributions of matching variables are not extremely different between cases and controls.

Fast motion estimation algorithm using multilevel distortion search in Walsh–Hadamard domain

Block-matching motion estimation (BME) can efficiently reduce the temporal redundancy between the successive video sequences in video compression coding system. In this study, a fast BME algorithm using multilevel distortion search in Walsh–Hadamard domain is proposed to reduce the computational burden and speed up coding process. First, the proposed algorithm divides the block into several sub-blocks. Then, the Walsh–Hadamard transform is applied to these sub-blocks. Finally, the proposed algorithm calculates the partial block matching distortion by utilising a novel back diagonal search scheme which can quickly reject unnecessary candidate block in a multilevel manner. Experimental results show that the proposed algorithm effectively reduces the number of operations in block distortion calculation meanwhile maintains the best motion estimation matching quality. Compared with the full search, the proposed algorithm can reduce 87.19% computational complexity without any degradation of the peak signal to noise ratio. In addition, compared with the partial distortion search algorithm, successive elimination algorithm, multilevel successive elimination algorithm and the transform-domain successive elimination algorithm, the proposed algorithm can also save 68.27, 70.09, 37.81 and 37.44% computational complexity, respectively. Moreover, the proposed algorithm can also be easily incorporated into any block-based template search motion estimation algorithm.

All-layer search algorithm using mean inequality and improved checkerboard partial distortion search for fast motion estimation

Block-matching motion estimation algorithm is used in many video compression coding systems because it could greatly reduce the temporal redundancy between the consequent video sequences. In this paper, an all-layer search algorithm using mean inequality and improved checkerboard partial distortion search scheme for fast block-matching motion estimation is proposed. A layer in the proposed method refers to a processed image which is derived from the reference frame or the adjacent lower layer. Firstly, the proposed algorithm constructs all layers from the reference frame or the adjacent lower layer by summing up all pixels over a sub-block. Then, a new mean inequality elimination method is introduced to reject a lot of unnecessary candidate search points on the top layers before calculating the real block matching distortion. Finally, the proposed algorithm utilizes an improved checkerboard partial distortion search scheme in the process of the real block distortion calculation on the following layers to further reduce the amount of computation. Experimental results show that the proposed algorithm can effectively reduce the computational complexity of motion estimation meanwhile guarantee the matching quality compared to other motion estimation algorithms. Compared to the full search algorithm, the proposed algorithm can reduce 97.30 % computational complexity with a negligible degradation of the peak signal to noise ratio (PSNR). Compared to the diamond search algorithm, directional gradient descent search algorithm, partial distortion search algorithm, transform-domain successive elimination algorithm and two-layer motion estimation algorithm, the proposed algorithm can also save 63.56 %, 52.73 %, 92.87 %, 85.77 % and 33.96 % computational complexity, respectively.

A NOVEL ZERO-WATERMARKING SCHEME FOR TEXT DOCUMENT AUTHENTICATION

The demand for copyright protection of text documents is extremely vital especially when the text is transmitted over the Internet. One of the best practical resolutions to maintain the security of document media is digital watermarking. Several approaches have been proposed to guarantee the safety and protection of the documents against dishonest copying and distribution. This paper suggests an innovative zero-watermarking scheme for the purpose of authentication and tamper detection in simple text documents. The algorithm generates a watermark based on the effective characters of the text contents which can be extracted later using extraction algorithm to identify the status of tampering in the text document. Experimental results demonstrate the effectiveness of the algorithm against random tampering attacks. Watermark pattern matching and watermark distortion rate are used as evaluation parameters on multiple text samples of varying length.

A Novel Boundary Matching Algorithm for Video Temporal Error Concealment

With the fast growth of communication networks, the video data transmission from these networks is extremely vulnerable. Error concealment is a technique to estimate the damaged data by employing the correctly received data at the decoder. In this paper, an efficient boundary matching algorithm for estimating damaged motion vectors (MVs) is proposed. The proposed algorithm performs error concealment for each damaged macro block (MB) according to the list of identified priority of each frame. It then uses a classic boundary matching criterion or the proposed boundary matching criterion adaptively to identify matching distortion in each boundary of candidate MB. Finally, the candidate MV with minimum distortion is selected as an MV of damaged MB and the list of priorities is updated. Experimental results show that the proposed algorithm improves both objective and subjective qualities of reconstructed frames without any significant increase in computational cost. The PSNR for test sequences in some frames is increased about 4.7, 4.5, and 4.4 dB compared to the classic boundary matching, directional boundary matching, and directional temporal boundary matching algorithm, respectively.

Histogram ordering model-based fast motion estimation

This study proposes two efficient block matching algorithms for fast motion estimation using a histogram ordering model in order to reduce the computational cost of video coding. Since the representative pixels that consist of edge or texture mainly contribute to the block matching criterion, it is important to analyse the details of the current block. To analyse the characteristics of the block with low complexity, the pixel histogram is used for the observation of the block. Based on this pixel histogram for the current block, an optimal block matching order is determined for the lossless fast matching algorithm. In addition, adaptive partial block matching algorithm for lossy fast motion estimation using histogram-based block matching ordering is also presented to further reduce the complexity of block matching. By capturing the representative pixels, block matching distortion is maximised soon enough during the block matching. For this reason, partial block matching is available instead of full block matching, which is also known as a full search algorithm. Our experimental results show that the proposed algorithm not only reduces the computational complexity of block matching criterion, but also tends to maintain the image quality when compared to the conventional fast matching algorithms.

Optimization of particle image distortion for PIV measurements

In this study, a jump matching correlation scheme is proposed. For the multi-grid, iterative particle image distortion analysis, the coarse grid solutions are not obtained from coarse pixel image constructed by binning small pixels to large pixel, but by jumping the interrogation window the number of pixels specified during the particle image matching process. With the jump matching scheme applied and particle image interpolated, the central difference particle image pattern matching and image distortion analysis are easily linked together to resolve flow fields from coarsest grid to super-resolution grid. Optimization of particle image distortion is achieved by averaging a forward and a backward image distortion correlation for every iteration process. The proposed method is first verified by standard particle images of impinging jet flow that has exact solution available, and then applied to analyze uniform flow past two side-by-side circular cylinders. Satisfactory results are obtained from the jump matching scheme proposed.

Frame Rate Up-Conversion Using Trilateral Filtering

Frame rate up-conversion (FRUC) can enhance the visual quality of low frame rate video presented on liquid crystal display. To minimize the difference between a reference block and an interpolated block, an effective FRUC algorithm partitions a large block into several sub-blocks of smaller size and estimates their motions. Motion estimation searches for the block which has the minimum difference (cost) with the processed block in terms of some block matching distortion and motion discontinuity. As convexity and convergence of the cost function are not guaranteed, the computational cost for such motion estimation is usually extensive or unpredictable. In our proposed FRUC method, the two predictions of a frame to be interpolated are generated through shifting its nearest neighbor frames in the previous and following directions with the motion vectors estimated between them. The initial interpolated frame and its pixel's reliability are subsequently estimated from these two predictions. We then apply a trilateral filter on the initial prediction to correct the unreliable pixels and to restore the missing pixels. Our proposed method not only reduces the computation for refining motion vectors, but also suppresses the interpolation noises and misregistration errors. We have conducted extensive experiments and the results show that the proposed algorithm outperforms the existing methods with better objective and subjective visual quality, and achieves about 3 dB on-average peak signal-to-noise ratio improvement.

A novel temporal error concealment method based on fuzzy reasoning for H.264

In this paper, a fuzzy reasoning based temporal error concealment method is proposed. The basic temporal error concealment is implemented by estimating Motion Vector (MV) of the lost MacroBlock (MB) from its neighboring MVs. Which MV is the most proper one is evaluated by some criteria. Generally, two criteria are widely used, namely Side Match Distortion (SMD) and Sum of Absolute Difference (SAD) of corresponding MV. However, each criterion could only partly describe the status of lost block. To accomplish the judgement more accurately, the two measures are considered together. Thus a refined measure based on fuzzy reasoning is adopted to balance the effects of SMD and SAD. Terms SMD and SAD are regarded as fuzzy input and the term ‘similarity’ as output to complete fuzzy reasoning. Result of fuzzy reasoning represents how the tested MV is similar to the original one. And k-means clustering technique is performed to define the membership function of input fuzzy sets adaptively. According to the experimental results, the concealment based on new measure achieves better performance.

Frame Error Concealment Using Pixel Correlation in Overlapped Motion Compensation Regions

In low bit-rate video transmission, the payload of a single packet can often contain a whole coded frame due to the high compression ratio in both spatial and temporal domains of most modern video coders. Thus, the loss of a single packet not only causes the loss of a whole frame, but also produces error propagation into subsequent frames. In this paper, we propose a novel whole frame error concealment algorithm which reconstructs the first of the subsequent frames instead of the current lost frame to suppress the effects of error propagation. In the proposed algorithm, we impose a constraint which uses side match distortion (SMD) and overlapped region difference (ORD) to estimate motion vectors between the target reconstructed frame and its reference frame. SMD measures the spatial smoothness connection between a block and its neighboring blocks. ORD is defined as the difference between the correlated pixels which are predicted from one reference pixel. Experimental results show that the proposed algorithm effectively suppresses error propagation and significantly outperforms other conventional techniques in terms of both peak signal-to-noise ratio performance and subjective visual quality.

Efficient tree structured motion estimation using successive elimination

In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorithm (SEA) is implemented in tree structured motion estimation with a simple and effective method to determine the initial motion vector, which exploits the strong correlation among the partially overlapped variable-size blocks. With identical performance to a full search algorithm, computations for block matching can be reduced to 1%–20%. Further, the SEA can be improved by incorporating two early termination conditions, then named ‘Quick SEA’. Finally, a novel fast motion estimation algorithm, successive elimination diamond search (SEDS), is proposed by efficiently integrating the Quick SEA and a modified diamond search pattern. Simulation results show that the proposed Quick SEA can reduce the computational complexity of block matching by 3–5 times compared to the basic SEA. SEDS further reduces by about one-half the computations of Quick SEA. With similar rate distortion performance, 0.2%–1% block matching distortion is calculated for SEDS with corresponding speed-up factors of 100 to 500 in comparison with the full search algorithm.

Neighbourhood-blocks motion vector estimation technique using pyramidal data structure

A pyramidal motion estimation technique that makes use of the motion correlation within a pyramidal level is proposed. In the proposed technique, motion vectors from neighbouring motion blocks are taken into consideration as possible candidates. This is done in lieu of scaling the motion vectors from the corresponding positions at the adjacent lower pyramidal level as the prediction motion vectors for the current pyramidal level (as performed in the conventional technique). Each of these candidate motion vectors is used as the prediction motion vector and refined, and the one that has the least matching distortion is chosen as the motion vector at the current pyramidal level. Compared to the conventional pyramidal motion estimation technique, the proposed method effectively overcomes the problem of propagation of false motion vectors. Simulation studies show that a substantial improvement is achieved in the performance, both in terms of the prediction mean square error and the number of coding bits for the motion vectors.