Block-based Algorithm Research Articles

PARALLEL DEQUE-FREE PARTITION DUAL-DEQUE MERGE SORTING ALGORITHM

Quicksort is a well-known algorithm used to solve scientific problems. It divides an input array into smaller subarrays and sorts them. However, this algorithm sorts the input data using only one thread. Thus, the algorithm can be implemented to execute in parallel to improve the sorting performance. The study developed a parallel sorting algorithm that uses a block-based concept to partition data and optimized this parallel sorting algorithm using the OpenMP scheduling type. In this study, the parallel sorting algorithm termed the parallel deque-free partition dual-deque merge sorting algorithm is proposed and executed on shared memory architecture. Deque-free partitioning (DFPar) is a parallel block-based partitioning algorithm that partitions an array in blocks without addition operations, and dual-deque merge is a merging algorithm that merges blocks of data into the correct position and sorting steps. This parallel sorting algorithm is implemented using an application programing interface that supports parallel programing on shared memory architecture named OpenMP to improve performance. In this experiment, an Intel Core i7 machine running the Ubuntu Linux operating system was used. Results show that DFPDMSort is faster than benchmarks such as the multi-way merge sort algorithm on a large data size. The performance of the Deque-Free Partitioning step is optimized by the OpenMP scheduling type and its chunk size. Moreover, the parameters of this algorithm, such as block size and cutoff size, can be used as guidelines to improve the performance of other parallel block-based algorithms. These parameters affect cache and branch load misses of the algorithm.

CF Model: A Coarse-to-Fine Model Based on Two-Level Local Search for Image Copy-Move Forgery Detection

Copy-move forgery is the most predominant forgery technique in the field of digital image forgery. Block-based and interest-based are currently the two mainstream categories for copy-move forgery detection methods. However, block-based algorithm lacks the ability to resist affine transformation attacks, and interest point-based algorithm is limited to accurately locate the tampered region. To tackle these challenges, a coarse-to-fine model (CFM) is proposed. By extracting features, affine transformation matrix and detecting forgery regions, the localization of tampered areas from sparse to precise is realized. Specifically, in order to further exactly extract the forged regions and improve performance of the model, a two-level local search algorithm is designed in the refinement stage. In the first level, the image blocks are used as search units for feature matching, and the second level is to refine the edge of the region at pixel level. The method maintains a good balance between the complexity and effectiveness of forgery detection, and the experimental results show that it has a better detection effect than the traditional interest-based copy and move forgery detection method. In addition, CFM method has high robustness on postprocessing operations, such as scaling, rotation, noise, and JPEG compression.

Block Matching Algorithms for the Estimation of Motion in Image Sequences: Analysis

Several video coding standards and techniques have been introduced for multimedia applications, particularly the h.26x series for video processing. These standards employ motion estimation processing to reduce the amount of data that is required to store or transmit the video. The motion estimation process is an inextricable part of the video coding as it removes the temporal redundancy between successive frames of video sequences. This paper is about these motion estimation algorithms, their search procedures, complexity, advantages, and limitations. A survey of motion estimation algorithms including full search, many fast, and fast full search block-based algorithms has been presented. An evaluation of up-to-date motion estimation algorithms, based on several empirical results on several test video sequences, is presented as well.

Forward and reverse logistics vehicle routing problems with time horizons in B2C e-commerce logistics

This research addresses a vehicle routing problem with simultaneous pickup and delivery with time windows from multiple depots (MVRPSPDTW) over a time horizon in the B2C e-commerce logistics system. We consider an e-commerce logistics system with a multi-period, which consists of customers, logistics service providers (LSPs), suppliers, and a decision-making platform. A mixed-integer non-linear programming (MINLP) model is developed and tested on small- and large-scale instances. To handle more realistic large-scale problems, we have used two approaches (i) exact optimisation approach using (i.e. CPLEX tool) and metaheuristic algorithms (i.e. Differential Evolutionary Algorithm (DE), Parallel Differential Evolutionary Algorithm (Par-DE), Genetic Algorithm (GA), and Block-based Genetic Algorithm (BBGA)) to minimise the total transportation cost and penalty due to the delay by logistics service providers. The computation experiment is conducted on the real practical scenario data and the comparative result is demonstrated.

Optimized Block-Based Algorithms to Label Connected Components on GPUs

Connected Components Labeling (CCL) is a crucial step of several image processing and computer vision pipelines. Many efficient sequential strategies exist, among which one of the most effective is the use of a block-based mask to drastically cut the number of memory accesses. In the last decade, aided by the fast development of Graphics Processing Units (GPUs), a lot of data parallel CCL algorithms have been proposed along with sequential ones. Applications that entirely run in GPU can benefit from parallel implementations of CCL that allow to avoid expensive memory transfers between host and device. In this paper, two new eight-connectivity CCL algorithms are proposed, namely Block-based Union Find (BUF) and Block-based Komura Equivalence (BKE). These algorithms optimize existing GPU solutions introducing a block-based approach. Extensions for three-dimensional datasets are also discussed. In order to produce a fair comparison with previously proposed alternatives, YACCLAB, a public CCL benchmarking framework, has been extended and made suitable for evaluating also GPU algorithms. Moreover, three-dimensional datasets have been added to its collection. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposals with respect to state-of-the-art, both on 2D and 3D scenarios.

A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to Local Binary Patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.

A Group-Based Buffer Management for SSD

Random writes limit the application of SSDs significantly because of their poor latency and high garbage collection overhead. Traditional page-based and block-based buffer management algorithms cannot achieve both high buffer hit ratio and good destage sequentiality at the same time. In this paper, we propose a hybrid scheme called the group-based buffer management (GBBM). To improve buffer hit ratio and decrease write/erase counts, GBBM divides buffer space into Page Region and Group Region. The frequently accessed data pages are placed at the Page Region, while infrequently accessed random written data are grouped in the Group Region. GBBM has been evaluated extensively through simulations. The write counts of GBBM show an average decrease of 12.7% compared with page-level buffer scheme. Compared with hybrid buffer management such as CBM, GBBM decreases the average write/erase count by 14.3%/12.1%. The write hit ratio of GBBM shows a 4.5% improvement as compared with PAB. The proposed GBBM can significantly reduce the number of write operations while maintaining a relatively high buffer hit ratio.

MLC STT-MRAM-Aware Memory Subsystem for Smart Image Applications

Next-generation memories with high storage capacity, high performance, and low power consumption are being researched due to the ever-growing demand for artificial intelligence and high-definition applications. Among such future memories, a multi-level cell (MLC) spin-transfer magnetic torque random access memory (STT-MRAM) attracts considerable attention as an alternative to static or dynamic random access memories. An MCL STT-MRAM has the advantages of capacity and non-volatility, but the disadvantages of performance, power consumption, and endurance resulting from complicated resistance state transition and detection processes. In particular, such issues are exacerbated in the latest smart image applications employing block-based processing algorithms. In this paper, we propose a memory subsystem that mitigates the MLC STT-MRAM disadvantages in smart image applications. Our main idea is threefold: MLC-aware image buffer composing, block-aware pixel-to-memory mapping, and prediction-aware image-to-buffer allocating techniques that all make multi-step resistance state transition and detection processes less required. Experimental results show that the proposed memory subsystem achieves 24.5% shorter application execution time, and 96.4% lower memory power consumption than the conventional memory subsystems for industrial smart image applications. In addition, our memory subsystem increases the lifetime of MLC STT-MRAMs via 93.8% fewer multi-step resistance state transition processes.

Novel Logarithmic Reference Free Adaptive Signal Enhancers for ECG Analysis of Wireless Cardiac Care Monitoring Systems

In remote cardiac care monitoring applications, electrocardiogram (ECG) signals are contaminated by artifacts during data acquisition and transmission of signals. The removal of the artifacts is an important task for proper diagnosis. In this paper, an attempt has been made to remove the artifacts, especially baseline wander (BW), muscle artifacts (MA), power line interference (PLI), and electrode motion (EM) using a least mean logarithmic squares (LMLS) algorithm. Further to improve the filtering ability and speed up the convergence process, data normalization is applied. The above algorithm can be normalized with reference to maximum data normalization which leads to reduced computational complexity in the denominator. Based on the above algorithms, various adaptive signal enhancers (ASE’s) are developed. To reduce the computational complexity of the signal enhancer, the proposed ASE’s are combined with sign-based algorithms. The proposed ASE’s are tested on real ECG signals obtained from the MIT-BIH database to compare the performance. The simulation results obtained illustrate that the block-based algorithms are better than LMLS in terms of the signal to noise ratio (SNR), excess mean square error, and computational complexity. Among the LMLS variants, the BB-SRNLMLS-based ASE’s have better filtering ability with a reduced number of computations. The improvement of the SNR achieved in the process through the use of BB-SRNLMLS-based ASE’s are calculated as 13.2945 dBs, 12.4589 dBs, 16.4289 dBs, and 13.6423 dBs, respectively, for BW, MA, PLI, and EM artifacts.

An efficient block-based algorithm for hair removal in dermoscopic images

Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 non-overlapped blocks and for each block, white pixels are replaced by locating the highest peak, using a histogram function and a morphological close operation. The proposed algorithm reports a true positive rate (sensitivity) of 97.36 %, a false positive rate (fall-out) of 4.25 %, and a true negative rate (specificity) of 95.75 %. The diagnostic accuracy achieved is recorded at a high level of 95.78 %.

Research of Block-Based Motion Estimation Methods for Video Compression

This work is a review of the block-based algorithms used for motion estimation in video compression. It researches different types of block-based algorithms that range from the simplest named Full Search to the fast adaptive algorithms like Hierarchical Search. The algorithms evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards, such as MPEG-4 Visual and H.264. The work also presents a very brief introduction to the entire flow of video compression.

BNIMS: Block-based Non-iterative Mean-shift Segmentation algorithm for Medical Images


 
 
 This paper proposed a novel Block based Mean Shift Image Segmentation Algorithm to significantly reduce the computation and improve the segmentation accuracy for high resolution Medical Image. One of the challenging tasks in the image analysis and computer vision area is to correctly classify the pixels as there are no crisp borders among entities in an image. In this proposed methodology, it is observed that the computational complexity of the procedure is diminished by combining the pixels of an image of size MXN into non overlapping image blocks of size 3x3 by eliminating the iterative way of the mean shift procedure. This proposed algorithm shrinks the size of the image by one third of its original image for the computational purpose and then equalizes the number of computations for each new image pixel by constructing links between pixels using their first mean-shift vectors without any iteration process. The accurateness and effectiveness of the proposed methodology is matched with the existing Iterative Mean Shift Algorithm by accomplishing the empherical experiments on the Medical Images (Pathologies Buccales and Eye Retina) composed along with the similarity measures.
 
 

Intelligent search techniques for network-based manufacturing systems: multi-objective formulation and solutions

Effective and efficient implementation of intelligent and recently emerged networked manufacturing systems requires enterprise-level integration. The first step in this direction is to integrate the manufacturing functions such as process planning and scheduling for multi-jobs in order to generate optimal or near optimal solutions. Addressed in this paper is multi-objective optimisation in the context of a network-based manufacturing system to optimise multiple objectives, i.e. minimisation of makespan and minimisation of variation of workload, simultaneously. This paper introduces a mathematical model for calculating the above-mentioned objectives with consideration of alternative machines, as well as tools and tool approach directions. The authors propose a new modified block-based genetic algorithm (MBBGA) and modified non-dominated sorting genetic algorithm (MNSGA-II) to resolve the above-mentioned complex problem and compare the proposed algorithms’ performance and their effectiveness with the non-dominated sorting genetic algorithm (NSGA-II). An illustrative example with complex scenarios is carried out to demonstrate the feasibility of the proposed MBBGA and MNSGA-II. The experimental results presented show that the proposed algorithms perform better in comparison with NSGA-II.

Integrated scheduling of rake and stockyard management with ship berthing: a block based evolutionary algorithm

An integrated problem of optimising the operations at a commercial bulk material port terminal is studied in this paper. We simultaneously optimise the stockyard operations and rake schedule for outbound cargo, in conjunction with the arriving vessels and the status of the stockyards at the port. A mixed integer linear programming model for the problem is developed while incorporating the inherent complexities of the integrated model. To solve the real-life instances, two heuristic methods are proposed specifically for the considered problem. Firstly, genetic algorithm coupled with a greedy heuristic and later, block-based evolutionary algorithm (BBEA) is employed. After applying both techniques, we obtain the optimised schedule for loading of rakes and allotment of stockyard space for vessels as well as rakes at the terminal. Finally, we test the results of both models in three traffic scenarios between themselves and with real-life data from a port situated along the Eastern coast of India. The study resulted in significant reduction of turnaround time for rakes at the port terminal, which in turn lead to monetary savings. The model also automates the day to day operational decision-making at the port.

X-ray 영상을 위한 블록 기반 대비 개선 기법

자연 영상들을 위한 전형적인 지역적 대비 개선 기법을 X-ray 영상에 적용할 경우, X-ray 고유의 특성을 고려하지 않기 때문에 과도한 개선이 이루어지거나 부자연스러운 화질을 생성하는 화상의 결함이 발생할 수 있다. 이러한 문제를 해결하기 위해 본 논문은 지역적으로 X-ray 영상을 위한 블록 기반 대비 개선 기법을 제안한다. 먼저 블록 단위로 가중치 누적 분포 변환 함수를 도출하고, 이를 이용한 블록 단위 대비 개선을 수행한다. 다음으로, 블록 기반 중첩을 적용하여 블록화 현상이 제거된 영상을 획득한다. 후처리로 고주파 성분을 강조하여 최종 영상을 획득한다. 모의실험을 통해 제안한 블록 기반 대비 개선기법이 일반적인 대비 개선 기법보다 정량적인 대비 개선 척도에서 최대 5배 우수함을 확인하였다.

Block-based noise variance estimation algorithm in blurred and noisy images with its application to motion deblurring

The present research is organized with a focus on a block-based algorithm to estimate the image noise variance in the blurred images, in order to be applicable in the area of motion deblurring approach. The main problem in estimating the image noise variance through traditional available applications is the loss of real edge lines in the process of creating smooth areas. This complicated point is considered in the present research to be solved through double-side edge lines for the purpose of identifying the potential smooth areas. The blind algorithm presented here finds large rectangles around each end-side of edge lines. This utilizes the statistical measures to estimate the noise variance in the selected areas. It should be noted that the most important difference between the approach presented here and the available literatures, is its usage in case of blind algorithm and also the low computational complexity. It assists us to present a real-time application, in one such case. The results show that the algorithm estimates the noise variances, successfully, based on a number of standard images by developing the motion deblurring approach. Afterwards, in line with the results of the image noise variance estimation, the motion deblurring approach has been designed to retrieve the original version of the blurred and noisy image, which is under the influence of uniform linear motion with constant velocity. In order to consider the effectiveness of the proposed approach, the outcomes regarding some comparison criteria including the peak signal to noise ratio, the structural similarity, the computational complexity and finally the size of processed images have been compared with those obtained from the well-known linear motion approaches, as the potential benchmarks.

Discrete Multiwavelet–Based Video Watermarking Scheme Using SURF

This paper proposes a robust, imperceptible block-based digital video watermarking algorithm that makes use of the Speeded Up Robust Feature (SURF) technique. The SURF technique is used to extract the most important features of a video. A discrete multiwavelet transform (DMWT) domain in conjunction with a discrete cosine transform is used for embedding a watermark into feature blocks. The watermark used is a binary image. The proposed algorithm is further improved for robustness by an error-correction code to protect the watermark against bit errors. The same watermark is embedded temporally for every set of frames of an input video to improve the decoded watermark correlation. Extensive experimental results demonstrate that the proposed DMWT domain video watermarking using SURF features is robust against common image processing attacks, motion JPEG2000 compression, frame averaging, and frame swapping attacks. The quality of a watermarked video under the proposed algorithm is high, demonstrating the imperceptibility of an embedded watermark.

Improved Listless Embedded Block Partitioning Algorithms for Image Compression

In this paper, two simple yet efficient embedded block-based image compression algorithms are presented. These algorithms not only improve the rate distortion performances of set partitioning in hierarchical trees (SPIHT) and set partitioning in embedded block coder (SPECK) at lower bit rates but also reduces the dynamic memory requirement by 91.1% in comparison to SPIHT. The former objective is achieved by better exploiting the coefficient decaying spectrum of the wavelet transformd images and the later objective is realised by improved listless implementation of the algorithms. The proposed algorithms explicitly perform breadth first search like SPECK. Extensive simulation conducted on various standard grayscale and color images indicate significant peak-signal-to-noise-ratio (PSNR) improvement over most of the state-of-the-art wavelet-based embedded coders including JPEG2000 at lower rates. The reduction of encoding and decoding time as well as improvement in coding efficiency at lower bit rates facilitate these coder as better candidates for multimedia applications.

A distributed Canny edge detector: algorithm and FPGA implementation.

The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM READ/WRITE time and the computation time) to detect edges of 512 × 512 images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.

Structured Compressive Channel Estimation for Large-Scale MISO-OFDM Systems

To estimate the increased channel parameters in large-scale multiple-input-single-output (MISO) systems, a structured compressive channel estimation scheme based on preamble signals is proposed. The channel estimation scheme exploits the sparse common support of different channel impulse responses (CIRs), leading to a block-structured compressive sensing model for the MISO system. Using this model, an optimization criteria characterizing the unique block sparsity is formulated, and accordingly a block-based orthogonal matching pursuit algorithm is developed which effectively recovers the channel parameters. Simulation results validate the efficacy of the proposed scheme in estimating many sparse CIRs, showing its performance advantage in the emerging large-scale antenna systems.