• Proceedings of the IEEK Conference
• /
• 2000.07a
• /
• pp.278-281
• /
• 2000

Matching pursuit algorithm is a signal expansion technique whose efficiency for motion compensated residual image has already been demonstrated in the MPEG-4 framework. However, one of the practical concerns related to applying matching pursuit algorithm to real-time scalable video coding is its massive computation required for finding dictionary elements. In this respective, this paper proposes a fast algorithm, which is composed of three sub-methods. The first method utilizes the property of symmetry in 1-D dictionary element and the second uses mathematical elimination of inner product calculation in advance, and the last one uses frequency property of 2-D dictionary. Experimental results show that our algorithm needs about 30% computational load compared to the conventional fast algorithm using separable property of 2-D gabor dictionary with negligible quality degradation.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.12
• /
• pp.1926-1931
• /
• 2012

We investigated a novel fast non-uniform illumination correction method for bi-level images. The proposed method divides a bi-level image into sub-images and roughly estimates block-wise illumination by low pass filtered maximum values of sub-images. After that, we apply bilinear interpolation using the block-wise illumination to estimate non-uniform illumination, and compensate for the effect of non-uniform illumination using the estimated illumination. Since the proposed method is not based on computation intensive iterative optimization, the proposed method can be used effectively for applications that require fast correction of non-uniform illumination. In simulations, the proposed method showed more than 20 times faster speed than existing entropy minimization method. Moreover, in simulations and experiments, the restored images by the proposed method were more close to true images than images restored by conventional method.

• Journal of Institute of Control, Robotics and Systems
• /
• v.9 no.8
• /
• pp.640-646
• /
• 2003

We presents an edge-based point correlation algorithm for fast and stable visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties in applying automated inspection systems to real factory environment. First of all, NGC algorithms involve highly complex computation and thus require high performance hardware for realtime process. In addition, lighting condition in realistic factory environments is not stable and therefore intensity variation from uncontrolled lights gives many troubles for applying NGC directly as pattern matching algorithm. We propose an algorithm to solve these problems, using thinned and binarized edge data, which are obtained from the original image. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the computational complexity. Matching edges instead of using original gray-level image pixels overcomes problems in NGC method and pyramid of edges also provides fast and stable processing. All proposed methods are proved by the experiments using real images.

• Journal of Communications and Networks
• /
• v.13 no.5
• /
• pp.449-455
• /
• 2011

Inspired by fast Jacket transforms, we propose simple factorization and construction algorithms for the M-dimensional discrete Fourier transform (DFT) matrices underlying generalized Chinese remainder theorem (CRT) index mappings. Based on successive coprime-order DFT matrices with respect to the CRT with recursive relations, the proposed algorithms are presented with simplicity and clarity on the basis of the yielded sparse matrices. The results indicate that our algorithms compare favorably with the direct-computation approach.

• Nuclear Engineering and Technology
• /
• v.55 no.8
• /
• pp.3071-3079
• /
• 2023

Helium production in the nuclear fuel matrix during irradiation plays a critical role in the design and performance of Gen-IV reactor fuel, as it represents a life-limiting factor for the operation of fuel pins. In this work, a surrogate model for the helium production rate in fast reactor MOX fuels is developed, targeting its inclusion in engineering tools such as fuel performance codes. This surrogate model is based on synthetic datasets obtained via the SCIANTIX burnup module. Such datasets are generated using Latin hypercube sampling to cover the range of input parameters (e.g., fuel initial composition, fission rate density, and irradiation time) and exploiting the low computation requirement of the burnup module itself. The surrogate model is verified against the SCIANTIX burnup module results for helium production with satisfactory performance.

• ETRI Journal
• /
• v.45 no.6
• /
• pp.1035-1045
• /
• 2023

We propose a novel graphics processing unit (GPU) algorithm that can handle a large-scale 3D fast Fourier transform (i.e., 3D-FFT) problem whose data size is larger than the GPU's memory. A 1D FFT-based 3D-FFT computational approach is used to solve the limited device memory issue. Moreover, to reduce the communication overhead between the CPU and GPU, we propose a 3D data-transposition method that converts the target 1D vector into a contiguous memory layout and improves data transfer efficiency. The transposed data are communicated between the host and device memories efficiently through the pinned buffer and multiple streams. We apply our method to various large-scale benchmarks and compare its performance with the state-of-the-art multicore CPU FFT library (i.e., fastest Fourier transform in the West [FFTW]) and a prior GPU-based 3D-FFT algorithm. Our method achieves a higher performance (up to 2.89 times) than FFTW; it yields more performance gaps as the data size increases. The performance of the prior GPU algorithm decreases considerably in massive-scale problems, whereas our method's performance is stable.

• International Journal of Computer Science & Network Security
• /
• v.24 no.1
• /
• pp.45-51
• /
• 2024

The subject of navigation has drawn a large interest in the last few years. The navigation within a city is to find the path between two points, source location and destination location. In many cities, solving the routing problem is very essential as to find the route between different locations (starting location (source) and an ending location (destination)) in a fast and efficient way. This paper considers streets with diagonal streets. Such streets pose a problem in determining the directions of the route to be followed. The paper presents a solution for the path planning using the reconfigurable mesh (R-Mesh). R-Mesh is a parallel platform that has very fast solutions to many problems and can be deployed in moving vehicles and moving robots. This paper presents a solution that is very fast in computing the routes.

• Journal of information and communication convergence engineering
• /
• v.12 no.4
• /
• pp.263-270
• /
• 2014

Corner detection and feature extraction are essential aspects of computer vision problems such as object recognition and tracking. Feature detectors such as Scale Invariant Feature Transform (SIFT) yields high quality features but computationally intensive for use in real-time applications. The Features from Accelerated Segment Test (FAST) detector provides faster feature computation by extracting only corner information in recognising an object. In this paper we have analyzed the efficient object detection algorithms with respect to efficiency, quality and robustness by comparing characteristics of image detectors for corner detector and feature extractors. The simulated result shows that compared to conventional SIFT algorithm, the object recognition system based on the FAST corner detector yields increased speed and low performance degradation. The average time to find keypoints in SIFT method is about 0.116 seconds for extracting 2169 keypoints. Similarly the average time to find corner points was 0.651 seconds for detecting 1714 keypoints in FAST methods at threshold 30. Thus the FAST method detects corner points faster with better quality images for object recognition.

• Journal of IKEEE
• /
• v.17 no.3
• /
• pp.371-377
• /
• 2013

This thesis propose an efficient parallel structure method for implementing a FAST and BRIEF algorithm based Augmented Reality. SURF algorithm that is well known in the object recognition algorithms is robust in object recognition. However, there is a disadvantage for real time operation because, SURF implementation requires a lot of computation. Therefore, we used a FAST and BRIEF algorithm for object recognition, and we improved Conventional Parallel Structure based on OpenMP Library. As a result, it achieves a 70%~100% improvement in execution time on the embedded system.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.32 no.5C
• /
• pp.536-541
• /
• 2007

In video coding standard H.264/AVC, motion estimation using multiple reference pictures improves compression efficiency but the efficiency depends upon image content not the number of reference pictures. So, the motion estimation includes a large amount of computation of no worth according to image. This paper proposes fast motion estimation algorithm that removes worthless computation in the motion estimation using multiple reference pictures. The proposed algorithm classifies a block into valid and invalid blocks for the multiple reference pictures and removes the workless computation by applying a single reference picture to the invalid block. To estimate the proposed algorithm's performance, image quality, bit rate, and motion estimation time are compared with ones of the conventional algorithm in the reference software JM 9.5. The simulation results show that the proposed algorithm can considerably save about 38.67% the averaged motion estimation time while keeping the image quality and the bit rate, whose are average values are -0.02dB and -0.77% respectively, as good as the conventional algorithm.