Motion Estimations based on Invariant Moments for Frames Interpolation in Stereovision

Favorskaya, Margarita N.; Pyankov, Dmitriy; Popov, A. A.

doi:10.1016/j.procs.2013.09.196

Cited by 15 publications

(6 citation statements)

References 14 publications

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“…After the region of the light spot is found by the program, the center point of the light spot region is displaced to overlap the CCD image center, and the magnification of the CCD is increased to coincide with the size of the light spot region. The light spot region is thus separated, and the first search is completed [16][17][18]. This system uses a convolutional neural network to search for the laser spot region.…”

Section: Resultsmentioning

confidence: 99%

The Application of Deep Learning and Image Processing Technology in Laser Positioning

et al. 2018

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In this study, machine vision technology was used to precisely position the highest energy of the laser spot to facilitate the subsequent joining of product workpieces in a laser welding machine. The displacement stage could place workpieces into the superposition area and allow the parts to be joined. With deep learning and a convolutional neural network training program, the system could enhance the accuracy of the positioning and enhance the efficiency of the machine work. A bi-analytic deep learning localization method was proposed in this study. A camera was used for real-time monitoring. The first step was to use a convolutional neural network to perform a large-scale preliminary search and locate the laser light spot region. The second step was to increase the optical magnification of the camera, re-image the spot area, and then use template matching to perform high-precision repositioning. According to the aspect ratio of the search result area, the integrity parameters of the target spot were determined. The centroid calculation was performed in the complete laser spot. If the target was an incomplete laser spot, the operation of invariant moments would be performed. Based on the result, the precise position of the highest energy of the laser spot could be obtained from the incomplete laser spot image. The amount of displacement could be calculated by overlapping the highest energy of the laser spot and the center of the image.

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Section: Resultsmentioning

confidence: 99%

The Application of Deep Learning and Image Processing Technology in Laser Positioning

et al. 2018

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“…If an image describing by 2D discrete intensity function I(x, y) with non-zero values in the finite part of XOY plane then the geometrical moments of all orders (p+q) are presented by Equation 4, Favorskaya et al (2013): …”

Section: Applying the Invariant Momentsmentioning

confidence: 99%

“…It is known that seven non -linear functions ϕ 1 -ϕ 7 of normalized invariant Hu moments Favorskaya et al (2013) 2 11 30 12 21 03 2 2 7 21 03 30 12 30 12 21 03 2 2 30 12 21 03 30 12 21 03 ) ]…”

Section: Applying the Invariant Momentsmentioning

confidence: 99%

Detection of a Moving Car Based on Invariant Moments

Rahma¹,

Basil²

2018

Journal of Computer Science

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This paper presents a proposed technique based on Invariant Moments to detect moving cars in front of Autonomous Cars in order to increase safety and reduce road accidents, thus saving lives, which are one of the most important matters in the context of Autonomous Cars. Object detection from a sequence of images is preparatory step but a crucial mission for computer vision applications in information extraction. In this work, a car database was constructed and used in background subtraction to define Region of Interest, then applying the invariant moments for RoI and dependence on the information of the database that was constructed to detect and recognize the moving car. The technique was tested and the experimental results showed that it could detect the moving objects successfully with detection rate over 87% and precision 97% and FoM 91%.

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“…In HEVC, a 8-tap or 7-tap interpolation filter is adopted for fractional motion estimation [3].The accuracy for luma interpolation is 1/4 pixel. It improves the precision compared with the 6-tap interpolation filter in H.264 [4], but it will cost more area in the hardware implementation.…”

Section: Introductionmentioning

confidence: 99%

“…Finally, Section 5 concludes this paper. In the latest video coding standard HEVC, a 8-tap and 7-tap interpolation filter is proposed as shown in equation (1)- (3). According to which fractional pixel should be computed, one interpolation filter is chosen.…”

Section: Introductionmentioning

confidence: 99%

An efficient interpolation filter VLSI architecture for HEVC standard

Lian

Zhou

Duan

et al. 2014

2014 IEEE China Summit &Amp; International Conference on Signal and Information Processing (ChinaSIP)

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The emerging video coding standard, High Efficient Video Coding (HEVC), aims at doubling coding efficiency of H.264/AVC. Fractional Motion Estimation (FME) in HEVC presents a significant challenge in clock latency and area cost as it consumes more than 40% of the total encoding time and its high computational complexity. Firstly, this paper proposes a fast interpolation filter algorithm, which is based on the 8-pixel interpolation unit. It can save 9.9% processing time on average while introducing only 0.1256% BD_PSNR coding quality degradation. Based on this, the paper designs and realizes the interpolation filter VLSI architecture with the reconfigurable configuration and the cell block reuse to reduce the implement hardware area. The final VLSI implementation only requires 64.5k gates in a standard 90nm CMOS technology at an operating frequency of 193MHz. The proposed architecture can be reused for halfpixel interpolation and quarter-pixel interpolation, which can reduce the area cost for about 131040 bits RAM. The processing latency of our proposed VLSI architecture can support the real-time processing of 4:2:0 format 3840×2160@47fps video sequences.

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Motion Estimations based on Invariant Moments for Frames Interpolation in Stereovision

Cited by 15 publications

References 14 publications

The Application of Deep Learning and Image Processing Technology in Laser Positioning

The Application of Deep Learning and Image Processing Technology in Laser Positioning

Detection of a Moving Car Based on Invariant Moments

An efficient interpolation filter VLSI architecture for HEVC standard

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