IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society 2015
DOI: 10.1109/iecon.2015.7392528
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A maximally stable extremal regions system-on-chip for real-time visual surveillance

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Cited by 18 publications
(11 citation statements)
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“…(4) The Parallel MSER Algorithm: One of the major drawbacks of the MSER algorithm is the need to run it twice on every frame to detect both dark and bright extremal regions. To circumvent on these issues, the authors proposed a parallel MSER algorithm [25]. Parallel in this context refers to the capability of detecting both extremal regions in a single run.…”
Section: A Mser Derivativesmentioning
confidence: 99%
“…(4) The Parallel MSER Algorithm: One of the major drawbacks of the MSER algorithm is the need to run it twice on every frame to detect both dark and bright extremal regions. To circumvent on these issues, the authors proposed a parallel MSER algorithm [25]. Parallel in this context refers to the capability of detecting both extremal regions in a single run.…”
Section: A Mser Derivativesmentioning
confidence: 99%
“…We summarize our estimations in Table 1 and compare them to the state of the art FPGA and ASIC implementations from [9] and [10]. The results for the squared image of 1536×1536 pixels are calculated in the case when there is no overlapping.…”
Section: Performance Analysismentioning
confidence: 99%
“…However, there is still significantly large reduction of both performance parameters comparing to the referenced implementations. Additional comparison with the implementations from [9] and [10] are shown in the Figs. 7 and 8.…”
Section: Performance Analysismentioning
confidence: 99%
See 1 more Smart Citation
“…An MSER is a stable connected component of some gray-level sets of the image and it is based on the idea of extracting regions which stay nearly the same through a wide range of thresholds. While MSER has been widely and successfully applied in different image processing applications (Mikolajczyk et al, 2005) (Fraundorfer & Bischof, 2005) some of which include tracking and 3D segmentation (Donoser & Bischof, 2006), retrieval or restoration of images (Nister & Stewenius, 2006), matching of wide baselines (Matas et al, 2004) and curvilinear structures (Lemaitre et al, 2011), object recognition (Obdrzalek & Matas, 2002), real-time visual surveillance (Salahat et al, 2015), Field Programmable Gate Array-FPGA (Kristensen & MacLean, 2007), cell detection and analysis (Kaakinen et al, 2013), etc, research efforts aimed at implementing it for mosaic generation or automatic image registration is relatively unknown. This paper presents some preliminary findings of the investigation of the robustness of MSER for the automatic registration of overlapping image pairs using images acquired from Trimble Ux-5 Unmanned Aerial Vehicles (UAV) and google earth online image data repository.…”
Section: Introductionmentioning
confidence: 99%