A maximally stable extremal regions system-on-chip for real-time visual surveillance

Salahat, Ehab; Saleh, Hani; Sluzek, Andrzej; Al‐Qutayri, Mahmoud; Mohammad, Baker; Ismail, Mohammad

doi:10.1109/iecon.2015.7392528

Cited by 18 publications

(11 citation statements)

References 9 publications

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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%

Recent advances in features extraction and description algorithms: A comprehensive survey

Salahat

Qasaimeh

2017

2017 IEEE International Conference on Industrial Technology (ICIT)

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109

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Abstract-Computer vision is one of the most active research fields in information technology today. Giving machines and robots the ability to see and comprehend the surrounding world at the speed of sight creates endless potential applications and opportunities. Feature detection and description algorithms can be indeed considered as the retina of the eyes of such machines and robots. However, these algorithms are typically computationally intensive, which prevents them from achieving the speed of sight real-time performance. In addition, they differ in their capabilities and some may favor and work better given a specific type of input compared to others. As such, it is essential to compactly report their pros and cons as well as their performances and recent advances. This paper is dedicated to provide a comprehensive overview on the state-of-the-art and recent advances in feature detection and description algorithms. Specifically, it starts by overviewing fundamental concepts. It then compares, reports and discusses their performance and capabilities. The Maximally Stable Extremal Regions algorithm and the Scale Invariant Feature Transform algorithms, being two of the best of their type, are selected to report their recent algorithmic derivatives.

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Section: A Mser Derivativesmentioning

confidence: 99%

Recent advances in features extraction and description algorithms: A comprehensive survey

Salahat

Qasaimeh

2017

2017 IEEE International Conference on Industrial Technology (ICIT)

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109

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“…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%

“…State of the art FPGA implementation has real-time performance, but only for images with spatial resolution up to 350 × 350 pixels [9]. The recent ASIC implementation has better performance, but it is designed to work on a higher clock rate [10]. Those two implementations have similar speed performance if comparison is based on the number of operations per clock cycle.…”

Section: Introductionmentioning

confidence: 99%

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A method for real-time memory efficient implementation of blob detection in large images

Petrović¹,

Popovic-Bozovic²

2017

Serb J Electr Eng

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In this paper we propose a method for real-time blob detection in large images with low memory cost. The method is suitable for implementation on the specialized parallel hardware such as multi-core platforms, FPGA and ASIC. It uses parallelism to speed-up the blob detection. The input image is divided into blocks of equal sizes to which the maximally stable extremal regions (MSER) blob detector is applied in parallel. We propose the usage of multiresolution analysis for detection of large blobs which are not detected by processing the small blocks. This method can find its place in many applications such as medical imaging, text recognition, as well as video surveillance or wide area motion imagery (WAMI). We explored the possibilities of usage of detected blobs in the feature-based image alignment as well. When large images are processed, our approach is 10 to over 20 times more memory efficient than the state of the art hardware implementation of the MSER.

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“…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%

Preliminary Investigation of the Robustness of Maximally Stable Extremal Regions (Mser) Model for the Automatic Registration of Overlapping Images

Ajayi

Nwadialor

Onuigbo

et al. 2018

Geoplanning: Journal of Geomatics and Planning

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Various researchers in Digital Image processing have developed keen interest in the automation of object detection, description and extraction process used for various applications and this has led to the development of series of Feature detection and extraction models one of which is the Maximally Stable Extremal Regions Feature Algorithm (MSER). This paper investigates the robustness of MSER algorithm (a blob-like and affine-invariant feature detector) for the detection and extraction of corresponding features used for the automatic registration of series of overlapping images. The robustness investigation was carried out in three different registration campaigns using overlapping images extracted from google earth and image pair acquired from an Unmanned Aerial Vehicle (UAV). Sum of Square Difference (SSD) and Bilinear interpolation models were used to establish the similarity measure between the images to be registered, resampling of the pixel-values and computation of non-integer coordinates respectively while Random Sampling Consensus (RANSAC) algorithm was used to exclude the outliers and to compute the transformation matrix using affine transformation function. The results obtained from this preliminary investigation shows that the processing speed of MSER is quite high for Automatic Image Registration with a relatively high accuracy. While an accuracy of 61.54% was obtained from the first campaign with a processing time of 11.92 seconds, the second campaign gave an accuracy of 52.02% with a processing time of 11.20 seconds and the third campaign produced an accuracy of 55.62% with a processing time of 3.27 seconds. The obtained speed and accuracy shows that MSER is a very robust model and as such, can be deployed as the feature detection and extraction model in the development of an automatic image registration scheme.

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A maximally stable extremal regions system-on-chip for real-time visual surveillance

Cited by 18 publications

References 9 publications

Recent advances in features extraction and description algorithms: A comprehensive survey

Recent advances in features extraction and description algorithms: A comprehensive survey

A method for real-time memory efficient implementation of blob detection in large images

Preliminary Investigation of the Robustness of Maximally Stable Extremal Regions (Mser) Model for the Automatic Registration of Overlapping Images

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