Color-to-grayscale algorithms effect on edge detection — A comparative study

Ahmad, Ijaz; Moon, Inkyu; Shin, Seok Joo

doi:10.23919/elinfocom.2018.8330719

Cited by 13 publications

(10 citation statements)

References 2 publications

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“…(where a denotes the middle coordinate of F , and P(x, y) = 0 for coordinates outside the image), we gain entries of R that -in a geometric interpretation -represent the dot product and hence the similarity between the filter and the corresponding region of the image. In grayscale images, we can, e.g., detect edges using the so-called Prewitt operator [2], which chooses two filter matrices F ∆ H and F ∆ V of dimension 3 × 3, so that the entries in R correspond to the similarity of the respective region of an image with a sharp horizontal (F ∆ H ) or vertical (F ∆ V ) edge; the higher the value of an entry, the higher the resemblance of the region with an edge in the direction of the passing of the filter. Thus, the left edge of a highlycontrasting vertical line as in our example scenario can be found by keeping track of the highest response of convolutions within our scanning region with F ∆ V , and the right edge using a convolution with −F ∆ V .…”

Section: R(x Y)mentioning

confidence: 99%

Towards Executing Computer Vision Functionality on Programmable Network Devices

Glebke

Krude

Kunze

et al. 2019

Proceedings of the 1st ACM CoNEXT Workshop on Emerging in-Network Computing Paradigms

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By offering the possibility to already perform processing as packets traverse the network, programmable data planes open up new perspectives for applications suffering from strict latency and high bandwidth requirements. Real-time Computer Vision (CV), with its high data rates and often mission-and safety-critical roles in the control of autonomous vehicles and industrial machinery, could particularly benefit from executing parts of its logic within network elements.In this paper, we thus explore what it takes to bring CV to the network. We present our work-in-progress efforts of implementing a line-following algorithm based on convolution filters on a P4-programmable NIC. We find that by appropriately identifying regions of interest in the image data and by diligently distributing the necessary calculations among the various match/action stages of the ingress-and egress pipelines of the NIC, our prototypical implementation can achieve over 19 decisions per second on 640x480 px grayscale images with filters large enough to guide a small autonomous car through various courses. CCS CONCEPTS• Networks → In-network processing; Middle boxes / network appliances; Programmable networks; • Computing methodologies → Computer vision;

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Section: R(x Y)mentioning

confidence: 99%

Towards Executing Computer Vision Functionality on Programmable Network Devices

Glebke

Krude

Kunze

et al. 2019

Proceedings of the 1st ACM CoNEXT Workshop on Emerging in-Network Computing Paradigms

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“…While the contrast in (c) is enhanced by using improved grayscale processing, and the interference conductors near the insulator string are also removed, which is advantageous for the subsequent segmentation and extraction of the insulator string. In this paper, five field insulator string images (1)-(5) in different environments are selected, as shown in Table 1, using grayscale [18], H-component [19], R-B [20] and the R + G improved grayscale algorithm respectively.…”

Section: Insulator String Identification a Image Pre-processingmentioning

confidence: 99%

Technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection

et al. 2019

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Finding out the wind deviation fault of the insulators of transmission lines in time can not only help people better complete the wind bias warning and prevention work, but also reduce the economic loss and the maintenance difficulty. A technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection is proposed in this paper. It can calculate the actual wind deviation angle and distance of the insulator through image processing technology and camera calibration. Firstly, the video monitoring device is installed on the tower to collect the field insulator images, and the R + G improved grayscale processing and median filtering are carried out. Secondly, two-dimensional Otsu threshold segmentation is applied to the pre-processed image, and the insulator string target is obtained by combining morphological filtering and connection domain extraction. Then, the improved Kirsch operator is used to obtain the complete single-pixel wide edge of the insulator string. Searching the left and right boundary points of each insulator separately and the center points of each insulator can be calculated. Finally, these center points are transformed into world coordinates by camera calibration and fitted by least square method (LSM). The deviation angle θ of the insulator string can be obtained according to the slope of the fitting line, and the deviation distance d according to the length of the insulator string. When θ is greater than 7.5 • and d is greater than 300mm, an alarm is issued to alert the staff. We analyze the performance of the technology by a series of experiments, the maximum error between the proposed method and the manual method is 8.72% and the minimum is 1.01%. Moreover, by analyzing 200 field insulator images, the identification accuracy are 93.75% and 91.36% respectively, which shows that the proposed method is effective and practical. It provides a new idea for the wind deviation monitoring of suspension insulator strings. INDEX TERMS Insulator wind deviation, improved gray processing, improved Kirsch operator, camera calibration, deviation angle and distance.

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“…The downside is the partial loss of information associated with the color to grayscale conversion, as each pixel which is now represented by just one dimension in grayscale only. 11 Several options exist for converting images from color to grayscale, [12][13][14][15] the simplest of which are those based on weighted averages of the red, green, and blue channels. In addition, certain methods employ different approaches to produce a more perceptually uniform grayscale representation that corresponds to human perception 16 and preserve appealing color contrast information in grayscale images.…”

Section: Introductionmentioning

confidence: 99%

“…19 Research has also shown that different methods to convert color images to grayscale and visual enhancement significantly affects the performance of edge detection algorithms. 11 Although the visual microphone has been successful in recovering sound, 2,5,[23][24][25][26][27][28][29] the choice of the most suitable color to grayscale conversion and visual enhancement for extracting local motion and subsequently recovering sound with the visual microphone has not been explored. This work aims to study the influence that various algorithms for converting color to grayscale and performing visual enhancement have on the recovered sound intelligibility using the visual microphone.…”

Section: Introductionmentioning

confidence: 99%

Impact of visual enhancement and color conversion algorithms on remote sound recovery from silent videos

Choong,

Yap,

Hum

et al. 2024

J Soc Info Display

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The visual microphone is a technique for remote sound recovery that extracts sound information from tiny pixel‐scale vibrations in a video. Despite having demonstrated success in sound recovery, the impact of various visual enhancement and color conversion algorithms applied on the video before the sound recovery process has not been explored. Thus, it is important to investigate these effects have on the recovered sound quality, as the vibrations are so small the effects play an important role. This work experimented with different color to grayscale conversions and visual enhancement algorithms on 576 videos, and found that the recovered sound quality is indeed greatly affected by the choice of algorithms. The best conversion algorithms were found to be the average of the red, green and blue color channels and the perceptual lightness in the CIELAB color space, improving the recovered sound quality by up to 23.22%. Furthermore, visual enhancement techniques such as gamma correction have been found to corrupt vibration information, leading to a 22.47% drop in recovered sound quality in one of the tested videos. Therefore, it is advisable to avoid or minimize the use of visual enhancement techniques for remote sound recovery to prevent the elimination of useful subtle vibrations.

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Color-to-grayscale algorithms effect on edge detection — A comparative study

Cited by 13 publications

References 2 publications

Towards Executing Computer Vision Functionality on Programmable Network Devices

Towards Executing Computer Vision Functionality on Programmable Network Devices

Technology of Actual Wind Deviation Monitoring for Suspension Insulator Strings Based on Improved Edge Detection

Impact of visual enhancement and color conversion algorithms on remote sound recovery from silent videos

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