Sobel edge detection processor for a real-time volume rendering system

Kazakova, N.; Margala, Martin; Durdle, N.G.

doi:10.1109/iscas.2004.1329421

Cited by 41 publications

(21 citation statements)

References 9 publications

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“…The Sobel operator performs a 2-D spatial gradient measurement on an image and so emphasizes regions of high spatial frequency that correspond to edges [17][18][19] . It is based on convolving the image with a small, separable and integer-valued filter in horizontal and vertical directions, and is therefore relatively inexpensive in terms of computations.…”

Section: Improved Sobel Kernelsmentioning

confidence: 99%

A Real-Time Range Finding System with Binocular Stereo Vision

Lai

Hai-shun

2012

International Journal of Advanced Robotic Systems

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To acquire range information for mobile robots, a TMS320DM642 DSP-based range finding system with binocular stereo vision is proposed. Firstly, paired images of the target are captured and a Gaussian filter, as well as improved Sobel kernels, are achieved. Secondly, a feature-based local stereo matching algorithm is performed so that the space location of the target can be determined. Finally, in order to improve the reliability and robustness of the stereo matching algorithm under complex conditions, the confidence filter and the left-right consistency filter are investigated to eliminate the mismatching points. In addition, the range finding algorithm is implemented in the DSP/BIOS operating system to gain real-time control. Experimental results show that the average accuracy of range finding is more than 99% for measuring single-point distances equal to 120cm in the simple scenario and the algorithm takes about 39ms for ranging a time in a complex scenario. The effectivity, as well as the feasibility, of the proposed range finding system are verified.

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Section: Improved Sobel Kernelsmentioning

confidence: 99%

A Real-Time Range Finding System with Binocular Stereo Vision

Lai

Hai-shun

2012

International Journal of Advanced Robotic Systems

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“…Sobel operator [28,29] is an edge detection approach, which utilizes the kernels to detect the edge directions: horizontal, vertical, and diagonal. In Chang and Lin's article, let a, b, c, d, e, f, g, and h be the eight neighboring pixels of an input pixel y of an image.…”

Section: Comparison Resultsmentioning

confidence: 99%

On the pinned field image binarization for signature generation in image ownership verification method

Lee

Chang

2011

EURASIP J. Adv. Signal Process.

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The issue of pinned field image binarization for signature generation in the ownership verification of the protected image is investigated. The pinned field explores the texture information of the protected image and can be employed to enhance the watermark robustness. In the proposed method, four optimization schemes are utilized to determine the threshold values for transforming the pinned field into a binary feature image, which is then utilized to generate an effective signature image. Experimental results show that the utilization of optimization schemes can significantly improve the signature robustness from the previous method (Lee and Chang, Opt. Eng. 49(9), 097005, 2010). While considering both the watermark retrieval rate and the computation speed, the genetic algorithm is strongly recommended. In addition, compared with Chang and Lin's scheme (J. Syst. Softw. 81 (7), [1118][1119][1120][1121][1122][1123][1124][1125][1126][1127][1128][1129] 2008), the proposed scheme also has better performance.

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“…The detection sub-system has three major phases: edge detection, training (re-training) and object detection. The edge detection phase uses the Sobel edge [19], which calculates the gradient of the image intensity at each point. The training phase is activated when the system is powered on for the first time.…”

Section: A Monitoringmentioning

confidence: 99%

Interoperable services based on activity monitoring in Ambient Assisted Living environments

Appiah

Hunter

Vitiello

2014

2014 IEEE Symposium on Intelligent Agents (IA)

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Abstract-Ambient Assisted Living (AAL) is considered as the main technological solution that will enable the aged and people in recovery to maintain their independence and a consequent high quality of life for a longer period of time than would otherwise be the case. This goal is achieved by monitoring human's activities and deploying the appropriate collection of services to set environmental features and satisfy user preferences in a given context. However, both human monitoring and services deployment are particularly hard to accomplish due to the uncertainty and ambiguity characterising human actions, and heterogeneity of hardware devices composed in an AAL system. This research addresses both the aforementioned challenges by introducing 1) an innovative system, based on Self Organising Feature Map (SOFM), for automatically classifying the resting location of a moving object in an indoor environment and 2) a strategy able to generate context-aware based Fuzzy Markup Language (FML) services in order to maximize the users' comfort and hardware interoperability level. The overall system runs on a distributed embedded platform with a specialised ceilingmounted video sensor for intelligent activity monitoring. The system has the ability to learn resting locations, to measure overall activity levels, to detect specific events such as potential falls and to deploy the right sequence of fuzzy services modelled through FML for supporting people in that particular context. Experimental results show less than 20% classification error in monitoring human activities and providing the right set of services, showing the robustness of our approach over others in literature with minimal power consumption.

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Sobel edge detection processor for a real-time volume rendering system

Cited by 41 publications

References 9 publications

A Real-Time Range Finding System with Binocular Stereo Vision

A Real-Time Range Finding System with Binocular Stereo Vision

On the pinned field image binarization for signature generation in image ownership verification method

Interoperable services based on activity monitoring in Ambient Assisted Living environments

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