Corner Detection and Curve Partitioning Using Arc-Chord Distance

Marji, Majed; Klette, Reinhard; Siy, P.

doi:10.1007/978-3-540-30503-3_37

Cited by 7 publications

(4 citation statements)

References 11 publications

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“…[11] uses both arc and ruler to measure the edge to find the local maximum. Marji et al [12] improve the [11]'s method by checking both local maxima and minima. Those methods lack of global views.…”

Section: Related Workmentioning

confidence: 99%

A non-parametric statistics based method for generic curve partition and classification

Gao

2010

2010 IEEE International Conference on Image Processing

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Generic shape feature extraction is a challenging task for image and video content analysis. We present a nonparametric statistics based method for extracting generic shape tokens based on a Perceptual Curve Partition and Grouping (PCPG) model. In this PCPG model, each curve is made up of Generic Edge Tokens (GET) connected at Curve Partitioning Points (CPP). The types of GET and CPP provide a set of basic shape descriptors for semantic vocabulary. The new implementation of the PCPG is based on: 1) An arctangent space is employed to signify the evidence of CPPs at pixel-level. 2) The pixels' sequential order is taken as heuristic to establish a bin order preserving arctangent histogram for locating CPPs by examining the continuity of generic feature criteria statistically. 3) A new CPP detection scheme is capable of detecting CPPs and classifying GETs on the fly. Experiments are presented for performance demonstration.

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Section: Related Workmentioning

confidence: 99%

A non-parametric statistics based method for generic curve partition and classification

Gao

2010

2010 IEEE International Conference on Image Processing

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“…Wu developed this further by making the value of k at each point depend on what its value had been for its preceding neighbour [248], surmising that the frequency of variation in a curve was likely to be similar at closely located points. Meanwhile, Marji et al adjusted Rosenfeld and Johnston's algorithm in order to make the results less dependent (but not independent) of the value of k supplied [160]. For this work, the Teh/Chin/Wu approach was chosen, as it results in a single output curve, whilst being scale independent.…”

Section: Corner Detectionmentioning

confidence: 99%

40 Gb/s wavelength division multiplexing-passive optical network (WDM-PON) for undersea wireless optical communication

Rashid

Sikder

Sadik

et al. 2021

Journal of Optical Communications

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We demonstrate the design and simulation of a long range four-channel wavelength division multiplexing-passive optical network (WDM-PON) operating at 40 Gb/s (4 × 10 Gb/s) in downstream transmission for undersea wireless optical communication. The proposed model consists of two separate subsections between the optical transmitter and receiver. The first subsection consists of the central office and 50 km long feeder fiber for transmitting the optical signal from a distant base station to the 1 × 4 optical demultiplexer located at the sea shore. The second subsection comprises 500 m distributed fiber from the 1 × 4 optical demultiplexer to the terminal point placed at the bottom of the sea and 15–20 m optical wireless channel inside sea water. Simulation results confirmed successful transmission of optical signals from the 50 km distant optical transmitter to the optical receiver located maximum 15 m inside the sea water with a signal-to-noise ratio of ∼20.96 dB, bit error rate of ∼1.55 × 10−8, and quality factor of ∼5.584. The eye diagram at the receiving end also exemplifies quality downstream data transmission at a rate of 10 Gb/s per channel. In addition, we compare the simulation results of the four-channel 40 Gb/s WDM-PON system with a four-channel WDM-PON system operating at 4 Gb/s (4 × 1 Gb/s). Simulation results confirm maximum reach of 17.5 m inside sea water at a cost of significant reduction in data rate. Furthermore, we analyze the system availability of the proposed WDM-PONs and find convincing results for high-speed secured data transmission under water.

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“…2) The chord-to-point distance accumulation [3] The chord-to-point distance accumulation measure belongs to a category of curvature measures which is loosely called Arc-Chord Distance. For a survey of the measures, see [16].…”

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confidence: 99%

On the Stability of Refined <i>L</i>-Curvature under Rotation Transformations

Zhong¹

2010

AMM

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Estimation of curvature on digital curves plays a critical role for various applications in the field of image processing and computer vision. The refined L-curvature has proven to be an efficient measure of discrete curvature for corner detection and curve matching. In this paper its stability under rotation transformations is evaluated. Experiments with comparisons to other existing curvature measures show that the refined L-curvature performs best.

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Corner Detection and Curve Partitioning Using Arc-Chord Distance

Cited by 7 publications

References 11 publications

A non-parametric statistics based method for generic curve partition and classification

A non-parametric statistics based method for generic curve partition and classification

40 Gb/s wavelength division multiplexing-passive optical network (WDM-PON) for undersea wireless optical communication

On the Stability of Refined <i>L</i>-Curvature under Rotation Transformations

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