Efficient unassisted video segmentation using enhanced fast marching

Stec, Piotr; Domański, Marek

doi:10.1109/icip.2003.1246708

Cited by 8 publications

(6 citation statements)

References 12 publications

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“…The algorithm extracts contour line for each character into eight and four directions by using 3×3 and 2×2 masks respectively, and then divides NP region into higher part and lower part using density indicating histogram for y-axis direction. In [84] and [85], a shape-driven active contour model is established, which uses a variation fast matching algorithm for NP character segmentation, where a coarse extracting of boundaries and class labels of each character are proposed using a shape driven fast marching technique with a gradient and curvature dependent speed function:…”

Section: Contours Tracking Algorithmsmentioning

confidence: 99%

“…Where k F denotes a curvature related term in order to keep the propagating curve as smooth as possible [85].…”

Section: Contours Tracking Algorithmsmentioning

confidence: 99%

“…The coarse segmentation results [85] After locating the coarse character boundary, a fine character boundary process is used to locate fine character boundaries and classify them with evolving active contours in the fast marching scheme, which depends on gradient, curvature and shape similarity information.…”

Section: Contours Tracking Algorithmsmentioning

confidence: 99%

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Automatic Number Plate Recognition on FPGA

Zhai

Bensaali

McDonald-Maier

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

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Automatic Number Plate Recognition on FPGA i AbstractIntelligent Transportation Systems (ITSs) play an important role in modern traffic management, which can be divided into intelligent infrastructure systems and intelligent vehicle systems. Automatic Number Plate Recognition systems (ANPRs) are one of infrastructure systems that allow users to track, identify and monitor moving vehicles by automatically extracting their number plates. ANPR is a well proven technology that is widely used throughout the world by both public and commercial organisations. There are a wide variety of commercial uses for the technology that include automatic congestion charge systems, access control and tracing of stolen cars. The fundamental requirements of an ANPR system are image capture using an ANPR camera and processing of the captured image. The image processing part, which is a computationally intensive task, includes three stages: Number Plate Localisation (NPL), Character Segmentation (CS) and Optical Character Recognition (OCR). The common hardware choice for its implementation is often high performance workstations. However, the cost, compactness and power issues that come with these solutions motivate the search for other platforms. Recent improvements in low-power high-performance Field Programmable Gate Arrays (FPGAs) and Digital Signal Processors (DSPs) for image processing have motivated researchers to consider them as a low cost solution for accelerating such computationally intensive tasks.Current ANPR systems generally use a separate camera and a stand-alone computer for processing. By optimising the ANPR algorithms to take specific advantages of technical features and innovations available within new FPGAs, such as low power consumption, development time, and vast on-chip resources, it will be possible to replace the high performance roadside computers with small in-camera dedicated platforms. In spite of this, costs associated with the computational resources required for complex algorithms together with limited memory have hindered the development of embedded vision platforms.The work described in this thesis is concerned with the development of a range of image The overall results achieved show that the entire ANPR system can be implemented on a single FPGA that can be placed within an ANPR camera housing to create a stand-alone unit. As the benefits of this are drastically improve energy efficiency and removing the need for the installation and cabling costs associated with bulky PCs situated in expensive, cooled, waterproof roadside cabinets. Certificate of OriginalityAutomatic Number Plate Recognition on FPGA iii Certificate of OriginalityI hereby certify that the work presented in this report is my original research and has not been presented for a higher degree at any other university or institute.

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Section: Contours Tracking Algorithmsmentioning

confidence: 99%

“…Where k F denotes a curvature related term in order to keep the propagating curve as smooth as possible [85].…”

Section: Contours Tracking Algorithmsmentioning

confidence: 99%

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Automatic Number Plate Recognition on FPGA

Zhai

Bensaali

McDonald-Maier

2013

2013 IEEE 20th International Conference on Electronics, Circuits, and Systems (ICECS)

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“…Capar and Gokmen [111] established a shape-driven active contour model, which utilizes a variational fast marching algorithm, and applied it to the plate character-segmentation problem. First, coarse location of each character is found by an ordinary fast marching technique [112] combined with a gradient-and curvature-dependent speed function, as presented in [113]. The method proceeds with the segmentation of exact boundaries through the calculation of a special fast marching methodology, which depends on gradient, curvature, and shape similarity information.…”

Section: A Binary Image Processing 1) Projections and Binary Algoritmentioning

confidence: 99%

License Plate Recognition From Still Images and Video Sequences: A Survey

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

507

248

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License plate recognition (LPR) algorithms in images or videos are generally composed of the following three processing steps: 1) extraction of a license plate region; 2) segmentation of the plate characters; and 3) recognition of each character. This task is quite challenging due to the diversity of plate formats and the nonuniform outdoor illumination conditions during image acquisition. Therefore, most approaches work only under restricted conditions such as fixed illumination, limited vehicle speed, designated routes, and stationary backgrounds. Numerous techniques have been developed for LPR in still images or video sequences, and the purpose of this paper is to categorize and assess them. Issues such as processing time, computational power, and recognition rate are also addressed, when available. Finally, this paper offers to researchers a link to a public image database to define a common reference point for LPR algorithmic assessment.

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“…To deal with the seriously degraded plate images, Nomura et al [ Based on a variational fast marching algorithm, Capar and Gokmen [60] proposed a shape-driven active contour model for character segmentation. First, coarse character locations are determined by an ordinary fast marching technique [61] combined with a gradient and curvature dependent speed function [62]. The exact boundaries of characters are then determined by means of a special fast marching methodology, which depends on gradient, curvature and shape similarity information.…”

Section: Binary Image-based License Plate Character Segmentationmentioning

confidence: 99%

Algorithms and architectures for low-cost license plate recognition system

Ku¹

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Efficient unassisted video segmentation using enhanced fast marching

Cited by 8 publications

References 12 publications

Automatic Number Plate Recognition on FPGA

Automatic Number Plate Recognition on FPGA

License Plate Recognition From Still Images and Video Sequences: A Survey

Algorithms and architectures for low-cost license plate recognition system

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