A robust vision system for vehicle licence plate recognition using grey-scale morphology

Poon, J.C.H.; Ghadiali, M.; Mao, G.M.T.; Sheung, L.M.

doi:10.1109/isie.1995.497029

Cited by 18 publications

(11 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multi-line LP segmentation algorithms can also be classified into three classes, namely algorithms based on projection [21][22][23], binarization [24][25][26][27] and global optimization [28]. In the projection algorithms, gradient or color projection on vertical orientation will be calculated at first.…”

Section: Related Workmentioning

confidence: 99%

A configurable method for multi-style license plate recognition

Jiao

Huang

2009

Pattern Recognition

171

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

A configurable method for multi-style license plate recognition

Jiao

Huang

2009

Pattern Recognition

171

View full text Add to dashboard Cite

“…A large number of edge detectors [1,2,13,16] have been reported for grayscale images. To apply the edge detectors to color images, one may first transform color images into intensity images.…”

Section: Introductionmentioning

confidence: 99%

Programmable logical circuit architecture for edge detection in color images

Chang

Yeh

Cherng

et al. 2009

2009 4th International Conference on Autonomous Robots and Agents

View full text Add to dashboard Cite

Perceiving a color difference is different from identifying the colors causing the difference. The former is a subtask of the latter that involves the tasks of color identification and color difference perception. In this raper, a technique capable of detecting specific color edges in an image is proposed. Instead of developing an efficient algorithm for detecting edges caused by specific colors, we prefer a process that can be effectively implemented in hardware. The proposed process first calculates the differences of the individual color components of each pixel. Next, the colors causing the differences are identified by analyzing the calculated differences of color components. An architecture realizing the process using an FPGA-based SOPC is detailed. The architecture is readily parallelizable and allows users to specify the colors that form the edges to be located.

show abstract

“…The features regarding license plate format include shape, symmetry [15], height-towidth ratio [23], [25], color [17], [25], texture of grayness [2], [25], spatial frequency [26], and variance of intensity values [8], [10]. Character features include line [34], blob [13], the sign transition of gradient magnitudes, the aspect ratio of characters [12], the distribution of intervals between characters [28], and the alignment of characters [32]. In reality, a small set of robust, reliable, and easy-to-detect object features would be adequate.…”

Section: Introductionmentioning

confidence: 99%

“…There are two major tasks involved in the identification stage, character separation and character recognition. Character separation has in the past been accomplished by such techniques as projection [11], [30], morphology [2], [10], [28] relaxation labeling, connected components [25], and blob coloring. Every technique has its own advantages and disadvantages.…”

Section: Introductionmentioning

confidence: 99%