Transformational DT-CNN design from morphological specifications

Brugge, M.H. ter; Nijhuis, J.A.G.; Spaanenburg, Lambert

doi:10.1109/81.721253

Cited by 9 publications

(3 citation statements)

References 24 publications

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“…For the "grayness" feature, the gray values of pixels taken from a large number of exemplary LPs are used to construct a frequency table. Pixels that belong to fixed ranges were then identified using several DTCNNs, whose templates were constructed by combining the appropriate morphological operations and traditional filter techniques (dilation, Sobel, and Laplacian operators), according to the research described in [30]. In [31], the pulse-coupled neural network (PCNN) schema was described to generate candidate regions that may contain an LP.…”

Section: Classifiers 1) Statistical Classifiersmentioning

confidence: 99%

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

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

512

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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Section: Classifiers 1) Statistical Classifiersmentioning

confidence: 99%

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

Anagnostopoulos

Psoroulas

et al. 2008

IEEE Trans. Intell. Transport. Syst.

512

248

View full text Add to dashboard Cite

show abstract

“…This analysis has two main limitations. On the one hand, it is limited to those DTCNN operations that are straight derivatives of morphological functions , and it limits the image processing to binary images and templates or implies the utilization of non‐linear templates (gray‐scale morphology, ). On the other hand, as in , it is limited to templates with dimensions multiple of 3.…”

Section: Related Workmentioning

confidence: 99%

Split and shift methodology on cellular processor arrays: area saving versus time penalty

Fernandez

Brea

Cabello

2012

Circuit Theory & Apps

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This paper addresses the so-called split and shift methodology. This methodology deals with the implementation of kernels of sizes that go above the physically implemented resources (local connections and weighting circuits) on synchronous cellular processor arrays (CPA), including the realization of large neighborhood operations and/or the reduction of the available hardware in order to drop the area consumption. Two main goals are pursued in the development of the methodology, namely: (1) minimum penalty at processing time and (2) absolutely no penalty at functional level. The paper presents different techniques and guidelines for the methodology application and introduces a Figure of Merit to evaluate them by relating area gains with time penalty. This, along with a kernel shape analysis, led us to propose more adequate configurations of weighting circuits and to justify the classical choice of North-East-West-South connectivity. To validate the methodology, we realize several estimates over actual physical implementations, and we propose the realization over CPAs of the spin filters, scale invariant feature transform and speeded-up robust features algorithms. A more in-depth trade-off analysis is realized over the implementation of the pixel level snakes algorithm.but it can interact with separated PEs, thanks to the propagative effects of the array dynamics of kernel application. The basic characteristic of local connectivity and its simple SIMD control makes this kind of systems very suitable for hardware implementation. However, as a consequence, the natural size of the kernels to be applied is limited to the smallest one (3 Â 3). This is, on the other hand, an important limitation in the functionality of a CPA as larger neighborhoods are needed in several image processing primitives as diffusion or low-pass filtering operations [1], halftoning [2], texture analysis [3] or matching and hit&miss operations [4], some of them used in algorithms like modern scale-and rotation-invariant feature extractors like scale invariant feature transform (SIFT) and speeded-up robust features (SURF) [5,6].As indicated, a CPA can realize global processing taking into account the whole image information thanks to the propagative effects of the architecture. According to this we can think in solving the remote neighbors interaction through the recursive application of templates. In fact, a recursive process can be summarized in the application of a large neighborhood (LN) template. Nevertheless, the inverse process, the decomposition of a LN template into minimum-sized templates is not trivial, and different approaches have been developed to deal with this issue.The challenge is then to implement any kind of LN operations while keeping the local connectivity and with affordable penalties in performance. Our goal is to do it, in addition, with the minimum impact in the architecture at hardware level.On the other hand, on focal-plane processors with a pixel-to-PE assignment, the area occupation is not only a matter of cost or h...

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“…At present, the representative license plate classification and recognition task is performed by machine learning methods, such as a Support Vector Machine (SVM) classifier, and the neural network methods including Backpropagation Neural Network (BPNN) [7][8][9][10][11]. In particularly, multilayer neural networks and backpropagation training is adequate for vehicle plate licenses reading and recognition [12][13][14]. In addition, since the high dimensional original images contain redundant information, it is better to extract useful image characteristics rather than using each pixel value of the images as feature vectors [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

Xie

Ming

Zhao

et al. 2018

Journal of Advanced Transportation

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The rapid development of the license plate recognition technology has made great progress for its widespread uses in intelligent transportation system (ITS). This paper has proposed a novel license plate detection and character recognition algorithm based on a combined feature extraction model and BPNN (Backpropagation Neural Network) which is adaptable in weak illumination and complicated backgrounds. Firstly, a preprocessing is first used to strengthen the contrast ratio of original car image. Secondly, the candidate regions of license plate are checked to verify the true plate, and the license plate image is located accurately by the integral projection method. Finally, a new feature extraction model is designed using three sets of features combination, training the feature vectors through BPNN to complete accurate recognition of the license plate characters. The experimental results with different license plate demonstrate effectiveness and efficiency of the proposed algorithm under various complex backgrounds. Compared with three traditional methods, the recognition accuracy of proposed algorithm has increased to 97.7% and the consuming time has decreased to 46.1ms.

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Transformational DT-CNN design from morphological specifications

Cited by 9 publications

References 24 publications

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

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

Split and shift methodology on cellular processor arrays: area saving versus time penalty

A Robust License Plate Detection and Character Recognition Algorithm Based on a Combined Feature Extraction Model and BPNN

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