Automatic logo detection and extraction using singular value decomposition

Dixit, Umesh D.; Shirdhonkar, M. S.

doi:10.1109/iccsp.2016.7754252

Cited by 8 publications

(3 citation statements)

References 4 publications

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“…Veershetty et al [44] eliminate noise and symbols using a morpho-logical operation, and extract each logo from the document using the CC rule. Dixit et al [13] find CCs and perform area-based thresholding to get pool of possible logo candidates. This method is based on the assumption that logo region has a higher spatial density than non-logo region, which is not always true.…”

Section: Related Workmentioning

confidence: 99%

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Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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The international tourism growth forces governments to make a big effort to improve the security of national borders. The compulsory passport stamping is used in guaranteeing the safekeeping of the entry point of the border. For each passenger, the border police must check the existence of exit stamps and/or the entry stamps of the country that the passenger visits, in all the pages of his passport. However, the systematic control considerably slows the operations of the border police. Protecting the borders from illegal immigrants and simplifying border checkpoints for law-abiding citizens and visitors is a delicate compromise. The purpose of this paper is to perform a flexible and scalable system that ensures faster, safer and more efficient stamp controlling. An automatic system of stamp extraction for travel documents is proposed. We incorporate several methods from the field of artificial intelligence, image processing and pattern recognition. At first, texture feature extraction is performed in order to find potential stamps. Next, image segmentation aimed at detecting objects of specific textures are employed. Then, isolated objects are extracted and classified using multi-layer perceptron artificial network. Promising results are obtained in terms of accuracy, with a maximum average of 0.945 among all the images, improving the performance of MLP neural network in all cases.

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Section: Related Workmentioning

confidence: 99%

“…The couple of texture features having the largest Fisher score are: « Mean » and « Hölder exponent », they are selected as the most discriminative for our case. The multifractal texture feature (Hölder exponent) α is defined by the following equation (13).…”

Section: Figurementioning

confidence: 99%

Neural Network-based System for Automatic Passport Stamp Classification

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

ITC

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“…According to the obtained results, we can affirm that the proposed approach is more effective than many other state-of-the-art logo detection methods. [5] Tobacco-800 1151 91.47 98.10 Li et al [15] Tobacco-800 1290 86.50 99.40 Pham et al [16] Tobacco-800 426 90.05 92.98 Dixit and Shirdhonkar [17] Tobacco-800 1290 89.52 -Jain and Doermann [18] Tobacco-800 435 -87.00 Le et al [19] Tobacco-800 1290 88.78 91.15 Kumar and Ranjith [20] Tobacco-800 500 91.30 -Wiggers et al [21] Tobacco-800 1290 -72 Alaei et al [22] Tobacco-800 1290 91.50 75.25 Proposed approach Tobacco-800 1290 96.86 98.93…”

Section: Experimental Studymentioning

confidence: 99%

Logo Detection Based on FCM Clustering Algorithm and Texture Features

Zaaboub¹,

Tlig²,

Sayadi³

et al. 2020

Lecture Notes in Computer Science

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Logo detection methods usually depend on logo shapes and need for training data or a-priori information on the processed images. This limits their effectiveness to real-world applications. In this paper, we tackle these challenges by exploring the textural information. Specifically we propose a novel approach for administrative logo detection based on a fuzzy classification with a multi-fractal texture feature, capable of automatically characterizing texture measures describing logo and non-logo regions. Experimental results, using two real datasets, confirm the feasibility of the proposed method for degraded administrative documents. Extensive comparative evaluations demonstrate the superiority of this approach over the state-of-the-art methods.

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