Wavelet and FFT Based Image Denoising Using Non-linear Filters

Gopinathan, S.; Kokila, R.; Thangavel, P.

doi:10.11591/ijece.v5i5.pp1018-1026

Cited by 19 publications

(13 citation statements)

References 26 publications

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“…Therefore, we need to remove the noise components from the document. Since the input document image is a binary image, we do not use common image denoising methods [31,32], such as median filtering [33], mean filtering [34], or fast fourier transform (FFT) methods [35], and so on.…”

Section: Noise Removalmentioning

confidence: 99%

“…Then, we use dilation operation to connect the Chinese character's upper part and lower part to form a connected region by considering a circular structuring element with radius 1, and the result is shown in Figure 2c. denoising methods [31,32], such as median filtering [33], mean filtering [34], or fast fourier transform (FFT) methods [35], and so on. In this stage, a CC with less than 25 pixels is considered a punctuation mark or noise and will be removed.…”

Section: Morphological Operationmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Document Skew Detection Method Using Probability Model and Q Test

Huang

Chen

et al. 2019

Electronics

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Document skew detection is one of the key technologies in most of the document analysis systems. However, existing skew detection methods either have low accuracy or require a large amount of computation. To achieve a good tradeoff between efficiency and performance, we propose a novel skew detection approach based on bounding boxes, probability model, and Dixon’s Q test. Firstly, bounding boxes are used to pick out the eligible connected components (ECC). Then, we calculate the slopes of the skew document with the probability model. Finally, we find the optimal result with Dixon’s Q test and projection profile method. Moreover, the proposed method can detect the skew angle in a wider range. The experimental results show that our skew detection algorithm can achieve high speed and accuracy simultaneously compared with existing algorithms.

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Section: Noise Removalmentioning

confidence: 99%

Section: Morphological Operationmentioning

confidence: 99%

An Efficient Document Skew Detection Method Using Probability Model and Q Test

Huang

Chen

et al. 2019

Electronics

View full text Add to dashboard Cite

show abstract

“…FFT (Fast Fourier Transform) is one of methods for transformation of audio signal in time domain into signal in frequency domain, which means that the audio recording process is kept in digital form in the form of audio spectrum wave with frequency base so it is easier in analyzing recorded audio frequency spectrum. On the other hand, this FFT implementation helps in the processing of filtering input signal properly to be frequency signal [11], [12].…”

Section: Fast Fourier Transformmentioning

confidence: 99%

Design and Analysis System of KNN and ID3 Algorithm for Music Classification based on Mood Feature Extraction

Sudarma¹,

Harsemadi²

2017

IJECE

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Each of music which has been created, has its own mood which is emitted, therefore, there has been many researches in Music Information Retrieval (MIR) field that has been done for recognition of mood to music. This research produced software to classify music to the mood by using K-Nearest Neighbor and ID3 algorithm. In this research accuracy performance comparison and measurement of average classification time is carried out which is obtained based on the value produced from music feature extraction process. For music feature extraction process it uses 9 types of spectral analysis, consists of 400 practicing data and 400 testing data. The system produced outcome as classification label of mood type those are contentment, exuberance, depression and anxious. Classification by using algorithm of KNN is good enough that is 86.55% at k value = 3 and average processing time is 0.01021. Whereas by using ID3 it results accuracy of 59.33% and average of processing time is 0.05091 second. Keyword:Clasification ID3 KNN Mood Music

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“…In particular, the application of band pass filters, such as high-pass and lowpass filters, may be considered as a most simple and rudimentary denoising method. The Fourier Transform might also be employed for denoising purposes [8]. However, all these mentioned approaches are ineffective when the noise is concentrated in a frequency band similar to that of the useful part of the signal, and this occurs in several real cases.…”

Section: Introductionmentioning

confidence: 99%