A new fast efficient non-maximum suppression algorithm  based on image segmentation

Al-Furaiji, Oday Jasim Mohammed; Tuấn, Nguyễn Anh; Tsviatkou, V. Yu.

doi:10.11591/ijeecs.v19.i2.pp1062-1070

Cited by 7 publications

(4 citation statements)

References 25 publications

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“…Figure 1 shows the structure of the bitmap image. The bitmap class is used to show image files on the canvas, and the image is displayed in the drawable folder or the onDraw() method showing the image file on the SD card is used to verify that the image is invoked [19]- [22].…”

Section: Image Analysis and Voice Output 31 Bitmap Image Loadmentioning

confidence: 99%

Security system using mobile image processing and color recognition for the visually impaired

Rhee

Cho

2022

IJEECS

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Voice technology at traffic lights or bus stops is emerging for the independent daily life of the blind, but there are few technologies that efficiently help the blind, such as knowing the color of clothes to wear before going out or entering the bus stop at once. To support such difficulties, this paper proposes a method that can be helped by using a smartphone application to distinguish the color of outdoor clothes. Smartphones, which are hardware-based for applications, have the advantage of predictable results, ease of transportation, independence from direct use, and personal support for the blind through various applications. However, there are very few applications to help the blind. This paper proposes the development of an application that can efficiently and independently recognize colors and images at anytime, anywhere by scanning images using smartphone cameras and converting them into bitmap images. Finally, the effects that can be expected through the application proposed in this study are described.

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Section: Image Analysis and Voice Output 31 Bitmap Image Loadmentioning

confidence: 99%

Security system using mobile image processing and color recognition for the visually impaired

Rhee

Cho

2022

IJEECS

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“…The image processing approach also minimizes the subjectivity of traditional classification methods and is more straightforward. Other image processing techniques that uses hybrid approach [21], image segmentation [22] and cluster-based feature [23] also proven to be useful in the process of image processing technique. Hence, according to the stated benefits, the proposed recognition system implements the image processing technique to detect the Harumanis mango leaf diseases.…”

Section: Related Workmentioning

confidence: 99%

Harumanis mango leaf disease recognition system using image processing technique

Gining

Fauzi

Yusoff

et al. 2021

IJEECS

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Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

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“…where p in , p out is the probability density function of pixel x inside and outside the object and can be calculated according to the segmentation result of the U-net model based on coarse calibration [29,30]. I (x) is the image intensity value, and the function g(x) is defined as…”

Section: Image Segmentationmentioning

confidence: 99%

Key Algorithms for Segmentation of Copperplate Printing Image Based on Deep Learning

Zhang

Xie

Zhang

2021

Mobile Information Systems

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As a branch of the field of machine learning, deep learning technology is abrupt in various computer vision tasks with its powerful functional learning functions. The deep learning method can extract the required features from the original data and dynamically adjust and update the parameters of the neural network through the backpropagation algorithm so as to achieve the purpose of automatically learning features. Compared with the method of extracting features manually, the recognition accuracy is improved, and it can be used for the segmentation of copperplate printing images. This article mainly introduces the research on the key algorithm of the copperplate printing image segmentation based on deep learning and intends to provide some ideas and directions for improving the copperplate printing image segmentation technology. This paper introduces the related principles, watershed algorithm, and guided filtering algorithm of copperplate printing image synthesis process and establishes an image segmentation model. As a result, a deep learning-based optimization algorithm mechanism for the segmentation of copper engraving printing images is proposed, and experimental steps such as main color extraction in the segmentation of copper engraving printing images, adaptive main color extraction based on fuzzy set 2, and main color extraction based on fuzzy set 2 are proposed. Experimental results show that the average processing time of each image segmentation model in this paper is 0.39 seconds, which is relatively short.

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A new fast efficient non-maximum suppression algorithm based on image segmentation

Cited by 7 publications

References 25 publications

Security system using mobile image processing and color recognition for the visually impaired

Security system using mobile image processing and color recognition for the visually impaired

Harumanis mango leaf disease recognition system using image processing technique

Key Algorithms for Segmentation of Copperplate Printing Image Based on Deep Learning

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