Hybrid local and global descriptor enhanced with colour information

Kabbai, Leila; Abdellaoui, Mehrez; Douik, Ali

doi:10.1049/iet-ipr.2016.0349

Cited by 5 publications

(5 citation statements)

References 36 publications

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“…Colour, thermal, spectral, acoustic, tactile and depth sensors have been used for data acquisition for classification and recognition in the fields of agriculture and food processing. Each sensor has some limitations for example, colour (RGB) images are highly sensitive to the lighting condition and background colour [2,85]. A detailed investigation of literature illustrates that the reflectance properties of objects can be represented by wavelength and hyperspectral cameras can be used for this purpose.…”

Section: Data Acquisitionmentioning

confidence: 99%

“…The interest points are not consistent and can vary from sample to sample in a recognition task [112]. Moreover, usual practices include a combination of local and global features for superior classification effectiveness [85,113,114]. Availability of whole object details is another inherent limitation in image acquisition due to the poor acquisition, noise, partial information, and data loss during conversion (e.g.…”

Section: Feature Extractionmentioning

confidence: 99%

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A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

151

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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Section: Data Acquisitionmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

151

View full text Add to dashboard Cite

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“…The primary step for zero-watermarking is how to extract special and robust image features, robust against various image attacks, to reconstruct the watermark and verify copyright. In [ 80 , 144 ], the authors discussed various global and local features. In global features, the processing is performed on whole host image pixels, as Geometric Moment Features, Space-Time Volumes (STV).…”

Section: Discussionmentioning

confidence: 99%

Security of medical images for telemedicine: a systematic review

Magdy

Hosny

Ghali

et al. 2022

Multimed Tools Appl

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Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

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“…With the rapid development of computer networks and multimedia technologies, the number of images is growing rapidly, and the demand for e cient browsing of images is increasing [1][2][3][4][5][6]. Therefore, content-based image classication and retrieval technology is a hot topic at present, but the images are diverse and most of them the image is a ected by illumination, noise, size and position of the object, which increases the di culty of image classi cation, so it is important to accurately acquire the stable features of the image [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

“…Since the image was taken, image restoration came into being [7]. Image restoration technology was rst applied to the restoration of medieval art in the Renaissance, and trained professionals lled the cracks to restore the original image [6]. Among them, the technique of lling the damaged area such as cracks and scratches in the damaged image to make it try to restore the original appearance is called image restoration or retouching.…”

Section: Introductionmentioning

confidence: 99%

Compressed sensing image restoration algorithm based on improved SURF operator

Zhou

Zhang

Lucas³

2018

Open Physics

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Aiming at the excellent descriptive ability of SURF operator for local features of images, except for the shortcoming of global feature description ability, a compressed sensing image restoration algorithm based on improved SURF operator is proposed. The SURF feature vector set of the image is extracted, and the vector set data is reduced into a single high-dimensional feature vector by using a histogram algorithm, and then the image HSV color histogram is extracted.MSA image decomposition algorithm is used to obtain sparse representation of image feature vectors. Total variation curvature diffusion method and Bayesian weighting method perform image restoration for data smoothing feature and local similarity feature of texture part respectively. A compressed sensing image restoration model is obtained by using Schatten-p norm, and image color supplement is performed on the model. The compressed sensing image is iteratively solved by alternating optimization method, and the compressed sensing image is restored. The experimental results show that the proposed algorithm has good restoration performance, and the restored image has finer edge and texture structure and better visual effect.

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Hybrid local and global descriptor enhanced with colour information

Cited by 5 publications

References 36 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

Security of medical images for telemedicine: a systematic review

Compressed sensing image restoration algorithm based on improved SURF operator

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