Image Annotation by Moments

Oujaoura, Mustapha; Minaoui, Brahim; Fakir, Mohamed

doi:10.15579/gcsr.vol1.ch10

Cited by 12 publications

(10 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, Hu presented the use of moments for image analysis and pattern recognition (Hu, 1962). Legendre moments are classical orthogonal moment which are one of widest and most commonly moments used in recognition and image analysis (Oujaoura et al, 2014).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Human Iris Recognition Based on Hybrid Technique

Hashim¹,

Al-Hashimi²

2019

Journal of Computer Science

View full text Add to dashboard Cite

Iris recognition is a biometric technique that uses iris pattern information to detect person identification. Initially, the system find out the boundary of the pupil and iris. Then, Circular Hough transform used to find out the center of both pupil and iris in order to crop iris part from the eye image. After that, Daugman's Rubber Sheet model utilized for performing the normalizing step. Then, features extracted based on Legendre moment and Local Quantized. Several orders value with many region of iris have been used to get best value, which satisfied the highest recognition rate. Matching was performed by City Block Distance. The simulation was carried out using samples from CASIA.v4-Interval database, the main tool for programming is MATLAB.

show abstract

Section: Methodsmentioning

confidence: 99%

“…Symmetry and recursion properties of the orthogonal basis function can be exploited to speed up the computation (Oujaoura et al, 2014).…”

Section: A Legendre Momentsmentioning

confidence: 99%

Human Iris Recognition Based on Hybrid Technique

Hashim¹,

Al-Hashimi²

2019

Journal of Computer Science

View full text Add to dashboard Cite

show abstract

“…They are computed from the Legendre polynomials. Zernike moments also have been used as features set in many applications [44], as they can represent the properties of an image with no redundancy or overlapping of information between moments. They are constructed as the mapping of an image onto a set of complex Zernike polynomials [41].…”

Section: Hand-crafted Image Descriptorsmentioning

confidence: 99%

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Loddo

Putzu

2021

View full text Add to dashboard Cite

Automating the analysis of digital microscopic images to identify the cell sub-types or the presence of illness has assumed a great importance since it aids the laborious manual process of review and diagnosis. In this paper, we have focused on the analysis of white blood cells. They are the body’s main defence against infections and diseases and, therefore, their reliable classification is very important. Current systems for leukocyte analysis are mainly dedicated to: counting, sub-types classification, disease detection or classification. Although these tasks seem very different, they share many steps in the analysis process, especially those dedicated to the detection of cells in blood smears. A very accurate detection step gives accurate results in the classification of white blood cells. Conversely, when detection is not accurate, it can adversely affect classification performance. However, it is very common in real-world applications that work on inaccurate or non-accurate regions. Many problems can affect detection results. They can be related to the quality of the blood smear images, e.g., colour and lighting conditions, absence of standards, or even density and presence of overlapping cells. To this end, we performed an in-depth investigation of the above scenario, simulating the regions produced by detection-based systems. We exploit various image descriptors combined with different classifiers, including CNNs, in order to evaluate which is the most suitable in such a scenario, when performing two different tasks: Classification of WBC subtypes and Leukaemia detection. Experimental results have shown that Convolutional Neural Networks are very robust in such a scenario, outperforming common machine learning techniques combined with hand-crafted descriptors. However, when exploiting appropriate images for model training, even simpler approaches can lead to accurate results in both tasks.

show abstract

“…The computation of Legendre moments is in general a time consuming process. In many references [30,42,41,23], their computation has been performed using closed form representations for orthogonal polynomials, and taking little care to the accuracy of the quadrature formulas used to approximate integrals. In this work, we describe a fast and stable algorithm for the computation of the orthogonal moments of an image with respect to both a continuous and a discrete inner product, based on classical recurrence relations for orthonormal polynomials.…”

Section: Introductionmentioning

confidence: 99%

“…In [38], the authors propose a CAD system for the diagnosis of breast masses in mammography images which uses Zernike moments for extracting the shape and margin properties of the masses. In [30], an image annotation system, based on different type of moments, has been developed to allow searching image databases. The experimental results showed that the annotation system coupling Legendre moments to Bayesian networks gives good results for images that are well and properly segmented.…”

Section: Introductionmentioning

confidence: 99%

Fast and accurate computation of orthogonal moments for texture analysis

Ruberto

Putzu

Rodriguez

2018

Pattern Recognition

View full text Add to dashboard Cite

In this work we describe a fast and stable algorithm for the computation of the orthogonal moments of an image. Indeed, orthogonal moments are characterized by a high discriminative power, but some of their possible formulations are characterized by a large computational complexity, which limits their real-time application. This paper describes in detail an approach based on recurrence relations, and proposes an optimized Matlab implementation of the corresponding computational procedure, aiming to solve the above limitations and put at the community's disposal an efficient and easy to use software. In our experiments we evaluate the effectiveness of the recurrence formulation, as well as its performance for the reconstruction task, in comparison to the closed form representation, often used in the literature. The results show a sensible reduction in the computational complexity, together with a greater accuracy in reconstruction. In order to assess and compare the accuracy of the computed moments in texture analysis, we perform classification experiments on six wellknown databases of texture images. Again, the recurrence formulation performs better in classification than the closed form representation. More importantly, if computed from the GLCM of the image using the proposed stable procedure, the orthogonal moments outperform in some situations some of the most diffused state-of-the-art descriptors for texture classification.

show abstract

Image Annotation by Moments

Cited by 12 publications

References 22 publications

Human Iris Recognition Based on Hybrid Technique

Human Iris Recognition Based on Hybrid Technique

On the Effectiveness of Leukocytes Classification Methods in a Real Application Scenario

Fast and accurate computation of orthogonal moments for texture analysis

Contact Info

Product

Resources

About