Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Dadgar, Alireza; Baleghi, Yasser; Ezoji, Mehdi

doi:10.5829/ije.2021.34.06c.08

Cited by 3 publications

(2 citation statements)

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“…From the above characteristices, it can be seen that the proposed algorithm on the challenging dataset in some cases, is sensitive to very small forged regions, Our future works include the localization of the forgery in the image using segmentation fusion [30] and the variants of Benford's law and enriching the feature vector with other popular features like Zernike Moments [31]. In addition, other classifiers like Radial Basis Function (RBF) neural networks [32] can be applied to improve the classification accuracy.…”

Section: Misclassification Casesmentioning

confidence: 99%

A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

2022

IJE

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Due to the ease of access to platforms that can be used by forgers to tamper digital documents, providing automatic tools for identifying forged images is now a hot research field in image processing. This paper presents a novel forgery detection algorithm based on variants of Benford's law. In the proposed method, Mean Absolute Deviation (MAD) feature is extracted using traditional Benford's law. Also, generalized Benford's law is used for mantissa distribution feature vector. In addition to Benford's law-based features, other statistical features are used to construct the final feature vector. Finally, support vector machine (SVM) with three different kernel functions is used to classify original and forged images. The method has been tested on two common image datasets (CASIA V1.0 and V2.0). The experimental results show that 0.27% and 0.21% improvements on CASIA V1.0 and CASIA V2.0 datasets were achieved, respectively in detection accuracy by the proposed method in comparison to best state-of-the-art methods. The proposed efficient algorithm has a simple implementation. Moreover, on the basis of Benford's law rich features are extracted from images so that classification process is efficiently performed by a simple SVM classifier in a short time.

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Section: Misclassification Casesmentioning

confidence: 99%

A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

2022

IJE

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“…It is a frontier hot topic and quite a few studies have been devoted to the continuous optimization of subsequent unrealistic objects after one "optimal" segmentation [29,30]. The identification and combination of multiple scales have been shown to be help resolve undesirable objects [31]. The concepts of over-subdivision and under-subdivision are equally applicable to classifying undesirable SUs.…”

Section: Introductionmentioning

confidence: 99%

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

et al. 2022

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Slope units (SUs) are sub-watersheds bounded by ridge and valley lines. A slope unit reflects the physical relationship between landslides and geomorphological features and is especially useful for landslide sensitivity modeling. There have been significant algorithmic advances in the automatic delineation of SUs. But the intrinsic difficulties of determining input parameters and correcting for unreasonable SUs have hindered their wide application. An improved method of the evaluation and local multi-scale optimization for the automatic extraction of SUs is proposed. The Sus’ groups more consistent with the topographic features were achieved through a stepwise approach from a global optimum to a local refining. First, the preliminary subdivisions of multiple SUs were obtained based on the r.slopeunit software. The optimal subdivision scale was obtained by a collaborative evaluation approach capable of simultaneously measuring objective minimum discrepancies and seeking a global optimum. Second, under the selected optimal scale, unreasonable SUs such as over-subdivided slope units (OSSUs) and under-subdivided slope units (USSUs) were further distinguished. The local average similarity (LS) metric for each SU was designed based on calculating the SU’s area, common boundary and neighborhood variability. The inflection points of the cumulative frequency curve of LS were calculated as the distinguishing intervals for those unrealistic SUs by maximum interclass variance threshold. Third, a new effective optimization mechanism containing the re-subdivision of USSUs and merging of OSSUs was put into effect. We thus obtained SUs composed of terrain subdivisions with multiple scales, which is currently one of the few available methods for non-single scales. The statistical distributions of density, size and shapes demonstrate the excellent performance of the refined SUs in capturing the variability of complex terrains. Benefiting from the sufficient integrating approach of diverse features for each object, it is a significant advantage that the processing object can be transferred from general entirety to each precise individual.

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A Novel Approach for Automated Strawberry Fruit Varieties Classification Using Image Processing and Machine Learning

Javidan,

Ampatzidis,

Vakilian

et al. 2024

2024 10th International Conference on Artificial Intelligence and Robotics (QICAR)

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Improved Object Matching in Multi-Objects Tracking Based On Zernike Moments and Combination of Multiple Similarity Metrics

Cited by 3 publications

References 40 publications

A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

A Novel Image Splicing Detection Algorithm Based on Generalized and Traditional Benford’s Law

An Improved Method for the Evaluation and Local Multi-Scale Optimization of the Automatic Extraction of Slope Units in Complex Terrains

A Novel Approach for Automated Strawberry Fruit Varieties Classification Using Image Processing and Machine Learning

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