Detecting Image Forgery Using XOR and Determinant of Pixels for Image Forensics

Mookdarsanit, Pakpoom; Soimart, Lawankorn; Ketcham, Mahasak; Hnoohom, Narit

doi:10.1109/sitis.2015.78

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…A pixel to pixel XOR logical operator is applied to perform the comparison between the polygons image and the binary human hand image. It consists of an XOR operation between each pixel of the polygons image and its correspondent pixel in the hand image, similarly to methods present in the available literature (Bovik and Desai, 2000;Koukounis et al, 2011;Mookdarsanit et al, 2015;de Faria Lemos et al, 2017). An analogous procedure is illustrated in Fig.…”

Section: Pattern Recognition Modulementioning

confidence: 99%

A Markerless Joint Detection through a Hand Geometric Representation

Lemos

Vince

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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In several fields, such as man-machine interface and occupational therapy, the human hand-joint position is required. Traditional methods usually rely on image processing allied with marker placement, e.g. reflexive marker, which can be time-consuming and uncomfortable for the subject. For these reasons, scientific efforts are being made to create reliable and convenient joint tracking. This paper proposes a methodology that generates geometric figures to mimic the hand configuration. This process is made possible by an optimization algorithm, which finds the most suitable placement of these geometric shapes. One time the real hand and the created representation share similar features, the joints position can be estimated. Two optimization algorithms were employed: particle swarm optimization and genetic algorithm. In both cases, satisfactory results were obtained. Although, particle swarm optimization marginally outperformed the latter method.

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Section: Pattern Recognition Modulementioning

confidence: 99%

A Markerless Joint Detection through a Hand Geometric Representation

Lemos

Vince

2020

Proceedings of the 12th International Conference on Agents and Artificial Intelligence

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“…Most improved methods used the concept of visual synonym to be accomplished geometric coherence estimation [30][31][32][33][34]. A visual synonym is a pair of visual contents [35][36][37][38] that can be combined to find the location of a photo from the large-scale dataset by mapping to the similar scenes and reducing the irrelevance.…”

Section: Visual-based Evidencementioning

confidence: 99%

Location Estimation of a Photo: A Geo-signature MapReduce Workflow

Mookdarsanit¹,

Rattanasiriwongwut²

2017

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Location estimation of a photo is the method to find the location where the photo was taken that is a new branch of image retrieval. Since a large number of photos are shared on the social multimedia. Some photos are without geo-tagging which can be estimated their location with the help of million geo-tagged photos from the social multimedia. Recent researches about the location estimation of a photo are available. However, most of them are neglectful to define the uniqueness of one place that is able to be totally distinguished from other places. In this paper, we design a workflow named G-sigMR (Geo-signature MapReduce) for the improvement of recognition performance. Our workflow generates the uniqueness of a location named Geo-signature which is summarized from the visual synonyms with the MapReduce structure for indexing to the large-scale dataset. In light of the validity for image retrieval, our G-sigMR was quantitatively evaluated using the standard benchmark specific for location estimation; to compare with other well-known approaches (IM2GPS, SC, CS, MSER, VSA and VCG) in term of average recognition rate. From the results, G-sigMR outperformed previous approaches.

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“…Neighborhood system ) (s N  , where sites s∈S refer to each component of the random variable [25][26][27]. In this work, a first order neighborhood system (1) Fig.…”

Section: First Order Neighborhood Systemmentioning

confidence: 99%

An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

Soimart¹,

Ketcham²

2016

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Abstract.Many image segmentation algorithms are available but most of them are not fit for interpretation of satellite images. Mean-shift algorithm has been used in many recent researches as a promising image segmentation technique, which has the speed at O(kn 2 ) where n is the number of data points and k is the number of average iteration steps for each data point. This method computes using a brute-force in the iteration of a pixel to compare with the region it is in. This paper proposes a novel algorithm named First-order Neighborhood Mean-shift (FNM) segmentation, which is enhanced from Mean-shift segmentation. This algorithm provides information about the relationship of a pixel with its neighbors; and makes them fall into the same region which improve the speed to O(kn). In this experiment, FNM was compared to well-known algorithms, i.e., K-mean (KM), Constrained K-mean (CKM), Adaptive K-mean (AKM), Fuzzy C-mean (FCM) and Meanshift (MS) using the reference map from the Landsat. FNM provided better results in terms of overall error and correctness criteria.

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Detecting Image Forgery Using XOR and Determinant of Pixels for Image Forensics

Cited by 10 publications

References 15 publications

A Markerless Joint Detection through a Hand Geometric Representation

A Markerless Joint Detection through a Hand Geometric Representation

Location Estimation of a Photo: A Geo-signature MapReduce Workflow

An Efficient Algorithm for Earth Surface Interpretation from Satellite Imagery

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