Sub-pixel matching method for low-resolution thermal stereo images

Zoetgnande, Yannick Wend Kuni; Cormier, Geoffroy; Fougères, Alain-Jérôme; Dillenseger, Jean-Louis

doi:10.1016/j.infrared.2019.103161

Cited by 4 publications

(3 citation statements)

References 30 publications

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“…Some problems of incomplete edges and contours because of the changes in the local illumination and hence an inadequate selective threshold is handled by [2] when a high-level technique is considered to accommodate useful data and reject redundant information. Three types of PC based frequency domain operations that considered a phase in their operating are presented as follows [16][17][18]:…”

Section: Phase Congruency Measurmentsmentioning

confidence: 99%

“…The system accuracy is computed at equal error rate (EER) value which reflects the maximum system performance when the false acceptance rate (FAR) equals to the false rejection rate (FRR) as explained in 15. The error distance between x ̅ and y ̅ with m+1 length can be described in (16)(17)(18) [30][31][32][33][34].…”

Section: Classificationmentioning

confidence: 99%

“…TELKOMNIKA Telecommun Comput El Control − Euclidean distance The ED is the straight-line distance between two points in Euclidean space, with this distance, Euclidean space becomes a metric space [35]. (16) − Manhattan distance…”

mentioning

confidence: 99%

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Towards better performance: phase congruency based face recognition

Hamd

Rasool

2020

TELKOMNIKA

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Phase congruency is an edge detector and measurement of the significant feature in the image. It is a robust method against contrast and illumination variation. In this paper, two novel techniques are introduced for developing a low-cost human identification system based on face recognition. Firstly, the valuable phase congruency features, the gradient-edges and their associated angles are utilized separately for classifying 130 subjects taken from three face databases with the motivation of eliminating the feature extraction phase. By doing this, the complexity can be significantly reduced. Secondly, the training process is modified when a new technique, called averaging-vectors is developed to accelerate the training process and minimizes the matching time to the lowest value. However, for more comparison and accurate evaluation, three competitive classifiers: Euclidean distance (ED), cosine distance (CD), and Manhattan distance (MD) are considered in this work. The system performance is very competitive and acceptable, where the experimental results show promising recognition rates with a reasonable matching time.

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Section: Phase Congruency Measurmentsmentioning

confidence: 99%