Human visual system inspired object detection and recognition

Mandal, Debashree; Panetta, Karen; Agaian, Sos S.

doi:10.1109/tepra.2012.6215669

Cited by 8 publications

(1 citation statement)

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“…The multi-channel mechanism of HVS shows that neurons decompose visual information into different channels, such as color, frequency, orientation, etc. [36]. According to the related research on the phenomenon of contrast sensitivity, human eyes are more sensitive to distortion in the mid-frequency region than in the low-frequency smooth region and the high-frequency texture region [37].…”

Section: Multi-scale Processing Techniquementioning

confidence: 99%

A No-Reference Quality Assessment Method for Screen Content Images Based on Human Visual Perception Characteristics

2022

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The widespread application of screen content images (SCIs) has met the needs of remote display and online working. It is a topic that is challenging and worthwhile discussing in research on quality assessment for SCIs. However, existing methods focus on extracting artificial features to predict image quality, which are subjective and incomplete, or lack good interpretability. To overcome these problems, we propose an effective quality assessment method for SCIs based on human visual perceptual characteristics. The proposed method simulates the multi-channel working mechanism of the human visual system (HVS) through pyramid decomposition and the information extraction process of brains with the help of dictionary learning and sparse coding. The input SCIs are first decomposed at multiple scales, and then dictionary learning and sparse coding are applied to the images at each scale. Furthermore, the sparse representation results are analyzed from multiple perspectives. First, a pooling scheme about generalized Gaussian distribution and log-normal distribution is designed to describe the sparse coefficients with and without zero values, respectively. Then the sparse coefficients are used to characterize the energy characteristics. Additionally, the probability of each atom is calculated to describe the statistical property of SCIs. Since the above process only deals with brightness, color-related features are also added to make the model more general and robust. Experimental results on three public SCI databases show that the proposed method can achieve better performance than existing methods.

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Section: Multi-scale Processing Techniquementioning

confidence: 99%