Human Visual System

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Sos S Agaian - One of the best experts on this subject based on the ideXlab platform.

  • autonomous facial recognition System inspired by Human Visual System based logarithmical image Visualization technique
    Mobile Multimedia Image Processing Security and Applications 2017, 2017
    Co-Authors: Qianwen Wan, Karen Panetta, Sos S Agaian
    Abstract:

    Autonomous facial recognition System is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition Systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition System inspired by the Human Visual System based, so called, logarithmical image Visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image Visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition System. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method’s efficiency, accuracy, and robustness of illumination invariance for facial recognition.

  • Human Visual System inspired underwater image quality measures
    IEEE Journal of Oceanic Engineering, 2016
    Co-Authors: Karen Panetta, Chen Gao, Sos S Agaian
    Abstract:

    Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the Visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to Human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of Human Visual Systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the Human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

  • autonomous facial recognition based on the Human Visual System
    International Conference on Imaging Systems and Techniques, 2015
    Co-Authors: Qianwen Wan, Karen Panetta, Sos S Agaian
    Abstract:

    This paper presents a real-time facial recognition System utilizing our Human Visual System algorithms coupled with logarithm Logical Binary Pattern feature descriptors and our region weighted model. The architecture can quickly find and rank the closest matches of a test image to a database of stored images. There are many potential applications for this work, including homeland security applications such as identifying persons of interest and other robot vision applications such as search and rescue missions. This new method significantly improves the performance of the previous Local Binary Pattern method. For our prototype application, we supplied the System testing images and found their best matches in the database of training images. In addition, the results were further improved by weighting the contribution of the most distinctive facial features. The System evaluates and selects the best matching image using the chi-squared statistic.

  • Human Visual System based mammogram enhancement and analysis
    International Conference on Image Processing, 2010
    Co-Authors: Yicong Zhou, Karen Panetta, Sos S Agaian
    Abstract:

    This paper introduces a new mammogram enhancement algorithm using the Human Visual System (HVS) based image decomposition. A new enhancement measure based on the second derivative is also introduced to measure and assess the enhancement performance. Experimental results show that the presented algorithm can improve the Visual quality of fine details in mammograms. The HVS-based image decomposition can segment the regions/objects from their surroundings. It offers the users flexibility to enhance either sub-images containing only significant illumination information or all the sub-images of the original mammograms. The algorithm can be used in the computer-aided diagnosis Systems for breast cancer detection.

  • Human Visual System based similarity metrics
    Systems Man and Cybernetics, 2008
    Co-Authors: Eric J Wharton, Karen Panetta, Sos S Agaian
    Abstract:

    Objective assessment of image quality is important for a number of image processing applications. Similarity metrics have been used for methods such as automating compression, automating watermarking, and benchmarking algorithm success. The goal of objective quality assessment is to quantify the quality of images in a manner consistent with Human perception. For this reason, we introduce a novel image similarity metric based on the Human Visual System. The measures of enhancement (EME, AME, and LogAME) have been successfully used to quantify Human quality perception for image enhancement. In this paper, we present a modified version of the Logarithmic AME which can successfully be used to quantify image similarity. We compare the quantitative assessments of this algorithm with those of the well known Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) on the basis of correlation with subjective Human evaluations for a number of images.

Jinhui Han - One of the best experts on this subject based on the ideXlab platform.

  • an infrared small target detecting algorithm based on Human Visual System
    IEEE Geoscience and Remote Sensing Letters, 2016
    Co-Authors: Jinhui Han, Jun Huang, Xiaoguang Mei
    Abstract:

    Infrared (IR) small target detection with high detection rate, low false alarm rate, and multiscale detection ability is a challenging task since raw IR images usually have low contrast and complex background. In recent years, robust Human Visual System (HVS) properties have been introduced into the IR small target detection field. However, existing algorithms based on HVS, such as difference of Gaussians (DoG) filters, are sensitive to not only real small targets but also background edges, which results in a high false alarm rate. In this letter, the difference of Gabor (DoGb) filters is proposed and improved (IDoGb), which is an extension of DoG but is sensitive to orientations and can better suppress the complex background edges, then achieves a lower false alarm rate. In addition, multiscale detection can be also achieved. Experimental results show that the IDoGb filter produces less false alarms at the same detection rate, while consuming only about 0.1 s for a single frame.

  • a robust infrared small target detection algorithm based on Human Visual System
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Jinhui Han, Bo Zhou, Fan Fan, Kun Liang, Yu Fang
    Abstract:

    Robust Human Visual System (HVS) properties can effectively improve the infrared (IR) small target detection capabilities, such as detection rate, false alarm rate, speed, etc. However, current algorithms based on HVS usually improve one or two of the aforementioned detection capabilities while sacrificing the others. In this letter, a robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously. First, an HVS size-adaptation process is used, and the IR image after preprocessing is divided into subblocks to improve detection speed. Then, based on HVS contrast mechanism, the improved local contrast measure, which can improve detection rate and reduce false alarm rate, is proposed to calculate the saliency map, and a threshold operation along with a rapid traversal mechanism based on HVS attention shift mechanism is used to get the target subblocks quickly. Experimental results show the proposed algorithm has good robustness and efficiency for real IR small target detection applications.

Karen Panetta - One of the best experts on this subject based on the ideXlab platform.

  • autonomous facial recognition System inspired by Human Visual System based logarithmical image Visualization technique
    Mobile Multimedia Image Processing Security and Applications 2017, 2017
    Co-Authors: Qianwen Wan, Karen Panetta, Sos S Agaian
    Abstract:

    Autonomous facial recognition System is widely used in real-life applications, such as homeland border security, law enforcement identification and authentication, and video-based surveillance analysis. Issues like low image quality, non-uniform illumination as well as variations in poses and facial expressions can impair the performance of recognition Systems. To address the non-uniform illumination challenge, we present a novel robust autonomous facial recognition System inspired by the Human Visual System based, so called, logarithmical image Visualization technique. In this paper, the proposed method, for the first time, utilizes the logarithmical image Visualization technique coupled with the local binary pattern to perform discriminative feature extraction for facial recognition System. The Yale database, the Yale-B database and the ATT database are used for computer simulation accuracy and efficiency testing. The extensive computer simulation demonstrates the method’s efficiency, accuracy, and robustness of illumination invariance for facial recognition.

  • Human Visual System inspired underwater image quality measures
    IEEE Journal of Oceanic Engineering, 2016
    Co-Authors: Karen Panetta, Chen Gao, Sos S Agaian
    Abstract:

    Underwater images suffer from blurring effects, low contrast, and grayed out colors due to the absorption and scattering effects under the water. Many image enhancement algorithms for improving the Visual quality of underwater images have been developed. Unfortunately, no well-accepted objective measure exists that can evaluate the quality of underwater images similar to Human perception. Predominant underwater image processing algorithms use either a subjective evaluation, which is time consuming and biased, or a generic image quality measure, which fails to consider the properties of underwater images. To address this problem, a new nonreference underwater image quality measure (UIQM) is presented in this paper. The UIQM comprises three underwater image attribute measures: the underwater image colorfulness measure (UICM), the underwater image sharpness measure (UISM), and the underwater image contrast measure (UIConM). Each attribute is selected for evaluating one aspect of the underwater image degradation, and each presented attribute measure is inspired by the properties of Human Visual Systems (HVSs). The experimental results demonstrate that the measures effectively evaluate the underwater image quality in accordance with the Human perceptions. These measures are also used on the AirAsia 8501 wreckage images to show their importance in practical applications.

  • autonomous facial recognition based on the Human Visual System
    International Conference on Imaging Systems and Techniques, 2015
    Co-Authors: Qianwen Wan, Karen Panetta, Sos S Agaian
    Abstract:

    This paper presents a real-time facial recognition System utilizing our Human Visual System algorithms coupled with logarithm Logical Binary Pattern feature descriptors and our region weighted model. The architecture can quickly find and rank the closest matches of a test image to a database of stored images. There are many potential applications for this work, including homeland security applications such as identifying persons of interest and other robot vision applications such as search and rescue missions. This new method significantly improves the performance of the previous Local Binary Pattern method. For our prototype application, we supplied the System testing images and found their best matches in the database of training images. In addition, the results were further improved by weighting the contribution of the most distinctive facial features. The System evaluates and selects the best matching image using the chi-squared statistic.

  • Non-Linear Direct Multi-Scale Image Enhancement Based on the Luminance and Contrast Masking Characteristics of the Human Visual System
    IEEE Transactions on Image Processing, 2013
    Co-Authors: Shahan Nercessian, Karen Panetta, Sos Agaian
    Abstract:

    Image enhancement is a crucial pre-processing step for various image processing applications and vision Systems. Many enhancement algorithms have been proposed based on different sets of criteria. However, a direct multi-scale image enhancement algorithm capable of independently and/or simultaneously providing adequate contrast enhancement, tonal rendition, dynamic range compression, and accurate edge preservation in a controlled manner has yet to be produced. In this paper, a multi-scale image enhancement algorithm based on a new parametric contrast measure is presented. The parametric contrast measure incorporates not only the luminance masking characteristic, but also the contrast masking characteristic of the Human Visual System. The formulation of the contrast measure can be adapted for any multi-resolution decomposition scheme in order to yield new Human Visual System-inspired multi-scale transforms. In this article, it is exemplified using the Laplacian pyramid, discrete wavelet transform, stationary wavelet transform, and dual-tree complex wavelet transform. Consequently, the proposed enhancement procedure is developed. The advantages of the proposed method include: 1) the integration of both the luminance and contrast masking phenomena; 2) the extension of non-linear mapping schemes to Human Visual System inspired multi-scale contrast coefficients; 3) the extension of Human Visual System-based image enhancement approaches to the stationary and dual-tree complex wavelet transforms, and a direct means of; 4) adjusting overall brightness; and 5) achieving dynamic range compression for image enhancement within a direct multi-scale enhancement framework. Experimental results demonstrate the ability of the proposed algorithm to achieve simultaneous local and global enhancements.

  • Human Visual System based mammogram enhancement and analysis
    International Conference on Image Processing, 2010
    Co-Authors: Yicong Zhou, Karen Panetta, Sos S Agaian
    Abstract:

    This paper introduces a new mammogram enhancement algorithm using the Human Visual System (HVS) based image decomposition. A new enhancement measure based on the second derivative is also introduced to measure and assess the enhancement performance. Experimental results show that the presented algorithm can improve the Visual quality of fine details in mammograms. The HVS-based image decomposition can segment the regions/objects from their surroundings. It offers the users flexibility to enhance either sub-images containing only significant illumination information or all the sub-images of the original mammograms. The algorithm can be used in the computer-aided diagnosis Systems for breast cancer detection.

Yu Fang - One of the best experts on this subject based on the ideXlab platform.

  • a robust infrared small target detection algorithm based on Human Visual System
    IEEE Geoscience and Remote Sensing Letters, 2014
    Co-Authors: Jinhui Han, Bo Zhou, Fan Fan, Kun Liang, Yu Fang
    Abstract:

    Robust Human Visual System (HVS) properties can effectively improve the infrared (IR) small target detection capabilities, such as detection rate, false alarm rate, speed, etc. However, current algorithms based on HVS usually improve one or two of the aforementioned detection capabilities while sacrificing the others. In this letter, a robust IR small target detection algorithm based on HVS is proposed to pursue good performance in detection rate, false alarm rate, and speed simultaneously. First, an HVS size-adaptation process is used, and the IR image after preprocessing is divided into subblocks to improve detection speed. Then, based on HVS contrast mechanism, the improved local contrast measure, which can improve detection rate and reduce false alarm rate, is proposed to calculate the saliency map, and a threshold operation along with a rapid traversal mechanism based on HVS attention shift mechanism is used to get the target subblocks quickly. Experimental results show the proposed algorithm has good robustness and efficiency for real IR small target detection applications.

Wouter J H Veldkamp - One of the best experts on this subject based on the ideXlab platform.

  • can the channelized hotelling observer including aspects of the Human Visual System predict Human observer performance in mammography
    Physica Medica, 2017
    Co-Authors: Ramona W Bouwman, M Goffi, R E Van Engen, Mireille J M Broeders, David R Dance, Kenneth C Young, Wouter J H Veldkamp
    Abstract:

    Abstract Purpose In mammography, images are processed prior to display. Model observers (MO) are candidates to objectively evaluate processed images if they can predict Human observer performance for detail detection. The aim of this study was to investigate if the channelized Hotelling observer (CHO) can be configured to predict Human observer performance in mammography like images. Methods The performance correlation between Human observers and CHO has been evaluated using different channel-sets and by including aspects of the Human Visual System (HVS). The correlation was investigated for the detection of disk-shaped details in simulated white noise (WN) and clustered lumpy backgrounds (CLB) images, representing respectively quantum noise limited and mammography like images. The images were scored by the MO and five Human observers in 2-alternative forced choice experiments. Results For WN images the most useful formulation of the CHO to predict Human observer performance was obtained using three difference of Gaussian channels without adding HVS aspects (R LR 2  = 0.62). For CLB images the most useful formulation was the partial least square channel-set without adding HVS aspects (R LR 2  = 0.71). The correlation was affected by detail size and background. Conclusions This study has shown that the CHO can predict Human observer performance. Due to object size and background dependency it is important that the range of object sizes and allowed variability in background are specified and validated carefully before the CHO can be implemented for objective image quality assessment.

  • can the non pre whitening model observer including aspects of the Human Visual System predict Human observer performance in mammography
    Physica Medica, 2016
    Co-Authors: Ramona W Bouwman, R E Van Engen, Mireille J M Broeders, David R Dance, Kenneth C Young, Wouter J H Veldkamp, G Den J Heeten
    Abstract:

    Abstract Purpose In mammography, images are processed prior to display. Current methodologies based on physical image quality measurements are however not designed for the evaluation of processed images. Model observers (MO) might be suitable for this evaluation. The aim of this study was to investigate whether the non-pre-whitening (NPW) MO can be used to predict Human observer performance in mammography-like images by including different aspects of the Human Visual System (HVS). Methods The correlation between Human and NPW MO performance has been investigated for the detection of disk shaped objects in simulated white noise (WN) and clustered lumpy backgrounds (CLB), representing quantum noise limited and mammography-like images respectively. The images were scored by the MO and five Human observers in a 2-alternative forced choice experiment. Results For WN images it was found that the log likelihood ratio (R LR 2 ), which expresses the goodness of fit, was highest (0.44) for the NPW MO without addition of HVS aspects. For CLB the R LR 2 improved from 0.46 to 0.65 with addition of HVS aspects. The correlation was affected by object size and background. Conclusions This study shows that by including aspects of the HVS, the performance of the NPW MO can be improved to better predict Human observer performance. This demonstrates that the NPW MO has potential for image quality assessment. However, due to the dependencies found in the correlation, the NPW MO can only be used for image quality assessment for a limited range of object sizes and background variability.