Predicting Detection Performance on Security X-Ray Images as a Function of Image Quality

Gupta, P. K. Sen; Sinno, Zeina; Glover, Jack L.; Paulter, Nicholas G.; Bovik, Alan C.

doi:10.1109/tip.2019.2896488

Cited by 18 publications

(17 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…We also demonstrated in [57] that QUIX features and IQIs supply complementary information, which when combined into a single predictor performed better than either of them in isolation. Under a similar premise, we augmented MVGCN-X-ray features with IQIs to obtain similar benefits in performance.…”

Section: Featurementioning

confidence: 70%

“…In previous work, we studied the natural scene statistics (NSS) of X-ray images and found that the NSS modeling paradigm applies quite well to X-ray image data, although the model is somewhat different from that of visible light (VL) images [23,57]. In prior work, we used a nominal set of X-ray NSS features along with standardized objective image quality indicators (IQIs) to analyze the relationship between X-ray image quality and the task performance of professional bomb technicians who were asked to detect and identify a collection of diverse potential threat objects.…”

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

“…The models used for comparison are the quality inspectors of X-ray images (QUIX) model [57], the IEEE/ANSI N42.55 standard [60] and combinations of these. QUIX features are a set of simple and efficient NSS-based perceptual quality features that accurately predict human task performance.…”

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

“…QUIX features are a set of simple and efficient NSS-based perceptual quality features that accurately predict human task performance. In [57], QUIX considers only horizontal and vertical correlations while extracting features denoted as 'pp' features. In order to be consistent and to have a fair comparison against QUIX, we developed a reduced feature version of MVGCN, which we refer to as MVGCN-X-ray, which does not include the products of diagonal coefficients as part of the paired-product modeling and corresponding MVGG fits.…”

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

“…We then devised a binary classification framework whereby features were mapped to a binary variable indicating whether the component was successfully identified by an expert. We used a logistic regression model to be consistent with [ 57 ]. The data from each component-clutter category was divided into an 80 % training set to learn logistic function parameters, which were then used to predict on the remaining 20 % test set.…”

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

See 4 more Smart Citations

Multivariate Statistical Approach to Image Quality Tasks

et al. 2018

Self Cite

View full text Add to dashboard Cite

Many existing natural scene statistics-based no reference image quality assessment (NR IQA) algorithms employ univariate parametric distributions to capture the statistical inconsistencies of bandpass distorted image coefficients. Here, we propose a multivariate model of natural image coefficients expressed in the bandpass spatial domain that has the potential to capture higher order correlations that may be induced by the presence of distortions. We analyze how the parameters of the multivariate model are affected by different distortion types, and we show their ability to capture distortion-sensitive image quality information. We also demonstrate the violation of Gaussianity assumptions that occur when locally estimating the energies of distorted image coefficients. Thus, we propose a generalized Gaussian-based local contrast estimator as a way to implement non-linear local gain control, which facilitates the accurate modeling of both pristine and distorted images. We integrate the novel approach of generalized contrast normalization with multivariate modeling of bandpass image coefficients into a holistic NR IQA model, which we refer to as multivariate generalized contrast normalization (MVGCN). We demonstrate the improved performance of MVGCN on quality-relevant tasks on multiple imaging modalities, including visible light image quality prediction and task success prediction on distorted X-ray images.

show abstract

Section: Featurementioning

confidence: 70%

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

Section: Predicting Detection Performance On X-ray Imagesmentioning

confidence: 99%

See 3 more Smart Citations