NICE : A Computational solution to close the gap from colour perception to colour categorization

The segmentation of visible electromagnetic radiation into chromatic categories by the human visual system has been extensively studied from a perceptual point of view, resulting in several colour appearance models. However, there is currently a void when it comes to relate these results to the physiological mechanisms that are known to shape the pre-cortical and cortical visual pathway. This work intends to begin to fill this void by proposing a new physiologically plausible model of colour categorization based on Neural Isoresponsive Colour Ellipsoids (NICE) in the cone-contrast space defined by the main directions of the visual signals entering the visual cortex. The model was adjusted to fit psychophysical measures that concentrate on the categorical boundaries and are consistent with the ellipsoidal isoresponse surfaces of visual cortical neurons. By revealing the shape of such categorical colour regions, our measures allow for a more precise and parsimonious description, connecting well-known early visual processing mechanisms to the less understood phenomenon of colour categorization. To test the feasibility of our method we applied it to exemplary images and a popular ground-truth chart obtaining labelling results that are better than those of current state-of-the-art algorithms

Reduction of visual acuity decreases capacity to evaluate radiographic image quality

Aim: To determine the impact of reduced visual acuity on the evaluation of a test object and appendicular radiographs. Methods: Visual acuity was reduced by two different magnitudes using simulation glasses and compared to normal vision (no glasses). During phase one phantom images were produced for the purpose of counting objects by 13 observers and on phase 2 image appraisal of anatomical structures was performed on anonymized radiographic images by 7 observers. The monitors were calibrated (SMPTE RP133 test pattern) and the room lighting was maintained at 7 ±1 lux. Image display and data on grading were managed using ViewDEX (v.2.0) and the area under the visual grading characteristic (AUCVGC) was calculated using VGC Analyzer (v1.0.2). Inferential statistics were calculated using SPSS. Results: For the evaluation of appendicular radiographs the total interpretation time was longer when visual acuity was reduced with 2 pairs of simulation glasses (15.4 versus 8.9 min). Visual grading analysis showed that observers can lose the ability to detect anatomical and contrast differences when they have a simulated visual acuity reduction, being more challenging to differentiate low contrast details. No simulation glasses, compared to 1 pair gives an AUCVGC of 0.302 (0.280, 0.333), that decreases to 0.197 (0.175, 0.223) when using 2 pairs of glasses. Conclusions: Reduced visual acuity has a significant negative impact on the evaluation of test objects and clinical images. Further work is required to test the impact of reduced visual acuity on visual search, technical evaluation of a wider range of images as well as pathology detection/characterization performance

Human-Centered Object-Based Image Retrieval

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