Traditionally, the appearance of an object in an image is edited to elicit a preferred perception. However, the editing method might be arbitrary and might not consider the human perception mechanism. In this study, the authors explored image-based leather “authenticity” editing using an estimation model that considers a perception mechanism derived in their previous work. They created leather rendered images by emphasizing or suppressing image properties corresponding to the “authenticity.” Subsequently, they performed two subjective experiments, one using fully edited images and another using partially edited images whose specular reflection intensity was constant. Participants observed the leather rendered images and evaluated the differences in the perception of “authenticity.” The authors found that the “authenticity” perception could be changed by manipulating the intensity of specular reflection and the texture (grain and surface irregularity) in the images. The results of this study could be used to tune the properties of images to make them more appealing.

Multi-modal image fusion can more accurately describe the features of a scene than a single image. Because of the different imaging mechanisms, the difference between multi-modal images is great, which leads to poor contrast of the fused images. Therefore, a simple and effective spatial domain fusion algorithm based on variable parameter fractional difference enhancement is proposed. Based on the characteristics of fractional difference enhancement, a variable parameter fractional difference is introduced, the multi-modal images are repeatedly enhanced, and multiple enhanced images are obtained. A correlation coefficient is applied to constrain the number of enhancement cycles. In addition, an energy contrast is used to extract the contrast features of the image, and the tangent function is simultaneously used to obtain the fusion weight to attain multiple contrast-enhanced initialization fusion images. Finally, the weighted average is applied to obtain the final fused image. Experimental results demonstrate that the proposed fusion algorithm can effectively preserve the contrast features between images and improve the quality of fused images.

Tim’s Vermeer is a recent documentary feature film following engineer and self-described non-artist Tim Jenison’s extensive efforts to “paint a Vermeer” by means of a novel optical telescope and mirror-comparator procedure. His efforts were inspired by the controversial claim that some Western painters as early as 1420 secretly built optical devices and traced passages in projected images during the execution of some of their works, thereby achieving a novel and compelling “optical look.” The authors examine the proposed telescope optics in historical perspective, the particular visual evidence adduced in support of the comparator hypothesis, and the difficulty and efficacy of the mirror-comparator procedure as revealed by an independent artist/copyist’s attempts to replicate the procedure. Specifically, the authors find that the luminance gradient along the rear wall in the duplicate painting is far from being rare, difficult, or even “impossible” to achieve as proponents claimed; in fact, such gradients appear in numerous Old Master paintings that show no ancillary evidence of having been executed with optics. There is indeed a slight bowing of a single contour in the Vermeer original, which one would normally expect to be straight; however, the optical explanation for this bowing implies that numerous other lines would be similarly bowed, but in fact all are straight. The proposed method does not explain some of the most compelling “optical” evidence in Vermeer’s works such as the small disk-shaped highlights, which appear like the blur spots that arise in an out-of-focus projected image. Likewise, the comparator-based explanations for the presence of pinprick holes at central vanishing points and the presence of underdrawings and pentimenti in several of Vermeer’s works have more plausible non-optical explanations. Finally, an independent experimental attempt to replicate the procedure fails overall to provide support for the telescope claim. In light of these considerations and evidence, the authors conclude that it is extremely unlikely that Vermeer used the proposed mirror-comparator procedure.

Photogrammetric three-dimensional (3D) reconstruction is an image processing technique used to develop digital 3D models from a series of two-dimensional images. This technique is commonly applied to optical photography though it can also be applied to microscopic imaging techniques such as scanning electron microscopy (SEM). The authors propose a method for the application of photogrammetry techniques to SEM micrographs in order to develop 3D models suitable for volumetric analysis. SEM operating parameters for image acquisition are explored and the relative effects discussed. This study considered a variety of microscopic samples, differing in size, geometry and composition, and found that optimal operating parameters vary with sample geometry. Evaluation of reconstructed 3D models suggests that the quality of the models strongly determines the accuracy of the volumetric measurements obtainable. In particular, they report on volumetric results achieved from a laser ablation pit and discuss considerations for data acquisition routines.

The authors introduce an integrative approach for the analysis of the high-dimensional parameter space relevant for decision-making in the context of quality control. Typically, a large number of parameters influence the quality of a manufactured part in an assembly process, and our approach supports the visual exploration and comprehension of the correlations among various parameters and their effects on part quality. We combine visualization and machine learning methods to help a user with the identification of important parameter value settings having certain effects on a part. The goal to understand the influence of parameter values on part quality is treated from a reverse engineering perspective, driven by the goal to determine what values cause what effects on part quality. The high-dimensional parameter value domain generally cannot be visualized directly, and the authors employ dimension reduction techniques to address this problem. Their prototype system makes possible the identification of regions in a high-dimensional parameter value space that lead to desirable (or non-desirable) parameter value settings for quality assurance. They demonstrate the validity and effectiveness of our methods and prototype by applying them to a sheet metal deformation example.

It is difficult to describe facial skin color through a solid color as it varies from region to region. In this article, the authors utilized image analysis to identify the facial color representative region. A total of 1052 female images from Humanae project were selected as a solid color was generated for each image as their representative skin colors by the photographer. Using the open CV-based libraries, such as EOS of Surrey Face Models and DeepFace, 3448 facial landmarks together with gender and race information were detected. For an illustrative and intuitive analysis, they then re-defined 27 visually important sub-regions to cluster the landmarks. The 27 sub-region colors for each image were finally derived and recorded in L^∗, a^∗, and b^∗. By estimating the color difference among representative color and 27 sub-regions, we discovered that sub-regions of below lips (low Labial) and central cheeks (upper Buccal) were the most representative regions across four major ethnicity groups. In future study, the methodology is expected to be applied for more image sources.

In this article, the authors present an image processing method to reduce three-dimensional (3D) crosstalk for eye-tracking-based 3D display. Specifically, they considered 3D pixel crosstalk and offset crosstalk and applied different approaches based on its characteristics. For 3D pixel crosstalk which depends on the viewer’s relative location, they proposed output pixel value weighting scheme based on viewer’s eye position, and for offset crosstalk they subtracted luminance of crosstalk components according to the measured display crosstalk level in advance. By simulations and experiments using the 3D display prototypes, the authors evaluated the effectiveness of proposed method.

Evaluating the utility of polarimetric imaging for material identification, as compared to conventional irradiance imaging, motivates this work. Images of diffuse objects captured with a wide field of view Mueller matrix polarimeter are used to demonstrate a classification and measurement optimization method. This imaging study is designed to test polarimetric utility in discriminating white fabric from white wood. The material color is constrained to be similar so that classification from only total radiance imaging is difficult, i.e., metamerism. A statistical divergence between two distributions of measured intensity is used to optimize the Polarization State Generator (PSG) and the Polarization State Analyzer (PSA) given two classes of Mueller matrices. The classification performance as a function of number of polarimetric measurements is computed. This work demonstrates that two polarimetric measurements of white fabric and white wood offer nearly perfect classification. The utility and design of partial Mueller imaging is supported by this optimization of PSG/PSA states and number of measurements.

The performance of a convolutional neural network (CNN) on an image texture detection task as a function of linear image processing and the number of training images is investigated. Performance is quantified by the area under (AUC) the receiver operating characteristic (ROC) curve. The Ideal Observer (IO) maximizes AUC but depends on high-dimensional image likelihoods. In many cases, the CNN performance can approximate the IO performance. This work demonstrates counterexamples where a full-rank linear transform degrades the CNN performance below the IO in the limit of large quantities of training data and network layers. A subsequent linear transform changes the images’ correlation structure, improves the AUC, and again demonstrates the CNN dependence on linear processing. Compression strictly decreases or maintains the IO detection performance while compression can increase the CNN performance especially for small quantities of training data. Results indicate an optimal compression ratio for the CNN based on task difficulty, compression method, and number of training images.