Occlusion, transparency, and lightness

Simple Assumptions to Improve Markov Illuminance and Reflectance

Murray recently introduced a novel computational lightness model, Markov illuminance and reflectance (MIR). MIR is a promising new approach that simulates human lightness processing using a conditional random field (CRF) where natural-scene statistics of reflectance and illumination are implemented. Although MIR can account for various lightness illusions and phenomena, it has limitations, such as the inability to predict reverse-contrast phenomena. In this study, we improved MIR performance by modifying its inference process, the prior on X-junctions, and that on general illumination changes. Our modified model improved predictions for Checkerboard assimilation, the simplified Checkershadow and its control figure, the influence of luminance noise, and White's effect and its several variants. In particular, White's effect is a partial reverse contrast that is challenging for computational models, so this improvement is a significant advance for the MIR framework. This study showed the high extensibility and potential of MIR, which shows the promise for further sophistication.

Proceedings of the 2017 National Toxicology Program Satellite Symposium

The 2017 annual National Toxicology Program Satellite Symposium, entitled "Pathology Potpourri," was held in Montreal, Quebec, Canada at the Society of Toxicologic Pathology's 36th annual meeting. The goal of this symposium was to present and discuss challenging diagnostic pathology and/or nomenclature issues. This article presents summaries of the speakers' talks along with select images that were used by the audience for voting and discussion. Various lesions and other topics covered during the symposium included renal papillary degeneration in perinatally exposed animals, an atriocaval mesothelioma, an unusual presentation of an alveolar-bronchiolar carcinoma, a paraganglioma of the organ of Zuckerkandl (also called an extra-adrenal pheochromocytoma), the use of human muscle samples to illustrate the challenges of manual scoring of fluorescent staining, intertubular spermatocytic seminomas, medical device pathology assessment and discussion of the approval process, collagen-induced arthritis, incisor denticles, ameloblast degeneration and poorly mineralized enamel matrix, connective tissue paragangliomas, microcystin-LR toxicity, perivascular mast cells in the forebrain thalamus unrelated to treatment, and 2 cases that provided a review of the International Harmonization of Nomenclature and Diagnostic Criteria (INHAND) bone nomenclature and recommended application of the terminology in routine nonclinical toxicity studies.

The Gold Standard Paradox in Digital Image Analysis: Manual Versus Automated Scoring as Ground Truth

CONTEXT

- Novel therapeutics often target complex cellular mechanisms. Increasingly, quantitative methods like digital tissue image analysis (tIA) are required to evaluate correspondingly complex biomarkers to elucidate subtle phenotypes that can inform treatment decisions with these targeted therapies. These tIA systems need a gold standard, or reference method, to establish analytical validity. Conventional, subjective histopathologic scores assigned by an experienced pathologist are the gold standard in anatomic pathology and are an attractive reference method. The pathologist's score can establish the ground truth to assess a tIA solution's analytical performance. The paradox of this validation strategy, however, is that tIA is often used to assist pathologists to score complex biomarkers because it is more objective and reproducible than manual evaluation alone by overcoming known biases in a human's visual evaluation of tissue, and because it can generate endpoints that cannot be generated by a human observer.

OBJECTIVE

- To discuss common visual and cognitive traps known in traditional pathology-based scoring paradigms that may impact characterization of tIA-assisted scoring accuracy, sensitivity, and specificity.

DATA SOURCES

- This manuscript reviews the current literature from the past decades available for traditional subjective pathology scoring paradigms and known cognitive and visual traps relevant to these scoring paradigms.

CONCLUSIONS

- Awareness of the gold standard paradox is necessary when using traditional pathologist scores to analytically validate a tIA tool because image analysis is used specifically to overcome known sources of bias in visual assessment of tissue sections.

The roles of image decomposition and edge curvature in the 'snake' lightness illusion

The snake illusion is an effect in which the lightness of target patches is strongly affected by the luminance of remote patches. One explanation is that such images are decomposed into a pattern of illumination and a pattern of reflectance, involving a classification of luminance edges into illumination and reflectance edges. Based on this decomposition, perceived reflectance is determined by discounting the illumination. A problem for this account is that image decomposition is not unique, and that different decompositions may lead to different lightness predictions. One way to rule out alternative decompositions and ensure correct predictions is to postulate that the visual system tends to classify curved luminance edges as reflectance edges rather than illumination edges. We have constructed several variations of the basic snake display in order to test the proposed curvature constraint and the more general image decomposition hypothesis. Although the results from some displays have confirmed previous findings of the effect of curvature, the general pattern of data questions the relevance of the shape of luminance edges for the determination of lightness in this class of displays. The data also argue against an image decomposition mechanism as an explanation of this effect. As an alternative, a tentative neurally based account is sketched.

Smart transparency for illustrative visualization of complex flow surfaces

The perception of transparency and the underlying neural mechanisms have been subject to extensive research in the cognitive sciences. However, we have yet to develop visualization techniques that optimally convey the inner structure of complex transparent shapes. In this paper, we apply the findings of perception research to develop a novel illustrative rendering method that enhances surface transparency nonlocally. Rendering of transparent geometry is computationally expensive since many optimizations, such as visibility culling, are not applicable and fragments have to be sorted by depth for correct blending. In order to overcome these difficulties efficiently, we propose the illustration buffer. This novel data structure combines the ideas of the A and G-buffers to store a list of all surface layers for each pixel. A set of local and nonlocal operators is then used to process these depth-lists to generate the final image. Our technique is interactive on current graphics hardware and is only limited by the available graphics memory. Based on this framework, we present an efficient algorithm for a nonlocal transparency enhancement that creates expressive renderings of transparent surfaces. A controlled quantitative double blind user study shows that the presented approach improves the understanding of complex transparent surfaces significantly.

The Anderson-Winawer illusion: it's not occlusion

In their recent article, Anderson and Winawer (2005) presented a dramatic lightness illusion in which identical texture patches appear to be either black or white. Albert (2007) argued that the Anderson and Winawer (2005) illusion can be explained by a simple theory in which occlusion cues determine the depth relationships of the different surfaces, and determine which stimulus areas are perceived as seen in plain view. Using both modeling and psychophysical methods, however, I show that alterations such as those that Albert used actually reverse the illusion within the range of figure contrasts that Anderson and Winawer (2005) tested. Albert's theory (and any occlusion-based theory), therefore, cannot account for Anderson and Winawer's (2005) data, at least in the lower figure-contrast range. I propose a novel scene-interpretation strategy to account for the effects.