Neural modeling of flow rendering effectiveness

Abstract

It has been previously proposed that understanding the mechanisms of contour perception can provide a theory for why some flow rendering methods allow for better judgments of advection pathways than others. In this article, we develop this theory through a numerical model of the primary visual cortex of the brain (Visual Area 1) where contour enhancement is understood to occur according to most neurological theories. We apply a two-stage model of contour perception to various visual representations of flow fields evaluated using the advection task of Laidlaw et al. In the first stage, contour enhancement is modeled based on Li's cortical model. In the second stage, a model of streamline tracing is proposed, designed to support the advection task. We examine the predictive power of the model by comparing its performance to that of human subjects on the advection task with four different visualizations. The results show the same overall pattern for humans and the model. In both cases, the best performance was obtained with an aligned streamline based method, which tied with a LIC-based method. Using a regular or jittered grid of arrows produced worse results. The model yields insights into the relative strengths of different flow visualization methods for the task of visualizing advection pathways.

Improving the Display of Wind Patterns and Ocean Currents

“…Figure 3a suggests that this will not be as effective in stimulating tangential responses as other solutions. This theoretical proposition has been supported both by experiments with humans and by models that computationally simulate the processing of contours in the human visual cortex (Pineo and Ware 2010). Arranging arrows so that they are smoothly aligned (Fig.…”

How Can Deep Neural Networks Aid Visualization Perception Research?

“…Existing research attempts to answer whether an artificial (convolutional) neural network can model a biological visual system [48,50,56,63,[78][79][80]116] (e.g., by comparing the extracted features [18,37,57,94]). These works motivate this research.…”

Visual Reconstructability as a Quality Metric for Flow Visualization

“…We enhance the previous works by integrating reconstruction methods for streamlines, glyph‐based and texture‐based visualization under the same framework for quality evaluation. This enables a qualitative comparison of three widely‐used forms of flow visualization based on reconstructability, whilst only two forms were considered in [PW08, LDM*01, LKJ*05]. Instead of artificial vector fields, we employ real CFD simulations.…”

“…Pineo and Ware [PW08] first made a case that Gabor filters can represent some functions of the human vision system. While we are cautious about the limitation of machine vision algorithms, we also appreciate the existing advances.…”

“…Chen et al [CCK07] reconstructed a vector field from sampled points on the streamlines in a visualization by interpolating values of these points via Delaunay triangulation. Pineo and Ware [PW08] reconstructed and evaluated glyph-based and texture-based flow visualization using Gabor filters. We enhance the previous works by integrating reconstruction methods for streamlines, glyph-based and texture-based visualization under the same framework for quality evaluation.…”