scholarly journals Where to Draw The Line?

Author(s):  
Heping Sheng ◽  
John Wilder ◽  
Dirk B. Walther

Abstract We often take people’s ability to understand and produce line drawings for granted. But where should we draw lines, and why? We address fundamental principles that underlie efficient representations of complex information in line drawings. First, 58 participants with varying degree of artistic experience produced multiple drawings of a small set of scenes by tracing contours on a digital tablet. Second, 37 independent observers ranked the drawings by how representative they are of the original photograph. Overall, artists’ drawings ranked higher than non-artists’. Matching contours between drawings of the same scene revealed that the most consistently drawn contours tend to be drawn earlier. We generated half-images with the most-versus least-consistently drawn contours by sorting contours by their consistency scores. Twenty five observers performed significantly better in a fast scene categorization task for the most compared to the least consistent half-images. The most consistent contours were longer and more likely to depict occlusion boundaries. Using psychophysics experiments and computational analysis, we confirmed quantitatively what makes certain contours in line drawings special: longer contours mark occlusion boundaries and aid rapid scene recognition. They allow artist and non-artists to convey important information starting from the first few strokes in their drawing process.

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258376
Author(s):  
Heping Sheng ◽  
John Wilder ◽  
Dirk B. Walther

We often take people’s ability to understand and produce line drawings for granted. But where should we draw lines, and why? We address psychological principles that underlie efficient representations of complex information in line drawings. First, 58 participants with varying degree of artistic experience produced multiple drawings of a small set of scenes by tracing contours on a digital tablet. Second, 37 independent observers ranked the drawings by how representative they are of the original photograph. Matching contours between drawings of the same scene revealed that the most consistently drawn contours tend to be drawn earlier. We generated half-images with the most- versus least-consistently drawn contours and asked 25 observers categorize the quickly presented scenes. Observers performed significantly better for the most compared to the least consistent half-images. The most consistently drawn contours were more likely to depict occlusion boundaries, whereas the least consistently drawn contours frequently depicted surface normals.


2013 ◽  
Vol 25 (6) ◽  
pp. 961-968 ◽  
Author(s):  
Rachel E. Ganaden ◽  
Caitlin R. Mullin ◽  
Jennifer K. E. Steeves

Traditionally, it has been theorized that the human visual system identifies and classifies scenes in an object-centered approach, such that scene recognition can only occur once key objects within a scene are identified. Recent research points toward an alternative approach, suggesting that the global image features of a scene are sufficient for the recognition and categorization of a scene. We have previously shown that disrupting object processing with repetitive TMS to object-selective cortex enhances scene processing possibly through a release of inhibitory mechanisms between object and scene pathways [Mullin, C. R., & Steeves, J. K. E. TMS to the lateral occipital cortex disrupts object processing but facilitates scene processing. Journal of Cognitive Neuroscience, 23, 4174–4184, 2011]. Here we show the effects of TMS to the transverse occipital sulcus (TOS), an area implicated in scene perception, on scene and object processing. TMS was delivered to the TOS or the vertex (control site) while participants performed an object and scene natural/nonnatural categorization task. Transiently interrupting the TOS resulted in significantly lower accuracies for scene categorization compared with control conditions. This demonstrates a causal role of the TOS in scene processing and indicates its importance, in addition to the parahippocampal place area and retrosplenial cortex, in the scene processing network. Unlike TMS to object-selective cortex, which facilitates scene categorization, disrupting scene processing through stimulation of the TOS did not affect object categorization. Further analysis revealed a higher proportion of errors for nonnatural scenes that led us to speculate that the TOS may be involved in processing the higher spatial frequency content of a scene. This supports a nonhierarchical model of scene recognition.


2017 ◽  
Vol 08 ◽  
Author(s):  
Qiufang Fu ◽  
Yong-Jin Liu ◽  
Zoltan Dienes ◽  
Jianhui Wu ◽  
Wenfeng Chen ◽  
...  

2014 ◽  
Vol 25 (6) ◽  
pp. 1561-1572 ◽  
Author(s):  
Shan-shan Zhu ◽  
Nelson H. C. Yung

2021 ◽  
Author(s):  
Mathias Sablé-Meyer ◽  
Kevin Ellis ◽  
Joshua Tenenbaum ◽  
Stanislas Dehaene

Why do geometric shapes such as lines, circles, zig-zags or spirals appear in all human cultures, but are never produced by other animals? Here, we formalize and test the hypothesis that all humans possess a compositional language of thought that can produce line drawings as recursive combinations of a minimal set of geometric primitives. We present a programming language, similar to Logo, that combines discrete numbers and continuous integration in higher-level structures based on repetition, concatenation and embedding, and show that the simplest programs in this language generate the fundamental geometric shapes observed in human cultures. On the perceptual side, we propose that shape perception in humans involves searching for the shortest program that correctly draws the image (program induction). A consequence of this framework is that the mental difficulty of remembering a shape should depend on its minimum description length (MDL) in the proposed language. In two experiments, we show that encoding and processing of geometric shapes is well predicted by MDL. Furthermore, our hypotheses predict additive laws for the psychological complexity of repeated, concatenated or embedded shapes, which are experimentally validated.


2018 ◽  
Author(s):  
Jiri Lukavsky

Humans display a very good understanding of the content in briefly presented photographs. To achieve this understanding, humans rely on information from both high-acuity central vision and peripheral vision. Previous studies have investigated the relative contribution of central/peripheral vision. However, the role of attention in this task remains unclear. In this study, we presented composite images with a scene in the center and another scene in the periphery. The two channels conveyed different information, and the participants were asked to focus on one channel while ignoring the other. In two experiments, we showed that (1) people are better at recognizing the central part, (2) the conflicting signal in the ignored part hinders performance, and (3) this effect is true for both parts (focusing on the central or peripheral part). We conclude that scene recognition is based on both central and peripheral information, even when participants are instructed to focus only on one part of the image and ignore the other. In contrast to the zoom-out hypothesis, we propose that the gist recognition process should be interpreted in terms of the evidence accumulation model in which information from the to-be-ignored parts is also included.


Author(s):  
Shuang Bai

Objects in scenes are thought to be important for scene recognition. In this paper, we propose to utilize scene-specific objects represented by deep features for scene categorization. Our approach combines benefits of deep learning and Latent Support Vector Machine (LSVM) to train a set of scene-specific object models for each scene category. Specifically, we first use deep Convolutional Neural Networks (CNNs) pre-trained on the large-scale object-centric image database ImageNet to learn rich object features and a large number of general object concepts. Then, the pre-trained CNNs is adopted to extract features from images in the target dataset, and initialize the learning of scene-specific object models for each scene category. After initialization, the scene-specific object models are obtained by alternating between searching over the most representative and discriminative regions of images in the target dataset and training linear SVM classifiers based on obtained region features. As a result, for each scene category a set of object models that are representative and discriminative can be acquired. We use them to perform scene categorization. In addition, to utilize global structure information of scenes, we use another CNNs pre-trained on the large-scale scene-centric database Places to capture structure information of scene images. By combining objects and structure information for scene categorization, we show superior performances to state-of-the-art approaches on three public datasets, i.e. MIT-indoor, UIUC-sports and SUN. Experiment results demonstrated the effectiveness of the proposed method.


2016 ◽  
Vol 173 ◽  
pp. 2041-2048 ◽  
Author(s):  
Minjing Yu ◽  
Yong-Jin Liu ◽  
Su-Jing Wang ◽  
Qiufang Fu ◽  
Xiaolan Fu

2018 ◽  
Vol 34 (4) ◽  
pp. 716-729
Author(s):  
Pramit Chaudhuri ◽  
Tathagata Dasgupta ◽  
Joseph P Dexter ◽  
Krithika Iyer

AbstractIdentifying the stylistic signatures characteristic of different genres is of central importance to literary theory and criticism. In this article we report a large-scale computational analysis of Latin prose and verse using a combination of quantitative stylistics and supervised machine learning. We train a set of classifiers to differentiate prose and poetry with high accuracy (>97%) based on a set of twenty-six text-based, primarily syntactic features and rank the relative importance of these features to identify a low-dimensional set still sufficient to achieve excellent classifier performance. This analysis demonstrates that Latin prose and verse can be classified effectively using just three top features. From examination of the highly ranked features, we observe that measures of the hypotactic style favored in Latin prose (i.e. subordinating constructions in complex sentences, such as relative clauses) are especially useful for classification.


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