scholarly journals Segmenting surface boundaries using luminance cues

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Christopher DiMattina ◽  
Curtis L. Baker
Keyword(s):  
Simple Model ◽  
Visual Perception ◽  
Natural Scenes ◽  
Sharp Change ◽  
Stage Model ◽  
Filter Model ◽  
Mean Luminance ◽  
Contrast Normalization ◽  
Texture Boundaries ◽  
The One

AbstractSegmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter–Rectify–Filter model positing two cascaded stages of filtering, which fits our data well, and explains observers' ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

scholarly journals Segmenting surface boundaries using luminance cues: Underlying mechanisms

2020 ◽  
Author(s):  
Christopher DiMattina ◽  
Curtis L. Baker
Keyword(s):  
Simple Model ◽  
Visual Perception ◽  
Natural Scenes ◽  
Sharp Change ◽  
Stage Model ◽  
Mean Luminance ◽  
Contrast Normalization ◽  
Texture Boundaries ◽  
Underlying Mechanisms ◽  
The One

ABSTRACTSegmenting scenes into distinct surfaces is a basic visual perception task, and luminance differences between adjacent surfaces often provide an important segmentation cue. However, mean luminance differences between two surfaces may exist without any sharp change in albedo at their boundary, but rather from differences in the proportion of small light and dark areas within each surface, e.g. texture elements, which we refer to as a luminance texture boundary. Here we investigate the performance of human observers segmenting luminance texture boundaries. We demonstrate that a simple model involving a single stage of filtering cannot explain observer performance, unless it incorporates contrast normalization. Performing additional experiments in which observers segment luminance texture boundaries while ignoring super-imposed luminance step boundaries, we demonstrate that the one-stage model, even with contrast normalization, cannot explain performance. We then present a Filter-Rectify-Filter (FRF) model positing two cascaded stages of filtering, which fits our data well, and explains observers’ ability to segment luminance texture boundary stimuli in the presence of interfering luminance step boundaries. We propose that such computations may be useful for boundary segmentation in natural scenes, where shadows often give rise to luminance step edges which do not correspond to surface boundaries.

scholarly journals Interactions between luminance steps and luminance textures for boundary segmentation

2021 ◽  
Author(s):  
Christopher DiMattina
Keyword(s):  
Experimental Work ◽  
Natural Scenes ◽  
Sharp Change ◽  
Relative Robustness ◽  
Mean Luminance ◽  
Texture Boundaries ◽  
Segmentation Task

ABSTRACTIn natural scenes, two adjacent surfaces may differ in mean luminance without any sharp change in luminance at their boundary, but rather due to different relative proportions of light and dark regions within each surface. We refer to such boundaries as luminance texture boundaries (LTBs), and in this study we investigate interactions between luminance texture boundaries and luminance step boundaries (LSBs) in a segmentation task. Using a simple masking paradigm, we find very little influence of LSB maskers on LTB segmentation thresholds. Similarly, we find only modest effects of LTB maskers on LSB thresholds. By contrast, each kind of boundary strongly masks targets of the same kind. Our data is consistent with the possibility that luminance texture boundaries may be segmented using different mechanisms than those used to segment luminance step boundaries. At the same time, our work also suggests that LTB segmentation is subject to influences from LSBs. We suggest that the relative robustness of LTB segmentation to interference from LSBs may serve the ecologically important role of providing robustness to changes in luminance caused by cast shadows, and we propose future experimental work to investigate this hypothesis.

scholarly journals Chaos and complexity in a simple model of production dynamics

2000 ◽  
Vol 5 (3) ◽  
pp. 179-187 ◽  
Author(s):  
I. Katzorke ◽  
A. Pikovsky
Keyword(s):  
Simple Model ◽  
Dynamical Behavior ◽  
Production Costs ◽  
Order Flow ◽  
One Dimensional ◽  
Dynamical Behaviors ◽  
The Stability ◽  
The One ◽  
Production Dynamics ◽  
Complex Dynamical Behavior

We consider complex dynamical behavior in a simple model of production dynamics, based on the Wiendahl’s funnel approach. In the case of continuous order flow a model of three parallel funnels reduces to the one-dimensional Bernoulli-type map, and demonstrates strong chaotic properties. The optimization of production costs is possible with the OGY method of chaos control. The dynamics changes drastically in the case of discrete order flow. We discuss different dynamical behaviors, the complexity and the stability of this discrete system.

The Time Course of Perceptual Grouping in Natural Scenes

2012 ◽  
Vol 23 (12) ◽  
pp. 1482-1489 ◽  
Author(s):  
Ilia Korjoukov ◽  
Danique Jeurissen ◽  
Niels A. Kloosterman ◽  
Josine E. Verhoeven ◽  
H. Steven Scholte ◽  
...  
Keyword(s):  
Visual Perception ◽  
Perceptual Grouping ◽  
Time Course ◽  
Reaction Times ◽  
Object Classification ◽  
Natural Scenes ◽  
Single Object ◽  
Object A ◽  
Object Parsing ◽  
Different Parts

Visual perception starts with localized filters that subdivide the image into fragments that undergo separate analyses. The visual system has to reconstruct objects by grouping image fragments that belong to the same object. A widely held view is that perceptual grouping occurs in parallel across the visual scene and without attention. To test this idea, we measured the speed of grouping in pictures of animals and vehicles. In a classification task, these pictures were categorized efficiently. In an image-parsing task, participants reported whether two cues fell on the same or different objects, and we measured reaction times. Despite the participants’ fast object classification, perceptual grouping required more time if the distance between cues was larger, and we observed an additional delay when the cues fell on different parts of a single object. Parsing was also slower for inverted than for upright objects. These results imply that perception starts with rapid object classification and that rapid classification is followed by a serial perceptual grouping phase, which is more efficient for objects in a familiar orientation than for objects in an unfamiliar orientation.

scholarly journals Rollover Warning for Articulated Heavy Vehicles Based on a Time-to-Rollover Metric

2004 ◽  
Vol 127 (3) ◽  
pp. 406-414 ◽  
Author(s):  
Bo-Chiuan Chen ◽  
Huei Peng
Keyword(s):  
Neural Network ◽  
Simple Model ◽  
Design Process ◽  
The Other ◽  
Roll Angle ◽  
Heavy Vehicle ◽  
Vehicle Speed ◽  
Heavy Vehicles ◽  
The One ◽  
Accuracy Problem

A Time-To-Rollover (TTR) metric is proposed as the basis to assess rollover threat for an articulated heavy vehicle. The TTR metric accurately “counts-down” toward rollover regardless of vehicle speed and steering patterns, so that the level of rollover threat is accurately assessed. There are two conflicting requirements in the implementation of TTR. On the one hand, a model significantly faster than real-time is needed. On the other hand, the TTR predicted by this model needs to be accurate enough under all driving scenarios. An innovative approach is proposed in this paper to solve this dilemma and the design process is illustrated in an example. First, a simple yet reasonably accurate yaw∕roll model is identified. A Neural Network (NN) is then developed to mitigate the accuracy problem of this simple model. The NN takes the TTR generated by the simple model, vehicle roll angle, and change of roll angle to generate an enhanced NN-TTR index. The NN was trained and verified under a variety of driving patterns. It was found that an accurate TTR is achieved across all the driving scenarios we tested.

Phallology, phlyakes, iconography and Aristophanes

1987 ◽  
Vol 33 ◽  
pp. 92-104 ◽  
Author(s):  
Oliver Taplin
Keyword(s):  
Golden Age ◽  
Fifth Century ◽  
Stage Model ◽  
Actual Performance ◽  
Old Comedy ◽  
The One

Two highly unusual vase paintings, which may be more or less direct representations of Aristophanes, have been first published recently. They have received little attention to date, and yet both bring with them intriguing problems, which are not, in my opinion, resolved in the original publications. This double accession is all the more remarkable since up till now there has been so little that might be claimed to illustrate pictorially the golden age of Old Comedy (say 435 to 390 B.C), however loosely or tightly the debatable term ‘illustration’ is used (see note 24). The best known has probably been the attic oenochoe with a squat, near-naked figure prancing on a low stage before an audience of two. He is usually taken to be burlesquing Perseus; but presumably his stage model, if indeed the genre is comedy at all, did not really perform naked and without mask. Closer to representation of actual performance may be the four unglazed oenochoai with polychrome decoration from towards the end of the fifth century, found in the Athenian Agora in 1954. For example the two porters, who are alleged to be carrying a large Dionysiac loaf, and who may be slaves or members of a chorus, have masks which are fairly grotesque, but their bodies are not particularly so (unless the one on the left is supposed to have a long but sketchy phallus pointing diagonally down?).

scholarly journals Does RMS contrast normalization impair coarse-to-fine processing of natural scenes?

2014 ◽  
Vol 14 (10) ◽  
pp. 361-361
Author(s):  
L. Kauffmann ◽  
A. Chauvin ◽  
N. Guyader ◽  
S. Ramanoel ◽  
C. Peyrin
Keyword(s):  
Natural Scenes ◽  
Contrast Normalization ◽  
Coarse To Fine

scholarly journals Fixation search in natural scenes: a new role for contrast normalization

2014 ◽  
Vol 14 (10) ◽  
pp. 1445-1445
Author(s):  
J. Abrams ◽  
C. Bradley ◽  
W. S. Geisler
Keyword(s):  
Natural Scenes ◽  
Contrast Normalization

scholarly journals A Review on Auditory Perception for Unmanned Aerial Vehicles

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7276
Author(s):  
Jose Martinez-Carranza ◽  
Caleb Rascon
Keyword(s):  
Visual Perception ◽  
Literature Review ◽  
Unmanned Aerial Vehicles ◽  
Current Literature ◽  
Auditory Perception ◽  
Additional Information ◽  
Aerial Vehicles ◽  
The One

Although a significant amount of work has been carried out for visual perception in the context of unmanned aerial vehicles (UAVs), not so much has been done regarding auditory perception. The latter can complement the observation of the environment that surrounds a UAV by providing additional information that can be used to detect, classify, and localize audio sources of interest. Motivated by the usefulness of auditory perception for UAVs, we present a literature review that discusses the audio techniques and microphone configurations reported in the literature. A categorization of techniques is proposed based on the role a UAV plays in the auditory perception (is it the one being perceived or is it the perceiver?), as well as a set of objectives that are more popularly aimed to be accomplished in the current literature (detection, classification, and localization). This literature review aims to provide a concise landscape of the most relevant works on auditory perception in the context of UAVs to date and provides insights into future avenues of research as a guide to those who are beginning to work in this field.

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