scholarly journals Figure-Ground Organization in Visual Cortex for Natural Scenes

eNeuro ◽  
2016 ◽  
Vol 3 (6) ◽  
pp. ENEURO.0127-16.2016 ◽  
Author(s):  
Jonathan R. Williford ◽  
Rüdiger von der Heydt
Keyword(s):  
Visual Cortex ◽  
Natural Scenes ◽  
Ground Organization

scholarly journals Figure-ground organization in visual cortex for natural scenes

2016 ◽  
Author(s):  
Jonathan R. Williford ◽  
Rüdiger von der Heydt
Keyword(s):  
Visual Cortex ◽  
Temporal Cortex ◽  
Stimulus Onset ◽  
Natural Scenes ◽  
Area V2 ◽  
Response Onset ◽  
Static Images ◽  
Simple Figure ◽  
Image Context ◽  
Ground Organization

AbstractFigure-ground organization and border-ownership assignment are essential for understanding natural scenes. It has been shown that many neurons in the macaque visual cortex signal border-ownership in displays of simple geometric shapes such as squares, but how well these neurons resolve border-ownership in natural scenes is not known. We studied area V2 neurons in behaving macaques with static images of complex natural scenes. We found that about half of the neurons were border-ownership selective for contours in natural scenes and this selectivity originated from the image context. The border-ownership signals emerged within 70 ms after stimulus onset, only ~30 ms after response onset. A substantial fraction of neurons were highly consistent across scenes. Thus, the cortical mechanisms of figure-ground organization are fast and efficient even in images of complex natural scenes. Understanding how the brain performs this task so fast remains a challenge.Significance StatementHere we show, for the first time, that neurons in primate visual area V2 signal border-ownership for objects in complex natural scenes. Surprisingly, these signals appear as early as the border-ownership signals for simple figure displays. In fact, they emerge well before object selective activity appears in infero-temporal cortex, which rules out feedback from that region as an explanation. Thus, “objectness” is detected by extremely fast mechanisms that do not depend on feedback from the known object-recognition centers.

scholarly journals Transformation from independent to integrative coding of multi-object arrangements in human visual cortex

2017 ◽  
Author(s):  
Daniel Kaiser ◽  
Marius V. Peelen
Keyword(s):  
Visual Cortex ◽  
Visual System ◽  
Activity Patterns ◽  
Spatial Arrangement ◽  
Living Room ◽  
Natural Scenes ◽  
Response Patterns ◽  
Object Representations ◽  
Individual Object ◽  
Object Coding

AbstractTo optimize processing, the human visual system utilizes regularities present in naturalistic visual input. One of these regularities is the relative position of objects in a scene (e.g., a sofa in front of a television), with behavioral research showing that regularly positioned objects are easier to perceive and to remember. Here we use fMRI to test how positional regularities are encoded in the visual system. Participants viewed pairs of objects that formed minimalistic two-object scenes (e.g., a “living room” consisting of a sofa and television) presented in their regularly experienced spatial arrangement or in an irregular arrangement (with interchanged positions). Additionally, single objects were presented centrally and in isolation. Multi-voxel activity patterns evoked by the object pairs were modeled as the average of the response patterns evoked by the two single objects forming the pair. In two experiments, this approximation in object-selective cortex was significantly less accurate for the regularly than the irregularly positioned pairs, indicating integration of individual object representations. More detailed analysis revealed a transition from independent to integrative coding along the posterior-anterior axis of the visual cortex, with the independent component (but not the integrative component) being almost perfectly predicted by object selectivity across the visual hierarchy. These results reveal a transitional stage between individual object and multi-object coding in visual cortex, providing a possible neural correlate of efficient processing of regularly positioned objects in natural scenes.

scholarly journals Visual cortex represents the statistical distributions of objects in natural scenes

2010 ◽  
Vol 10 (7) ◽  
pp. 1260-1260
Author(s):  
D. Stansbury ◽  
T. Naselaris ◽  
A. Vu ◽  
J. Gallant
Keyword(s):  
Visual Cortex ◽  
Natural Scenes ◽  
Statistical Distributions

Ultrafast Object Detection in Naturalistic Vision Relies on Ultrafast Distractor Suppression

2019 ◽  
Vol 31 (10) ◽  
pp. 1563-1572 ◽  
Author(s):  
Clayton Hickey ◽  
Daniele Pollicino ◽  
Giacomo Bertazzoli ◽  
Ludwig Barbaro
Keyword(s):  
Visual Cortex ◽  
Spatial Attention ◽  
Brain Activity ◽  
Natural Scenes ◽  
Search Efficiency ◽  
Object Representations ◽  
Target Presence ◽  
Object Categories ◽  
Frontal Brain ◽  
Scene Information

People are quicker to detect examples of real-world object categories in natural scenes than is predicted by classic attention theories. One explanation for this puzzle suggests that experience renders the visual system sensitive to midlevel features diagnosing target presence. These are detected without the need for spatial attention, much as occurs for targets defined by low-level features like color or orientation. The alternative is that naturalistic search relies on spatial attention but is highly efficient because global scene information can be used to quickly reject nontarget objects and locations. Here, we use ERPs to differentiate between these possibilities. Results show that hallmark evidence of ultrafast target detection in frontal brain activity is preceded by an index of spatially specific distractor suppression in visual cortex. Naturalistic search for heterogenous targets therefore appears to rely on spatial operations that act on neural object representations, as predicted by classic attention theory. People appear able to rapidly reject nontarget objects and locations, consistent with the idea that global scene information is used to constrain naturalistic search and increase search efficiency.

A Selective Sparse Coding Model with Embedded Attention Mechanism

2011 ◽  
pp. 78-90
Author(s):  
Qingyong Li ◽  
Zhiping Shi ◽  
Zhongzhi Shi
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Sparse Coding ◽  
Coding Theory ◽  
Neural Representation ◽  
Natural Scenes ◽  
Processing Capacity ◽  
Uniform Sampling ◽  
Coding Efficiency ◽  
Non Uniform Sampling

Sparse coding theory demonstrates that the neurons in the primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics, but a typical scene contains many different patterns (corresponding to neurons in cortex) competing for neural representation because of the limited processing capacity of the visual system. We propose an attention-guided sparse coding model. This model includes two modules: the non-uniform sampling module simulating the process of retina and a data-driven attention module based on the response saliency. Our experiment results show that the model notably decreases the number of coefficients which may be activated, and retains the main vision information at the same time. It provides a way to improve the coding efficiency for sparse coding model and to achieve good performance in both population sparseness and lifetime sparseness.

scholarly journals Independent Components of Color Natural Scenes Resemble V1 Neurons in Their Spatial and Color Tuning

2004 ◽  
Vol 91 (6) ◽  
pp. 2859-2873 ◽  
Author(s):  
Matthew S. Caywood ◽  
Benjamin Willmore ◽  
David J. Tolhurst
Keyword(s):  
Visual Cortex ◽  
Sensory Input ◽  
Independent Component ◽  
Natural Scenes ◽  
Sensory Coding ◽  
Color Coding ◽  
Visual Code ◽  
Independent Components ◽  
V1 Neurons ◽  
Redundancy Reduction

It has been hypothesized that mammalian sensory systems are efficient because they reduce the redundancy of natural sensory input. If correct, this theory could unify our understanding of sensory coding; here, we test its predictions for color coding in the primate primary visual cortex (V1). We apply independent component analysis (ICA) to simulated cone responses to natural scenes, obtaining a set of colored independent component (IC) filters that form a redundancy-reducing visual code. We compare IC filters with physiologically measured V1 neurons, and find great spatial similarity between IC filters and V1 simple cells. On cursory inspection, there is little chromatic similarity; however, we find that many apparent differences result from biases in the physiological measurements and ICA analysis. After correcting these biases, we find that the chromatic tuning of IC filters does indeed resemble the population of V1 neurons, supporting the redundancy-reduction hypothesis.

Sign in / Sign up

Export Citation Format

Share Document