The Roles of Endstopped and Curvature Tuned Computations in a Hierarchical Representation of 2D Shape

2012 ◽  
pp. 184-207
Author(s):  
Antonio J. Rodríguez-Sánchez ◽  
John K. Tsotsos
Keyword(s):  
Computer Vision ◽  
Computational Models ◽  
Shape Selectivity ◽  
Intermediate Representation ◽  
Object Representations ◽  
Direct Impact ◽  
Biological Inspiration ◽  
Visual Processes ◽  
Shape Selective ◽  
Biological Realism

Computational models of visual processes are of interest in fields such as cybernetics, robotics, computer vision, and others. This chapter argues for the importance of intermediate representation layers in the visual cortex that have direct impact on the next generation of object recognition strategies in computer vision. Biological inspiration - and even biological realism - is currently of great interest in the computer vision community. The authors propose that endstopping and curvature cells are of great importance for shape selectivity and show how their combination can lead to shape selective neurons, providing an approach that does not require learning between early stages based on Gabor or Difference of Gaussian filters and later stages closer to object representations.

The Roles of Endstopped and Curvature Tuned Computations in a Hierarchical Representation of 2D Shape

2013 ◽  
pp. 1338-1360
Author(s):  
Antonio J. Rodríguez-Sánchez ◽  
John K. Tsotsos
Keyword(s):  
Computer Vision ◽  
Computational Models ◽  
Shape Selectivity ◽  
Intermediate Representation ◽  
Object Representations ◽  
Direct Impact ◽  
Biological Inspiration ◽  
Visual Processes ◽  
Shape Selective ◽  
Biological Realism

Computational models of visual processes are of interest in fields such as cybernetics, robotics, computer vision, and others. This chapter argues for the importance of intermediate representation layers in the visual cortex that have direct impact on the next generation of object recognition strategies in computer vision. Biological inspiration - and even biological realism - is currently of great interest in the computer vision community. The authors propose that endstopping and curvature cells are of great importance for shape selectivity and show how their combination can lead to shape selective neurons, providing an approach that does not require learning between early stages based on Gabor or Difference of Gaussian filters and later stages closer to object representations.

Processing of Kinetic Boundaries in Macaque V4

2006 ◽  
Vol 95 (3) ◽  
pp. 1864-1880 ◽  
Author(s):  
Santosh G. Mysore ◽  
Rufin Vogels ◽  
Steve E. Raiguel ◽  
Guy A. Orban
Keyword(s):  
Relative Motion ◽  
Receptive Fields ◽  
Shape Selectivity ◽  
Luminance Contrast ◽  
Motion Cues ◽  
Form Processing ◽  
Similar Shape ◽  
Shape Selective ◽  
Sizeable Fraction ◽  
V4 Neurons

We used gratings and shapes defined by relative motion to study selectivity for static kinetic boundaries in macaque V4 neurons. Kinetic gratings were generated by random pixels moving in opposite directions in the neighboring bars, either parallel to the orientation of the boundary (parallel kinetic grating) or perpendicular to the boundary (orthogonal kinetic grating). Neurons were also tested with static, luminance defined gratings to establish cue invariance. In addition, we used eight shapes defined either by relative motion or by luminance contrast, as used previously to test cue invariance in the infero-temporal (IT) cortex. A sizeable fraction (10–20%) of the V4 neurons responded selectively to kinetic patterns. Most neurons selective for kinetic contours had receptive fields (RFs) within the central 10° of the visual field. Neurons selective for the orientation of kinetic gratings were defined as having similar orientation preferences for the two types of kinetic gratings, and the vast majority of these neurons also retained the same orientation preference for luminance defined gratings. Also, kinetic shape selective neurons had similar shape preferences when the shape was defined by relative motion or by luminance contrast, showing a cue-invariant form processing in V4. Although shape selectivity was weaker in V4 than what has been reported in the IT cortex, cue invariance was similar in the two areas, suggesting that invariance for luminance and motion cues of IT originates in V4. The neurons selective for kinetic patterns tended to be clustered within dorsal V4.

scholarly journals Position specificity of adaptation-related face aftereffects

2011 ◽  
Vol 366 (1564) ◽  
pp. 586-595 ◽  
Author(s):  
Márta Zimmer ◽  
Gyula Kovács
Keyword(s):  
Prolonged Exposure ◽  
Saccadic Eye Movements ◽  
Object Representations ◽  
Human Face ◽  
Visual Stability ◽  
Multiple Components ◽  
Processing Stream ◽  
Position Specificity ◽  
Shape Selective

It has been shown that prolonged exposure to a human face leads to shape-selective visual aftereffects. It seems that these face-specific aftereffects (FAEs) have multiple components, related to the adaptation of earlier and higher level processing of visual stimuli. The largest magnitude of FAE, using long-term adaptation periods, is usually observed at the retinotopic position of the preceding adaptor stimulus. However, FAE is also detected, to a smaller degree, at other retinal positions in a spatially invariant way and this component depends less on the adaptation duration. Several lines of evidences suggest that while the position-specific FAE involves lower level areas of the ventral processing stream, the position-invariant FAE depends on the activation of higher level face-processing areas and the fusiform gyrus in particular. In the present paper, we summarize the available behavioural, electrophysiological and neuroimaging results regarding the spatial selectivity of FAE and discuss their implications for the visual stability of object representations across saccadic eye movements.

Design of Computer Vision and Image Processing Technology Based Car Keys Tooth Identification Detection System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1061-1063 ◽  
Author(s):  
Hong Ying Li
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Detection System ◽  
Processing Technology ◽  
Direct Impact ◽  
Assembly Process ◽  
Accurate Identification ◽  
Detection Technology ◽  
Key Aspects ◽  
The Cost

This paper can be used as acar key toothed recognition and detection technology and computer vision, imageprocessing technology combined with interdisciplinary applications. Car lockassembly complicated procedures, identification and car keys tooth detection isone of the key aspects of automotive lock assembly, lock a direct impact on theefficiency of the assembly process. The system can effectively improve theexisting car key tooth detection technology to reduce the cost of car keystooth detection recognition, while also rapid and accurate identification, sothat the entire lock assembly process much more efficient.

scholarly journals Maximizing sinusoidal channels of HZSM-5 for high shape-selectivity to p-xylene

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Chuanfu Wang ◽  
Lei Zhang ◽  
Xin Huang ◽  
Yufei Zhu ◽  
Gang (Kevin) Li ◽  
...  
Keyword(s):  
High Activity ◽  
Chemical Industry ◽  
Chemical Separation ◽  
Shape Selectivity ◽  
Toluene Alkylation ◽  
Shape Selective ◽  
Toluene Methylation ◽  
Selective Catalysis ◽  
The Cost ◽  
Very High

Abstract The shape-selective catalysis enabled by zeolite micropore’s molecular-sized sieving is an efficient way to reduce the cost of chemical separation in the chemical industry. Although well studied since its discovery, HZSM-5′s shape-selective capability has never been fully exploited due to the co-existence of its different-sized straight channels and sinusoidal channels, which makes the shape-selective p-xylene production from toluene alkylation with the least m-xylene and o-xylene continue to be one of the few industrial challenges in the chemical industry. Rather than modifications which promote zeolite shape-selectivity at the cost of stability and reactivity loss, here inverse Al zoned HZSM-5 with sinusoidal channels predominantly opened to their external surfaces is constructed to maximize the shape-selectivity of HZSM-5 sinusoidal channels and reach > 99 % p-xylene selectivity, while keeping a very high activity and good stability ( > 220 h) in toluene methylation reactions. The strategy shows good prospects for shape-selective control of molecules with tiny differences in size.

Shape-selective alkylation of biphenyl over mordenites: effects of dealumination on shape-selectivity and coke deposition

1994 ◽  
Vol 26 (1-2) ◽  
pp. 181-187 ◽  
Author(s):  
Y. Sugi ◽  
T. Matsuzaki ◽  
T. Hanaoka ◽  
Y. Kubota ◽  
J. -H. Kim ◽  
...  
Keyword(s):  
Shape Selectivity ◽  
Coke Deposition ◽  
Shape Selective ◽  
Selective Alkylation

scholarly journals The SONATA Data Format for Efficient Description of Large-Scale Network Models

2019 ◽  
Author(s):  
Kael Dai ◽  
Juan Hernando ◽  
Yazan N. Billeh ◽  
Sergey L. Gratiy ◽  
Judit Planas ◽  
...  
Keyword(s):  
Network Architecture ◽  
High Performance ◽  
Large Scale ◽  
Computational Models ◽  
Model Building ◽  
Network Models ◽  
Data Format ◽  
Large Scale Network ◽  
Scale Network ◽  
Biological Realism

AbstractIncreasing availability of comprehensive experimental datasets and of high-performance computing resources are driving rapid growth in scale, complexity, and biological realism of computational models in neuroscience. To support construction and simulation, as well as sharing of such large-scale models, a broadly applicable, flexible, and high-performance data format is necessary. To address this need, we have developed the Scalable Open Network Architecture TemplAte (SONATA) data format. It is designed for memory and computational efficiency and works across multiple platforms. The format represents neuronal circuits and simulation inputs and outputs via standardized files and provides much flexibility for adding new conventions or extensions. SONATA is used in multiple modeling and visualization tools, and we also provide reference Application Programming Interfaces and model examples to catalyze further adoption. SONATA format is free and open for the community to use and build upon with the goal of enabling efficient model building, sharing, and reproducibility.

scholarly journals Computational Models of Face Perception

2017 ◽  
Vol 26 (3) ◽  
pp. 263-269 ◽  
Author(s):  
Aleix M. Martinez
Keyword(s):  
Computer Vision ◽  
Inverse Problem ◽  
Facial Expression ◽  
Computational Modeling ◽  
Face Perception ◽  
Computational Models ◽  
Emotional State ◽  
Current Understanding ◽  
Important Means ◽  
The Brain

Faces are one of the most important means of communication for humans. For example, a short glance at a person’s face provides information about his or her identity and emotional state. What are the computations the brain uses to acquire this information so accurately and seemingly effortlessly? This article summarizes current research on computational modeling, a technique used to answer this question. Specifically, my research tests the hypothesis that this algorithm is tasked with solving the inverse problem of production. For example, to recognize identity, our brain needs to identify shape and shading features that are invariant to facial expression, pose, and illumination. Similarly, to recognize emotion, the brain needs to identify shape and shading features that are invariant to identity, pose, and illumination. If one defines the physics equations that render an image under different identities, expressions, poses, and illuminations, then gaining invariance to these factors can be readily resolved by computing the inverse of this rendering function. I describe our current understanding of the algorithms used by our brains to resolve this inverse problem. I also discuss how these results are driving research in computer vision to design computer systems that are as accurate, robust, and efficient as humans.

Applications of Visual Attention in Image Processing, Computer Vision, and Graphics

2018 ◽  
pp. 1662-1685
Author(s):  
Rajarshi Pal
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Visual Attention ◽  
Visual Information ◽  
Computational Models ◽  
External Environment ◽  
Real Life ◽  
Visual Content ◽  
Selective Visual Attention ◽  
Speed Up

Selective visual attention is an amazing capability of primate visual system to restrict the focus to few interesting objects (or portions) in a scene. Thus, primates are able to pay attention to the required visual content amidst myriads of other visual information. It enables them to interact with the external environment in real time through reduction of computational load in their brain. This inspires image and computer vision scientists to derive computational models of visual attention and to use them in varieties of applications in real-life, mainly to speed up the processing through reduction of computational burden which often characterizes image processing and vision tasks. This chapter discusses a wide variety of such applications of visual attention models in image processing, computer vision and graphics.

Computational Models of Visual Attention

2014 ◽  
pp. 54-76 ◽  
Author(s):  
Rajarshi Pal
Keyword(s):  
Computer Vision ◽  
Visual Attention ◽  
Human Brain ◽  
Computational Modeling ◽  
Computational Models ◽  
Sensory Information ◽  
Processing Capacity ◽  
Human Beings ◽  
Psychological Phenomenon ◽  
External Stimuli

Even the enormous processing capacity of the human brain is not enough to handle all the visual sensory information that falls upon the retina. Still human beings can efficiently respond to the external stimuli. Selective attention plays an important role here. It helps to select only the pertinent portions of the scene being viewed for further processing at the deeper brain. Computational modeling of this neuro-psychological phenomenon has the potential to enrich many computer vision tasks. Enormous amounts of research involving psychovisual experiments and computational models of attention have been and are being carried out all within the past few decades. This article compiles a good volume of these research efforts. It also discusses various aspects related to computational modeling of attention–such as, choice of features, evaluation of these models, and so forth.

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