The Local Structure of Space-variant Images

Fischl, Bruce; Cohen, Michael A.; Schwartz, Eric L.

doi:10.1016/s0893-6080(96)00125-6

Cited by 13 publications

(6 citation statements)

References 67 publications

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“…where the parameter a controls the size of the foveal region (Fischl, Cohen, & Schwartz, 1997). We set a ϭ 1.…”

Section: Appendix a Model Equationsmentioning

confidence: 99%

“…The complex log-polar mapping transforms retinal points z into cortical points w . The mapping for the right and left hemifields is given by where the parameter a controls the size of the foveal region (Fischl, Cohen, & Schwartz, 1997). We set a = 1.The log-polar equation defines a mapping from retinal coordinates p ( x , y ) to cortical coordinates q ( u , v ) = (Re( w ), Im( w )).…”

Section: Model Equationsmentioning

confidence: 99%

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A neural model of visually guided steering, obstacle avoidance, and route selection.

Elder¹,

Grossberg²,

Mingolla³

2009

Journal of Experimental Psychology: Human Perception and Perfor

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A neural model is developed to explain how humans can approach a goal object on foot while steering around obstacles to avoid collisions in a cluttered environment. The model uses optic flow from a 3D virtual reality environment to determine the position of objects based on motion discontinuities, and computes heading direction, or the direction of self-motion, from global optic flow. The cortical representation of heading interacts with the representations of a goal and obstacles such that the goal acts as an attractor of heading, while obstacles act as repellers. In addition the model maintains fixation on the goal object by generating smooth pursuit eye movements. Eye rotations can distort the optic flow field, complicating heading perception, and the model uses extraretinal signals to correct for this distortion and accurately represent heading. The model explains how motion processing mechanisms in cortical areas MT, MST, and VIP can be used to guide steering. The model quantitatively simulates human psychophysical data about visually-guided steering, obstacle avoidance, and route selection.Key Words: Heading Perception, Steering, Optic Flow, Obstacle, Goal, Pursuit Eye Movement, Gain Fields, Peak Shift, V2, MT, MST, VIP, LIP 3 IntroductionMany important steering tasks are guided by visual information, including walking through a cluttered environment (Fajen & Warren, 2003), driving (Land & Horwood, 1995;Hildreth, Beusmans, Boer, & Royden, 2000;Wallis, Chatziastros, & Bülthoff, 2002), vehicle braking (Lee, 1976, piloting an aircraft (Gibson, Olum, & Rosenblatt, 1955;Beall & Loomis, 1997), and intercepting a moving target on foot (Fajen & Warren, 2004). Steering through a cluttered environment involves the selection of a path that avoids obstacles and simultaneously approaches the intended goal. Human steering is guided by visual information about the spatial layout of the environment and the direction of self-motion through the environment, as well as proprioceptive feedback and information from other sensory systems. In particular, the visual system provides information about the relative positions of the goal object and obstacles in the environment.Movement through the world creates a full-field pattern of motion on the retina, called optic flow, which contains information about the direction of self-motion, or heading (Gibson, 1950). In principle, optic flow can be used to compute heading (Longuet-Higgins & Prazdny, 1980). During translational movement of the eye, the optic flow field contains a singularity, called the focus of expansion, which specifies the direction of heading in the absence of an eye rotation.Figures 1 & 2 Whereas optic flow relates observer motion to the available visual stimuli, recent research clarifies how visual information governs the dynamics of human navigational behavior. Fajen and Warren (2003) studied the dynamics of human steering behavior in simple goal approach and obstacle avoidance tasks using an immersive virtual reality system. They found that human performa...

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“…where the parameter a controls the size of the foveal region (Fischl, Cohen, & Schwartz, 1997). We set a ϭ 1.…”

Section: Appendix a Model Equationsmentioning

confidence: 99%

Section: Model Equationsmentioning

confidence: 99%

A neural model of visually guided steering, obstacle avoidance, and route selection.

Elder¹,

Grossberg²,

Mingolla³

2009

Journal of Experimental Psychology: Human Perception and Perfor

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show abstract

“…Analysis of whole image in Cartesian space is a timeconsuming task due to a large amount of data that is necessary to process, so different techniques for reducing the complexity of this process were developed. Among them, a log-polar transform [2] is widely used, especially for object recognition [5,6]. One of its important feature is the ability to significantly reduce the number of pixels without sacrificing of image quality -at least near the Fixations would be usually located in areas that contain objects, so the center of log-polar transformation would be placed somewhere inside an object.…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Objects in Log-Polar Space

Bubliński

Pawlik

2015

Image Processing &Amp; Communications

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The aim of this work was to develop a simple and fast yet robust algorithm for segmentation of objects in log-polar space. Assuming that some interesting parts of an image are selected by fixation of point of attention, pixels located close to fixation points are transformed to a log-polar space, then edge detection is performed. As a final step, detected edges are processed in a specific manner, which should lead to obtain boundaries of objects. After some smoothing, the detected boundaries are transformed back to a Cartesian space and are superimposed on an original image in order to show results of segmentation.

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“…Although, in theory, the conformal mapping should permit a direct application of the visual operators developed for the Cartesian images to logpolar ones, these approaches have to cope with the necessity of properly adapting the algorithms before applying them on the space-variant images. In particular, Fischl et al (1997) analyze the properties of the log-polar transform from an analytic point of view, by addressing an application of the Laplacian operator, nevertheless neglecting the problems related to the discretization of the log-polar coordinates. Nattel and Yeshurun (2002) present some common global and local operators specifically designed to be directly applied in the log-polar domain, and Smeraldi and Bigun (2002) use modified Gabor filters designed in the log-polar frequency plane for facial features detection and face authentication.…”

Section: Introductionmentioning

confidence: 99%

Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain

Solari

Chessa

Sabatini

2012

Pattern Recognition Letters

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Despite the well known advantages that a space-variant representation of the visual signal offers, the required adaptation of the algorithms developed in the Cartesian domain, before applying them in the log-polar space, has limited a wide use of such representation in visual processing applications. In this paper, we present a set of original rules for designing a discrete log-polar mapping that allows a direct application in the log-polar domain of the algorithms, based on spatial multi-scale and multi-orientation filtering, originally developed for the Cartesian domain. The advantage of the approach is to gain, without modifications, an effective space-variance and data reduction. Such design strategies are based on a quantitative analysis of the relationships between the spatial filtering and the space-variant representation. We assess the devised rules by using a distributed approach based on a bank of band-pass filters to compute reliable disparity maps, by providing quantitative measures of the computational load and of the accuracy of the computed visual features

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The Local Structure of Space-variant Images

Cited by 13 publications

References 67 publications

A neural model of visually guided steering, obstacle avoidance, and route selection.

A neural model of visually guided steering, obstacle avoidance, and route selection.

Segmentation of Objects in Log-Polar Space

Design strategies for direct multi-scale and multi-orientation feature extraction in the log-polar domain

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