Computing motion in the primate's visual system

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (V1) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle ‘the final optical flow should be as smooth as possible’ (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory

Computing optical flow in the primate visual system

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We show how gradient models, a well-known class of motion algorithms, can be implemented within the magnocellular pathway of the primate's visual system. Our cooperative algorithm computes optical flow in two steps. In the first stage, assumed to be located in primary visual cortex, local motion is measured while spatial integration occurs in the second stage, assumed to be located in the middle temporal area (MT). The final optical flow is extracted in this second stage using population coding, such that the velocity is represented by the vector sum of neurons coding for motion in different directions. Our theory, relating the single-cell to the perceptual level, accounts for a number of psychophysical and electrophysiological observations and illusions

Pixel-level data fusion: from algorithm to chip

Pixel level image processing algorithms have to work with noisy sensor data to extract spatial features. This often required the use of operators which amplify high frequency noise. One method of dealing with this problem is to perform image smoothing prior to any use of spatial differentiation. Such spatial smoothing results in the spread of object characteristics beyond the object boundaries. Identification of discontinuities and explicit use of these as boundaries for smoothing has been proposed as a technique to overcome this problem. This approach has been used to perform cooperative computations between multiple descriptions of the scene, e.g., fusion of edge and motion field for a given scene. This approach is extended to multisensor systems. The discontinuities detected in the output of one sensor are used to define regions of smoothing for a second sensor. For example, the depth discontinuities present in laser radar can be used to define smoothing boundaries for infrared focal plane arrays. The authors have recently developed a CMOS chip (28 X 36) which performs this task in real time. This chip consists of a resistive network and elements that can be switched ON or OFF, by loading a suitable bit pattern. The bit pattern for the control of switches can be generated from the discontinuities found in the output of sensor #1. The output of sensor #2 is applied to the resistive network for data smoothing. If all the switches are held in conducting state, this chip performs the usual data smoothing. However, if switches along object boundaries are turned OFF, a region for bounded smoothing is created. In this chip, information from a third sensor data (e.g., intensity data from laser radar) can be incorporated in the form of a map of 'confidence in data.' The results obtained with this chip using synthetic data and other potential applications of this chip are described

Object-Based Analog VLSI Vision Circuits

We describe two successfully working, analog VLSI vision circuits that move beyond pixel-based early vision algorithms. One circuit, implementing the dynamic wires model, provides for dedicated lines of communication among groups of pixels that share a common property. The chip uses the dynamic wires model to compute the arclength of visual contours. Another circuit labels all points inside a given contour with one voltage and all other with another voltage. Its behavior is very robust, since small breaks in contours are automatically sealed, providing for Figure-Ground segregation in a noisy environment. Both chips are implemented using networks of resistors and switches and represent a step towards object level processing since a single voltage value encodes the property of an ensemble of pixels

COMPUTING MOTION IN THE PRIMATE'S VISUAL SYSTEM

Computing motion on the basis of the time-varying image intensity is a difficult problem for both artificial and biological vision systems. We will show how one well-known gradient-based computer algorithm for estimating visual motion can be implemented within the primate's visual system. This relaxation algorithm computes the optical flow field by minimizing a variational functional of a form commonly encountered in early vision, and is performed in two steps. In the first stage, local motion is computed, while in the second stage spatial integration occurs. Neurons in the second stage represent the optical flow field via a population-coding scheme, such that the vector sum of all neurons at each location codes for the direction and magnitude of the velocity at that location. The resulting network maps onto the magnocellular pathway of the primate visual system, in particular onto cells in the primary visual cortex (VI) as well as onto cells in the middle temporal area (MT). Our algorithm mimics a number of psychophysical phenomena and illusions (perception of coherent plaids, motion capture, motion coherence) as well as electrophysiological recordings. Thus, a single unifying principle 'the final optical flow should be as smooth as possible' (except at isolated motion discontinuities) explains a large number of phenomena and links single-cell behavior with perception and computational theory

Infection with an Extended-Spectrum β-Lactamase-Producing Strain of Serratia marcescens following Tongue Reconstruction

We report a case of postsurgical wound infection of polymicrobial etiology caused by Serratia marcescens and Pseudomonas aeruginosa following the use of a radial forearm free flap for oncological tongue reconstruction. S. marcescens was a producer of SHV-12 extended-spectrum β-lactamase (ESBL). This is the first report from India of this ESBL. S. marcescens and P. aeruginosa were resistant to the empirical perioperative antibiotics administered. Delay in the recognition of the type of infection and in the institution of appropriate therapy resulted in total loss of the free flap