The relationship between attention and consciousness is a close one, leading many scholars to
conflate the two. However, recent research has slowly corroded a belief that selective attention and
consciousness are so tightly entangled that they cannot be individually examined.
Bottom-up or saliency-based visual attention allows primates to detect non-specific conspicuous targets in cluttered scenes. A classical metaphor, derived from electrophysiological and psychophysical studies, describes attention as a rapidly shiftable 'spotlight'. The model described here reproduces the attentional scanpaths of this spotlight: Simple multi-scale 'feature maps' detect local spatial discontinuities in intensity, color, orientation or optical flow, and are combined into a unique 'master' or 'saliency' map. the saliency map is sequentially scanned, in order of decreasing saliency, by the focus of attention. We study the problem of combining feature maps, from different visual modalities and with unrelated dynamic ranges, into a unique saliency map. Four combination strategies are compared using three databases of natural color images: (1) Simple normalized summation, (2) linear combination with learned weights, (3) global non-linear normalization followed by summation, and (4) local non-linear competition between salient locations. Performance was measured as the number of false detections before the most salient target was found. Strategy (1) always yielded poorest performance and (2) best performance, with a 3- to 8-fold improvement in time to find a salient target. However, (2) yielded specialized systems with poor generations. Interestingly, strategy (4) and its simplified, computationally efficient approximation (3) yielded significantly better performance than (1), with up to 4-fold improvement, while preserving generality.
KEYWORDS: Sensors, Analog electronics, Spatial frequencies, Very large scale integration, Optical flow, Linear filtering, Capacitors, Computing systems, Motion measurement, Prototyping
In this work we present the first working focal plane analog VLSI sensor for the spatially resolved computation of the 2D motion field based on temporal and spatial derivatives. Using an adaptive CMOS photoreceptor the temporal derivative and a function of the spatial derivative of the local high intensity are computed. By multiplying these values separately for both spatial dimensions a vector is obtained, which points in the direction of the normal optical flow and whose magnitude for a given stimulus is proportional to its velocity. The circuit consists of only 31 MOSFETs and three capacitors per pixel. We present measurements data from fully functional prototype 2D pixel arrays for natural stimuli of varying velocity, orientation, contrast and spatial frequency. High direction selectivity even for very low contrast input is demonstrated. As application it is shown how the pixel-parallel architecture of the sensor can favorably be used for real-time computation of the focus of expansion and the axis of rotation. Because of its compactness, its robust operation and its uncritical handling the sensor might be favorably applied in industrial applications.
A family of analog CMOS velocity sensors is described which measures the velocity of a moving edge by computing its time of travel between adjacent pixels. These sensors are compact, largely invariant to illumination over a wide range, sensitive to edges with very low contrast, and responsive to several orders of magnitude of velocity. Two successful 1D velocity sensors are described in detail; preliminary data on a new 2D velocity sensor is shown. Work in progress to extend these sensors to processing of 2D optical flow is outlined.
We present different compact analog VLSI motion sensors that compute the 1-D velocity of optical stimuli over a large range and are suitable for integration in focal plane arrays. They have been extensively tested and optimized for robust performance under varying light conditions. Since their output signals are only weakly dependent on contrast, they directly extract optical flow data from an image. Focal plane arrays of such sensors are particularly interesting for application in single-chip systems that perform navigation tasks for moving robots or vehicles, where light weight, low power consumption, and real-time processing are crucial. Several monolithic motion-processing systems based on such velocity sensors have been built and tested. We describe here three chips, designed for the determination of the focus of expansion, the estimation of the time to contact, and the detection of motion discontinuities respectively. The first two systems have been specifically designed for vehicle navigation tasks. The choice of this application domain allows us to make a priori assumptions about the optical flow field that simplifies the structure of the systems and improves their overall performance. The motion-discontinuity-detection system can be more generally used to segment images based on the velocities of its different domains with respect to the camera. It is particularly useful for background-foreground segregation in the case of ego-motion of an autonomous system in a static environment. Tests results of the three systems are presented and their performance is evaluated.
In the last ten years, significant progress has been made in understanding the first steps in visual processing. Thus, a large number of algorithms exist that locate edges, compute disparities, estimate motion fields and find discontinuities in depth, motion, color and intensity. However, the application of these algorithms to real-life vision problems has been less successful, mainly because the associated computational cost prevents real-time machine vision implementations on anything but large-scale expensive digital computers. We here review the use of analog, special-purpose vision hardware, integrating image acquisition with early vision algorithms on a single VLSI chip. Such circuits have been designed and successfully tested for edge detection, surface interpolation, computing optical flow and sensor fusion. Thus, it appears that real-time, small, power-lean and robust analog computers are making a limited comeback in the form of highly dedicated, smart vision chips.
Novel use of an analog motion detection circuit is presented. The circuit, developed by Tanner and Mead, computes motion by dividing the time derivative of intensity by its spatial derivative; the four-quadrant division is realized with a multiplier within a negative feedback loop. The authors have opened the loop and characterized the circuit as a multiplication-based motion detector, in which the output is the product of the temporal and spatial derivatives of intensity, for various light levels and various moving patterns. An application to the time-to- contact computation is presented.
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.
The authors have designed and tested a one-dimensional 64 pixel, analog CMOS VLSI chip which localizes intensity edges in real-time. This device exploits on-chip photoreceptors and the natural filtering properties of resistive networks to implement a scheme similar to and motivated by the Difference of Gaussians (DOG) operator proposed by Marr and Hildreth (1980). The chip computes the zero-crossings associated with the difference of two exponential weighting functions and reports only those zero-crossings at which the derivative is above an adjustable threshold. A real-time motion detection system based on the zero- crossing chip and a conventional microprocessor provides linear velocity output over two orders of magnitude of light intensity and target velocity.
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