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Authors' Information Jennifer A. Hogan M.Sc. graduate student, Department of Electrical and Computer Engineering; University of Calgary, Calgary, Alberta, Canada, T2N 1NMartin P. Mintchev Prof., Dr., Department of Electrical and Computer Engineering; University of Calgary;

Calgary, Alberta, Canada, T2N 1N4; Department of Surgery, University of Alberta; Edmonton, Alberta T6G 2BPhone: (403) 220-5309; Fax (403) 282-6855; e-mail: mmintche@ucalgary.ca LOW-POWER TRACKING IMAGE SENSOR BASED ON BIOLOGICAL MODELS OF ATTENTION Alexander Fish, Liby Sudakov-Boreysha, Orly Yadid-Pecht Abstract: This paper presents implementation of a low-power tracking CMOS image sensor based on biological models of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing "smart" image sensor architecture, where all image processing is implemented on the sensor focal plane, the proposed imager allows reduction of the amount of data transmitted from the sensor array to external processing units and thus provides real time operation. The imager operation and architecture are based on the models taken from biological systems, where data sensed by many millions of receptors should be transmitted and processed in real time. The imager architecture is optimized to achieve low-power dissipation both in acquisition and tracking modes of operation. The tracking concept is presented, the system architecture is shown and the circuits description is discussed.

Keywords: Low-power image sensors, image processing, tracking imager, models of attention, CMOS sensors ACM Classification Keywords: B.7.0 Integrated circuits: General, I.4.8 Image processing and computer vision:

scene analysis: tracking 1. Introduction Real time visual tracking of salient targets in the field of view (FOV) is a very important operation in machine vision, star tracking and navigation applications. To accomplish real time operation a large amount of information is to be processed in parallel. This parallel processing is a very complicated task that demands huge computation resources. The same problem exists in biological vision systems. Compared to the state-of-the-art artificial imaging systems, having about twenty millions sensors, the human eye has more than one hundred million receptors (rods and cones). Thus, the question is how biological vision systems succeed to transmit and to process such a large amount of information in real time The answer is that to cope with potential overload, the brain is equipped with a variety of attentional mechanisms [1]. These mechanisms have two important functions:

Fourth International Conference I.TECH 2006 (a) attention can be used to select relevant information and/or to ignore the irrelevant or interfering information;

(b) attention can modulate or enhance the selected information according to the state and goals of the perceiver.

Most models of attention mechanisms are based on the fact that a serial selection of regions of interest and their subsequent processing can greatly facilitate the computation complexity. Numerous research efforts in physiology were triggered during the last five decades to understand the attention mechanism [2]-[10]. Generally, works related to physiological analysis of the human attention system can be divided into two main groups: those that present a spatial (spotlight) model for visual attention [2]-[4] and those following object-based attention [5][10]. The main difference between these models is that the object-based theory is based on the assumption that attention is referenced to a target or perceptual groups in the visual field, while the spotlight theory indicates that attention selects a place at which to enhance the efficiency of information processing.

The design of efficient real time tracking systems mostly depends on deep understanding of the model of visual attention. Thus, a discipline, named neuromorphic VLSI that imitates the processing architectures found in biological systems as closely as possible was introduced [11]. Both spotlight and object-based models have been recently implemented in analog neuromorphic VLSI design [12]-[23]. Most of them are based on the theory of selective shifts of attention which arises from a saliency map, as was first introduced by Koch and Ullman [12].

Object-based selective attention systems VLSI implementations in 1-D and lately 2-D were presented by Morris et al [13]-[16]. An additional work on an analog VLSI based attentional search/tracking was presented by Horiuchi and Niebur in 1999 [17].

Many works on neuromorphic VLSI implementations of selective attention systems have been presented by Indiveri [19]-[21] and others [22]-[23]. In 1998 Brajovic and Kanade presented a computational sensor for visual tracking with attention. These works often use winner-take-all (WTA) [24] networks that are responsible for selection and tracking inputs with the strongest amplitude. This sequential search method is equivalent to the spotlight attention found in biological systems.

Most previously presented neuromorphic imagers utilize image processing implemented on the focal plane level and employ photodiode or phototransistor current-mode pixels. Typically, each pixel consists of a photo detector and local circuitry, performing spatio-temporal computations on the analog signal. These computations are fully parallel and distributed, since the information is processed according to the locally sensed signals and data from pixel neighbors. This concept allows reduction in the computational cost of the next processing stages placed in the interface. Unfortunately, when image quality and high spatial resolution are important, image processing should be performed in the periphery. This way a high fill factor (FF) can be achieved even in small pixels.

This paper presents implementation of a low-power tracking CMOS image sensor based on a spotlight model of attention. The presented imager allows tracking of up to N salient targets in the field of view. Employing image processing at the sensor focal plane, the proposed sensor allows parallel computations and is distributed, but on the other hand most of the image processing is performed in the array periphery, allowing image quality and high spatial resolution. The imager architecture is optimized to achieve low-power dissipation both in acquisition and tracking modes of operation. This paper is a continuation of the work presented in [25], where we proposed to employ a spotlight model of attention for the bottleneck problem reduction in high resolution "smart" CMOS image sensors and of the work presented in [26], where the basic concept for an efficient VLSI tracking sensor was presented.

Section 2 briefly describes spotlight and object-based models of attention and presents system architecture of the proposed sensor. Low-power considerations, as well imager circuits description are shown in Section 3. Section discusses advantages and limitations of the proposed system. Conclusions and future work are presented in Section 5.

Information Technologies in Biomedicine 2. Tracking Sensor Architecture The proposed tracking sensor operation is based on the imitation of the spotlight model of visual attention.

Because this paper presents concepts taken from different research disciplines, first, a brief description of existing models of attention is presented for the readers that are not familiar with this field. Then, the proposed sensors architecture is shown.

2.1 Existing Attention Models Much research was done in attention during the last decades and numerous models have been proposed over the years. However, there is still much confusion as to the nature and role of attention. All presented models of attention can be divided to two main groups: spatial (spotlight) or early attention and object-based, or late attention. While the object-based theory suggests that the visual world is parsed into objects or perceptual groups, the spatial (spotlight) model purports that attention is directed to unparsed regions of space. Experimental research provides some degree of support to both models of attention. While both models are useful in understanding the processing of visual information, the spotlight model suffers from more drawbacks than the object-based model. However, the spotlight model is simpler and can be more useful for tracking imager implementations, as will be shown below.

2.1.1 The Spatial (Spotlight) Model The model of spotlight visual attention mainly grew out of the application of information theory developed by Shannon. In electronic systems, similar to physiological, the amount of the incoming information is limited by the system resources. There are two main models of spotlight attention. The simplest model can be looked upon as a spatial filter, where what falls outside the attentional spotlight is assumed not to be processed. In the second model, the spotlight serves to concentrate attentional resources to a particular region in space, thus enhancing processing at that location and almost eliminating processing of the unattended regions. The main difference between these models is that in the first one the spotlight only passively blocks the irrelevant information, while in the second model it actively directs the "processing efforts" to the chosen region.

Figure 1(a) and Figure 1(b) visually clarify the difference between the spatial filtering and spotlight attention.

Figure 1 (a). An example of spatial filtering Figure 1 (b). An example of spotlight model of attention A conventional view of the spotlight model assumes that only a single region of interest is processed at a certain time point and supposes smooth movement to other regions of interest. Later versions of the spotlight model assume that the attentional spotlight can be divided between several regions in space. In addition, the latter support the theory that the spotlight moves discretely from one region to the other.

2.1.2 Object-based Model As reviewed above, the spotlight metaphor is useful for understanding how attention is deployed across space.

However, this metaphor has serious limitations. A detailed analysis of the spotlight model drawbacks can be found in [1]. An object-based attention model suits more practical experiments in physiology and is based on the assumption that attention is referred to discrete objects in the visual field. However being more practical, in Fourth International Conference I.TECH 2006 contrast to the spotlight model, where one would predict that two nearby or overlapping objects are attended as a single object, in the object-based model this divided attention between objects results in less efficient processing than attending to a single object. It should be noted that spotlight and object-based attention theories are not contradictory but rather complementary. Nevertheless, in many cases the object-based theory explains many phenomena better than the spotlight model does.

The object-based model is more complicated for implementation, since it requires objects' recognition, while the spotlight model only requires identifying the regions of interest, where the attentional resources will be concentrated for further processing.

2.2 System Architecture The proposed sensor has two modes of operation: target acquisition and target tracking. In the acquisition mode N most salient targets of interest in the FOV are found. Then, N windows of interest with programmable size around the targets are defined. These windows define the active regions, where the subsequent processing will occur, similar to the flexible spotlight size in the biological systems. In the tracking mode, the system sequentially attends only to the previously chosen regions, while completely inhibiting the dataflow from the other regions.

The proposed concept permits choosing the attended regions in the desired order, independent on the targets saliency. In addition it allows shifting the attention from one active region to the other, independent of the distance between the targets. The proposed sensor aims to output the coordinates of all tracking targets in real time.

Similar to biological systems, which are limited in their computational resources, the engineering applications are constrained with low-power dissipation. Thus, maximum efforts have been done to reduce power consumption in the proposed sensor. This power reduction is based on the general idea of "no movement no action", meaning that minimum power should be dissipated if no change in the targets position occurred.

Figure 2. Architecture of the proposed tracking sensor.

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