Spiral Architecture continues to demonstrate its potential as the basis for a general-purpose machine vision system. This paper explores another application of spiral architecture in the area of image compression. Ear...
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ISBN:
(纸本)1932415653
Spiral Architecture continues to demonstrate its potential as the basis for a general-purpose machine vision system. This paper explores another application of spiral architecture in the area of image compression. Earlier research into compression using the Spiral Architecture focused on the properties of the pixel address labelling scheme. This gives rise to the property of interest which is the physical proximity of the hexagonal pixels with neighbouring addresses. It was shown that rectangular (that is, raster based) systems have vertical physically adjacent pixels but the address distance is the length of a scan line. The Spiral Architecture, unlike the rectangular system, has the property that neighbouring pixels in address space have similar intensities thus giving opportunities for better compression. The scaling property of the Spiral architecture is again leveraged in a compression mechanism where transmission of various small portions of an extremely large image (such as a map) need to be foveated (navigated and viewed at scales and resolutions) as smoothly and seamlessly as possible.
In the recent past, much effort has been put into the development of distributed vision systems with smart cameras as key components. Smart cameras combine video sensing, processing and communication within a single e...
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ISBN:
(纸本)9781424413539
In the recent past, much effort has been put into the development of distributed vision systems with smart cameras as key components. Smart cameras combine video sensing, processing and communication within a single embedded device and provide sufficient on-board infrastructure to carry out high-level video analysis tasks. Networks of smart cameras help to overcome some hard problems inherent to singlecamera systems by providing multiple views of a scene. This paper reports on an improved, agent-oriented middleware for embedded smart cameras. Each imageprocessing task is represented by an agent resident on a smart camera within the network. Agents are able to move from one camera to another as needed during run-time. An agent is comprised of the high-level application logic and the imageprocessing algorithm which is executed on the processing unit. The presented middleware is also designed for distributed image processing where two or more cameras can cooperate for a single task. In the paper we discuss the requirements for such an agent-oriented middleware capable of supporting distributed image processing. Further, we describe the architecture of our middleware implementation. The evaluation of our current middleware implementation shows significant performance improvements compared to our previous Javabased implementation.
Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most metho...
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Camera networks are being deployed for various applications like security and surveillance, disaster response and environmental modeling. However, there is little automated processing of the data. Moreover, most methods for multicamera analysis are centralized schemes that require the data to be present at a central server. In many applications, this is prohibitively expensive, both technically and economically. In this paper, we investigate distributed scene analysis algorithms by leveraging upon concepts of consensus that have been studied in the context of multiagent systems, but have had little applications in video analysis. Each camera estimates certain parameters based upon its own sensed data which is then shared locally with the neighboring cameras in an iterative fashion, and a final estimate is arrived at in the network using consensus algorithms. We specifically focus on two basic problems-tracking and activity recognition. For multitarget tracking in a distributed camera network, we show how the Kalman-Consensus algorithm can be adapted to take into account the directional nature of video sensors and the network topology. For the activity recognition problem, we derive a probabilistic consensus scheme that combines the similarity scores of neighboring cameras to come up with a probability for each action at the network level. Thorough experimental results are shown on real data along with a quantitative analysis.
The ease of deploying wireless camera sensor nodes has grown with the reduction of manufacturing costs of low power, high resolution cameras. Although current wireless sensor network platforms have limited on-board re...
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ISBN:
(纸本)9781605585192
The ease of deploying wireless camera sensor nodes has grown with the reduction of manufacturing costs of low power, high resolution cameras. Although current wireless sensor network platforms have limited on-board resources for solving highly complex computer vision problems, we show that by splitting the processing costs between the sensor node and a powerful backend, we can achieve better classification results. Using such a distributedprocessing approach, we balance the computational and communication costs for achieving better detection performance while improving the system lifetime.
Spiral Architecture is a relatively new and powerful approach to general purpose machine vision system. It contains very useful geometric and algebraic properties. Two algebraic operations, Spiral Addition and Spiral ...
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ISBN:
(纸本)0909925895
Spiral Architecture is a relatively new and powerful approach to general purpose machine vision system. It contains very useful geometric and algebraic properties. Two algebraic operations, Spiral Addition and Spiral Multiplication, have been defined on it. This paper presents a way to segment the object(s) in an image uniformly by Spiral Multiplication. Namely, a number of analogous small copies of the original object(s) are made during segmentation. An algorithm is also developed in this paper to compute the scaling factor or the number of the small copies, so image segmentation can be done flexibly and quantitatively according to the specific application. The research results are very beneficial to image segmentation in parallel imageprocessing and distributed image processing.
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