In this paper, we give experimental case studies on a cooperative estimation algorithm for visual sensor networks. We first introduce an overview of a passivity-based cooperative estimation algorithm presented in our ...
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ISBN:
(纸本)9784907764388
In this paper, we give experimental case studies on a cooperative estimation algorithm for visual sensor networks. We first introduce an overview of a passivity-based cooperative estimation algorithm presented in our previous works. Then, we describe the experimental system with multiple cameras. Finally, we perform experiments and give analyses from experimental data.
Multisensory fusion has become an area of intense research activity in the past few years. The goal of this paper is to present a technique for fusing infrared and visible videos. In this technique we propose a fusion...
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ISBN:
(纸本)9781467311496
Multisensory fusion has become an area of intense research activity in the past few years. The goal of this paper is to present a technique for fusing infrared and visible videos. In this technique we propose a fusion method that quickly fuses infrared and visible frames and gives a better performance. This is done by first decomposing the inputs using DWT and extracting two maps (resulted from Choose Max rule) from approximation sub frames and then fusing detail subframes according to these maps. After being compared to some of the popular fusion methods, the experimental results demonstrate that not only does this proposed method have a superior fusion performance, it can also be easily implemented in visual sensor networks in which speed and simplicity are of critical importance.
In a visualsensor network, a large number of camera nodes are able to acquire and process image data locally, collaborate with other camera nodes and provide a description about the captured events. Typically, camera...
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ISBN:
(纸本)9781479957521
In a visualsensor network, a large number of camera nodes are able to acquire and process image data locally, collaborate with other camera nodes and provide a description about the captured events. Typically, camera nodes have severe constraints in terms of energy, bandwidth resources and processing capabilities. Considering these unique characteristics, coding and transmission of the pixel-level representation of the visual scene must be avoided, due to the energy resources required. A promising approach is to extract at the camera nodes, compact visual features that are coded to meet the bandwidth and power requirements of the underlying network and devices. Since the total number of features extracted from an image may be rather significant, this paper proposes a novel method to select the most relevant features before the actual coding process. The solution relies on a score that estimates the accuracy of each local feature. Then, local features are ranked and only the most relevant features are coded and transmitted. The selected features must maximize the efficiency of the image analysis task but also minimize the required computational and transmission resources. Experimental results show that higher efficiency is achieved when compared to the previous state-of-the-art.
Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectu...
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ISBN:
(纸本)9781479957521
Local visual features are commonly adopted to accomplish analysis tasks such as object recognition/tracking and image retrieval. Recently, several visual features extraction algorithms tailored to low-power architectures have been proposed, in order to enable image analysis on energy-constrained devices such as smart-phones or visual sensor networks (VSN). In this work, we dissect and analyze BRISK, a state-of-the-art low-power visual feature extractor, in order to evaluate the impact of its individual building blocks on the overall energy consumption. For each building block, we propose a solution to limit the energy consumption without affecting the overall analysis performance. The resulting BRISKOLA (BRISK Optimized for Low-power ARM architectures) feature extractor exhibits energy savings up to 30% with respect to the original implementation.
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