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This brief presents a low-power, flexible, and multichannel electroencephalography (EEG) feature extractor and classifier for the purpose of personalized seizure detection. Various features and classifiers were explored with the goal of maximizing detection accuracy while minimizing power, area, and latency. Additionally, algorithmic and hardware optimizations were identified to further improve performance...
Usage of statistical classifiers, namely AdaBoost and its modifications, in object detection and pattern recognition is a contemporary and popular trend. The computatiponal performance of these classifiers largely depends on low level image features they are using: both from the point of view of the amount of information the feature provides and the executional time of its evaluation. Local rank difference...
This paper presents a real-time multi-object tracking architecture based on image segmentation and object matching and its FPGA/ASIC implementation. With image segmentation, we can detect all objects in the image no matter whether they are moving or not. Using image segmentation results of successive frames, we exploit object matching in a simple object feature space for tracking of objects. For both...
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