27 April 2010 Neurally inspired rapid detection of sparse objects in videos
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In this paper, we describe COGNIVA, a closed-loop Cognitive-Neural method and system for image and video analysis that combines recent technological breakthroughs in bio-vision cognitive algorithms and neural signatures of human visual processing. COGNIVA is an "operational neuroscience" framework for intelligent and rapid search and categorization of Items Of Interest (IOI) in imagery and video. The IOI could be a single object, group of objects, specific image regions, specific spatio-temporal pattern/sequence or even the category that the image itself belongs to (e.g., vehicle or non-vehicle). There are two main types of approach for rapid search and categorization of IOI in imagery and video. The first approach uses conventional machine vision or bio-inspired cognitive algorithms. These usually need a predefined set of IOI and suffer from high false alarm rates. The second class of algorithms is based on neural signatures of target detection. These algorithms usually break the entire image into sub-images and process EEG data from these images and classify them based on it. This approach may suffer from high false alarms and is slow because the entire image is chipped and presented to the human observer. The proposed COGNIVA overcomes the limitations of both methods by combining them resulting in a low false alarm rate and high detection with high throughput making it applicable to both image and video analysis. In the most basic form, COGNIVA first uses bioinspired cognitive algorithms for deciding potential IOI in a sequence of images/video. These potential IOI are then shown to a human and neural signatures of visual detection of IOI are collected and processed. The resulting signatures are used to categorize and provide final IOI. We will present the concept and typical results of COGNIVA for detecting Items of interest in image data.
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Deepak Khosla, Deepak Khosla, David J. Huber, David J. Huber, Rajan Bhattacharyya, Rajan Bhattacharyya, Mike Daily, Mike Daily, Penn Tasinga, Penn Tasinga, } "Neurally inspired rapid detection of sparse objects in videos", Proc. SPIE 7697, Signal Processing, Sensor Fusion, and Target Recognition XIX, 76971C (27 April 2010); doi: 10.1117/12.850760; https://doi.org/10.1117/12.850760

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