Described is a bio-inspired vision system for object recognition. The system comprises an attention module, an object recognition module, and an online labeling module. The attention module is configured to receive an image representing a scene and find and extract an object from the image. The attention module is also configured to generate feature vectors corresponding to color, intensity, and orientation information within the extracted object. The object recognition module is configured to receive the extracted object and the feature vectors and associate a label with the extracted object. Finally, the online labeling module is configured to alert a user if the extracted object is an unknown object so that it can be labeled.

Visual attention and object recognition system

Described is a system for optimizing rapid serial visual presentation (RSVP). A similarity metric is computed for RSVP images, and the images are sequenced according to the similarity metrics. The sequenced images are presented to a user, and neural signals are received to detect a P300 signal. A neural score for each image is computed, and the system is optimized to model the neural scores. The images are resequenced according a predictive model to output a sequence prediction which does not cause a false P300 signal. Additionally, the present invention describes computing a set of motion surprise maps from image chips. The image chips are labeled as static or moving and prepared into RSVP datasets. Neural signals are recorded in response to the RSVP datasets, and an EEG score is computed from the neural signals. Each image chip is then classified as containing or not containing an item of interest.

System for optimal rapid serial visual presentation (RSVP) from user-specific neural brain signals

Described is a system for finding salient regions in imagery. The system improves upon the prior art by receiving an input image of a scene and dividing the image into a plurality of image sub-regions. Each sub-region is assigned a coordinate position within the image such that the sub-regions collectively form the input image. A plurality of local saliency maps are generated, where each local saliency map is based on a corresponding sub-region and a coordinate position representative of the corresponding sub-region. Finally, the plurality of local saliency maps is combined according to their coordinate positions to generate a single global saliency map of the input image of the scene.

Visual attention system for salient regions in imagery

Described is a bio-inspired vision system for attention and object segmentation capable of computing attention for a natural scene, attending to regions in a scene in their rank of saliency, and extracting the boundary of an attended proto-object based on feature contours to segment the attended object. The attention module can work in both a bottom-up and a top-down mode, the latter allowing for directed searches for specific targets. The region growing module allows for object segmentation that has been shown to work under a variety of natural scenes that would be problematic for traditional object segmentation algorithms. The system can perform at multiple scales of object extraction and possesses a high degree of automation. Lastly, the system can be used by itself for stand-alone searching for salient objects in a scene, or as the front-end of an object recognition and online labeling system.

Visual attention and segmentation system

The present invention relates to a system for identifying regions of interest in visual imagery. The system is configured to receive a series of consecutive frames representing a scene as captured from N sensors. The frames include at least a current frame and a previous frame. A surprise map can be generated based on features found in the current frame and the previous frame. The surprise map having a plurality of values corresponding to spatial locations within the scene. Based on the values, a surprise in the scene can be identified if a value in the surprise map exceeds a predetermined threshold.

System for identifying regions of interest in visual imagery

User-supplied data such as browsing logs, click-through data, and relevance feedback judgements are an important source of knowledge during semantic indexing of documents such as images and video. Low-level indexing and abstraction methods are limited in the manner with which semantic data can be dealt. In this paper and in the context of this semantic data, we apply latent semantic analysis on two forms of user-supplied data, real-world and artificially generated relevance feedback judgements in order to examine the validity of using artificially generated interaction data for the study of semantic image clustering.

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Semantic clustering of images using patterns of relevance feedback

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Semantic clustering of images using patterns of relevance feedback