4,160 research outputs found
Object Segmentation in Images using EEG Signals
This paper explores the potential of brain-computer interfaces in segmenting
objects from images. Our approach is centered around designing an effective
method for displaying the image parts to the users such that they generate
measurable brain reactions. When an image region, specifically a block of
pixels, is displayed we estimate the probability of the block containing the
object of interest using a score based on EEG activity. After several such
blocks are displayed, the resulting probability map is binarized and combined
with the GrabCut algorithm to segment the image into object and background
regions. This study shows that BCI and simple EEG analysis are useful in
locating object boundaries in images.Comment: This is a preprint version prior to submission for peer-review of the
paper accepted to the 22nd ACM International Conference on Multimedia
(November 3-7, 2014, Orlando, Florida, USA) for the High Risk High Reward
session. 10 page
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Analyzing P300 Distractors for Target Reconstruction
P300-based brain-computer interfaces (BCIs) are often trained per-user and
per-application space. Training such models requires ground truth knowledge of
target and non-target stimulus categories during model training, which imparts
bias into the model. Additionally, not all non-targets are created equal; some
may contain visual features that resemble targets or may otherwise be visually
salient. Current research has indicated that non-target distractors may elicit
attenuated P300 responses based on the perceptual similarity of these
distractors to the target category. To minimize this bias, and enable a more
nuanced analysis, we use a generalized BCI approach that is fit to neither user
nor task. We do not seek to improve the overall accuracy of the BCI with our
generalized approach; we instead demonstrate the utility of our approach for
identifying target-related image features. When combined with other intelligent
agents, such as computer vision systems, the performance of the generalized
model equals that of the user-specific models, without any user specific data.Comment: 4 pages, 3 figure
Synthetic-Neuroscore: Using A Neuro-AI Interface for Evaluating Generative Adversarial Networks
Generative adversarial networks (GANs) are increasingly attracting attention
in the computer vision, natural language processing, speech synthesis and
similar domains. Arguably the most striking results have been in the area of
image synthesis. However, evaluating the performance of GANs is still an open
and challenging problem. Existing evaluation metrics primarily measure the
dissimilarity between real and generated images using automated statistical
methods. They often require large sample sizes for evaluation and do not
directly reflect human perception of image quality. In this work, we describe
an evaluation metric we call Neuroscore, for evaluating the performance of
GANs, that more directly reflects psychoperceptual image quality through the
utilization of brain signals. Our results show that Neuroscore has superior
performance to the current evaluation metrics in that: (1) It is more
consistent with human judgment; (2) The evaluation process needs much smaller
numbers of samples; and (3) It is able to rank the quality of images on a per
GAN basis. A convolutional neural network (CNN) based neuro-AI interface is
proposed to predict Neuroscore from GAN-generated images directly without the
need for neural responses. Importantly, we show that including neural responses
during the training phase of the network can significantly improve the
prediction capability of the proposed model. Materials related to this work are
provided at https://github.com/villawang/Neuro-AI-Interface
An EEG-Based Image Annotation System
The success of deep learning in computer vision has greatly increased the need for annotated image datasets. We propose an EEG (Electroencephalogram)-based image annotation system. While humans can recognize objects in 20–200 ms, the need to manually label images results in a low annotation throughput. Our system employs brain signals captured via a consumer EEG device to achieve an annotation rate of up to 10 images per second. We exploit the P300 event-related potential (ERP) signature to identify target images during a rapid serial visual presentation (RSVP) task. We further perform unsupervised outlier removal to achieve an F1-score of 0.88 on the test set. The proposed system does not depend on category-specific EEG signatures enabling the annotation of any new image category without any model pre-training
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