6,894 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
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Adaptive Nonparametric Image Parsing
In this paper, we present an adaptive nonparametric solution to the image
parsing task, namely annotating each image pixel with its corresponding
category label. For a given test image, first, a locality-aware retrieval set
is extracted from the training data based on super-pixel matching similarities,
which are augmented with feature extraction for better differentiation of local
super-pixels. Then, the category of each super-pixel is initialized by the
majority vote of the -nearest-neighbor super-pixels in the retrieval set.
Instead of fixing as in traditional non-parametric approaches, here we
propose a novel adaptive nonparametric approach which determines the
sample-specific k for each test image. In particular, is adaptively set to
be the number of the fewest nearest super-pixels which the images in the
retrieval set can use to get the best category prediction. Finally, the initial
super-pixel labels are further refined by contextual smoothing. Extensive
experiments on challenging datasets demonstrate the superiority of the new
solution over other state-of-the-art nonparametric solutions.Comment: 11 page
Automatic region-of-interest extraction in low depth-of-field images
PhD ThesisAutomatic extraction of focused regions from images with low depth-of-field
(DOF) is a problem without an efficient solution yet. The capability of
extracting focused regions can help to bridge the semantic gap by integrating
image regions which are meaningfully relevant and generally do not exhibit
uniform visual characteristics. There exist two main difficulties for extracting
focused regions from low DOF images using high-frequency based techniques:
computational complexity and performance.
A novel unsupervised segmentation approach based on ensemble clustering is
proposed to extract the focused regions from low DOF images in two stages.
The first stage is to cluster image blocks in a joint contrast-energy feature space
into three constituent groups. To achieve this, we make use of a normal
mixture-based model along with standard expectation-maximization (EM)
algorithm at two consecutive levels of block size. To avoid the common
problem of local optima experienced in many models, an ensemble EM
clustering algorithm is proposed. As a result, relevant blocks, i.e., block-based
region-of-interest (ROI), closely conforming to image objects are extracted.
In stage two, two different approaches have been developed to extract
pixel-based ROI. In the first approach, a binary saliency map is constructed
from the relevant blocks at the pixel level, which is based on difference of
Gaussian (DOG) and binarization methods. Then, a set of morphological
operations is employed to create the pixel-based ROI from the map.
Experimental results demonstrate that the proposed approach achieves an
average segmentation performance of 91.3% and is computationally 3 times
faster than the best existing approach. In the second approach, a minimal graph
cut is constructed by using the max-flow method and also by using
object/background seeds provided by the ensemble clustering algorithm.
Experimental results demonstrate an average segmentation performance of 91.7%
and approximately 50% reduction of the average computational time by the
proposed colour based approach compared with existing unsupervised
approaches
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