52,961 research outputs found
Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images
Efficient and easy segmentation of images and volumes is of great practical
importance. Segmentation problems that motivate our approach originate from
microscopy imaging commonly used in materials science, medicine, and biology.
We formulate image segmentation as a probabilistic pixel classification
problem, and we apply segmentation as a step towards characterising image
content. Our method allows the user to define structures of interest by
interactively marking a subset of pixels. Thanks to the real-time feedback, the
user can place new markings strategically, depending on the current outcome.
The final pixel classification may be obtained from a very modest user input.
An important ingredient of our method is a graph that encodes image content.
This graph is built in an unsupervised manner during initialisation and is
based on clustering of image features. Since we combine a limited amount of
user-labelled data with the clustering information obtained from the unlabelled
parts of the image, our method fits in the general framework of semi-supervised
learning. We demonstrate how this can be a very efficient approach to
segmentation through pixel classification.Comment: 9 pages, 7 figures, PDFLaTe
Becoming the Expert - Interactive Multi-Class Machine Teaching
Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important and representative images first, followed by less important
images later - or not at all. However, image-importance is individual-specific,
i.e. a teaching image is important to a student if it changes their overall
ability to discriminate between classes. Further, students keep learning, so
while image-importance depends on their current knowledge, it also varies with
time.
In this work we propose an Interactive Machine Teaching algorithm that
enables a computer to teach challenging visual concepts to a human. Our
adaptive algorithm chooses, online, which labeled images from a teaching set
should be shown to the student as they learn. We show that a teaching strategy
that probabilistically models the student's ability and progress, based on
their correct and incorrect answers, produces better 'experts'. We present
results using real human participants across several varied and challenging
real-world datasets.Comment: CVPR 201
Automatic emotional state detection using facial expression dynamic in videos
In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states.
The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems
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
Integrating sustainability into Project Management practices: the perspective of professional institutions
This is paper is based on a work in progress research project, therefore results and conclusions are preliminary.Synopsis: âSustainabilityâ in its broadest meaning has acquired a great importance in modern society, and consequently influences almost every aspect of social life. This paper analyses the transformation that the project management profession is undergoing towards the integration of sustainability into its core values and practices.Research design: This research uses qualitative data from a mix of semi-structured interviews and archival evidence â professional bodies of knowledge,codes of ethics, newsletters, websites, social media platforms, blogs, onlinedatabases, and international standards â with the intention of answering thefollowing research question: âwhat is the influence of professional associationswith regard to the institutionalizing of sustainability practices into projectmanagement (PM) tools and techniques?âMain findings: There are different players which influence, in different ways, thePM profession. Our analysis reveals that the nature of these actors is veryheterogeneous, and the influence of the professional world of PM on theinstitutionalization of sustainable project management is manifested in thedifferent actions carried on by the entities we highlighted above. Therefore, theshift towards SPM is the result of the combination of each actorâs individualstrategy (Muzio, Brock, & Suddaby, 2013).Research implications: The analysis of sustainable project management (SPM) isaimed at contributing to the PM academic literature, describing thetransformation of PM practices, and to the practitioner literature, engaging withPM professional associations on the way they introduce the set of new practices
Motion analysis report
Human motion analysis is the task of converting actual human movements into computer readable data. Such movement information may be obtained though active or passive sensing methods. Active methods include physical measuring devices such as goniometers on joints of the body, force plates, and manually operated sensors such as a Cybex dynamometer. Passive sensing de-couples the position measuring device from actual human contact. Passive sensors include Selspot scanning systems (since there is no mechanical connection between the subject's attached LEDs and the infrared sensing cameras), sonic (spark-based) three-dimensional digitizers, Polhemus six-dimensional tracking systems, and image processing systems based on multiple views and photogrammetric calculations
K-Space at TRECVID 2008
In this paper we describe K-Spaceâs participation in
TRECVid 2008 in the interactive search task. For 2008
the K-Space group performed one of the largest interactive
video information retrieval experiments conducted
in a laboratory setting. We had three institutions participating
in a multi-site multi-system experiment. In
total 36 users participated, 12 each from Dublin City
University (DCU, Ireland), University of Glasgow (GU,
Scotland) and Centrum Wiskunde and Informatica (CWI,
the Netherlands). Three user interfaces were developed,
two from DCU which were also used in 2007 as well as
an interface from GU. All interfaces leveraged the same
search service. Using a latin squares arrangement, each
user conducted 12 topics, leading in total to 6 runs per
site, 18 in total. We officially submitted for evaluation 3
of these runs to NIST with an additional expert run using
a 4th system. Our submitted runs performed around
the median. In this paper we will present an overview of
the search system utilized, the experimental setup and a
preliminary analysis of our results
K-Space at TRECVid 2008
In this paper we describe K-Spaceâs participation in TRECVid 2008 in the interactive search task. For 2008 the K-Space group performed one of the largest interactive video information retrieval experiments conducted in a laboratory setting. We had three institutions participating in a multi-site multi-system experiment. In total 36 users participated, 12 each from Dublin City University (DCU, Ireland), University of Glasgow (GU, Scotland) and Centrum Wiskunde & Informatica (CWI, the Netherlands). Three user interfaces were developed, two from DCU which were also used in 2007 as well as an interface from GU. All interfaces leveraged the same search service. Using a latin squares arrangement, each user conducted 12 topics, leading in total to 6 runs per site, 18 in total. We officially submitted for evaluation 3 of these runs to NIST with an additional expert run using a 4th system. Our submitted runs performed around the median. In this paper we will present an overview of the search system utilized, the experimental setup and a preliminary analysis of our results
Insight Centre for Data Analytics (DCU) at TRECVid 2014: instance search and semantic indexing tasks
Insight-DCU participated in the instance search (INS) and semantic indexing (SIN) tasks in 2014. Two very different approaches were submitted for instance search, one based on features extracted using pre-trained deep convolutional neural networks (CNNs), and another based on local SIFT features, large vocabulary visual bag-of-words aggregation, inverted index-based lookup, and geometric verification on the top-N retrieved results. Two interactive runs and two automatic runs were submitted, the best interactive runs achieved a mAP of 0.135 and the best automatic 0.12. Our semantic indexing runs were based also on using convolutional neural network features, and on Support Vector Machine classifiers with linear and RBF kernels. One run was submitted to the main task, two to the no annotation task, and one to the progress task. Data for the no-annotation task was gathered from Google Images and ImageNet. The main task run has achieved a mAP of 0.086, the best no-annotation runs had a close performance to the main run by achieving a mAP of 0.080, while the progress run had 0.043
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