6,113 research outputs found
Deep Poselets for Human Detection
We address the problem of detecting people in natural scenes using a part
approach based on poselets. We propose a bootstrapping method that allows us to
collect millions of weakly labeled examples for each poselet type. We use these
examples to train a Convolutional Neural Net to discriminate different poselet
types and separate them from the background class. We then use the trained CNN
as a way to represent poselet patches with a Pose Discriminative Feature (PDF)
vector -- a compact 256-dimensional feature vector that is effective at
discriminating pose from appearance. We train the poselet model on top of PDF
features and combine them with object-level CNNs for detection and bounding box
prediction. The resulting model leads to state-of-the-art performance for human
detection on the PASCAL datasets
Search Process as Transitions Between Neural States
Search is one of the most performed activities on the World Wide
Web. Various conceptual models postulate that the search process
can be broken down into distinct emotional and cognitive states
of searchers while they engage in a search process. These models
significantly contribute to our understanding of the search process.
However, they are typically based on self-report measures, such as
surveys, questionnaire, etc. and therefore, only indirectly monitor
the brain activity that supports such a process. With this work,
we take one step further and directly measure the brain activity
involved in a search process. To do so, we break down a search
process into five time periods: a realisation of Information Need,
Query Formulation, Query Submission, Relevance Judgment and
Satisfaction Judgment. We then investigate the brain activity between
these time periods. Using functional Magnetic Resonance
Imaging (fMRI), we monitored the brain activity of twenty-four participants
during a search process that involved answering questions
carefully selected from the TREC-8 and TREC 2001 Q/A Tracks.
This novel analysis that focuses on transitions rather than states
reveals the contrasting brain activity between time periods – which
enables the identification of the distinct parts of the search process
as the user moves through them. This work, therefore, provides an
important first step in representing the search process based on the
transitions between neural states. Discovering more precisely how
brain activity relates to different parts of the search process will
enable the development of brain-computer interactions that better
support search and search interactions, which we believe our study
and conclusions advance
Prefrontal involvement in imitation learning of hand actions : effects of practice and expertise.
In this event-related fMRI study, we demonstrate the effects of a single session of practising configural hand actions (guitar chords) on cortical activations during observation, motor preparation, and imitative execution. During the observation of non-practised actions, the mirror neuron system (MNS), consisting of inferior parietal and ventral premotor areas, was more strongly activated than for the practised actions. This finding indicates a strong role of the MNS in the early stages of imitation learning. In addition, the dorsolateral prefrontal cortex (DLPFC) was selectively involved during observation and motor preparation of the non-practised chords. This finding confirms Buccino et al.’s (2004a) model of imitation learning: for actions that are not yet part of the observer’s motor repertoire, DLPFC engages in operations of selection and combination of existing, elementary representations in the MNS. The pattern of prefrontal activations further supports Shallice’s (2004) proposal of a dominant role of the left DLPFC in modulating lower-level systems, and of a dominant role of the right DLPFC in monitoring operations
Learning Rigid Image Registration - Utilizing Convolutional Neural Networks for Medical Image Registration
Many traditional computer vision tasks, such as segmentation, have seen large step-changes in accuracy and/or speed with the application of Convolutional Neural Networks (CNNs). Image registration, the alignment of two or more images to a common space, is a fundamental step in many medical imaging workflows. In this paper we investigate whether these techniques can also bring tangible benefits to the registration task. We describe and evaluate the use of convolutional neural networks (CNNs) for both mono- and multi- modality registration and compare their performance to more traditional schemes, namely multi-scale, iterative registration. This paper also investigates incorporating inverse consistency of the learned spatial transformations to impose additional constraints on the network during training and investigate any benefit in accuracy during detection. The approaches are validated with a series of artificial mono-modal registration tasks utilizing T1-weighted MR brain i mages from the Open Access Series of Imaging Studies (OASIS) study and IXI brain development dataset and a series of real multi-modality registration tasks using T1-weighted and T2-weighted MR brain images from the 2015 Ischemia Stroke Lesion segmentation (ISLES) challenge. The results demonstrate that CNNs give excellent performance for both mono- and multi- modality head and neck registration compared to the baseline method with significantly fewer outliers and lower mean errors
Music in the first days of life
In adults, specific neural systems with right-hemispheric weighting are necessary to process pitch, melody and harmony, as well as structure and meaning emerging from musical sequences. To which extent does this neural specialization result from exposure to music or from neurobiological predispositions? We used fMRI to measure brain activity in 1 to 3 days old newborns while listening to Western tonal music, and to the same excerpts altered, so as to include tonal violations or dissonance. Music caused predominant right hemisphere activations in primary and higher-order auditory cortex. For altered music, activations were seen in the left inferior frontal cortex and limbic structures. Thus, the newborn's brain is able to plenty receive music and to figure out even small perceptual and structural differences in the music sequences. This neural architecture present at birth provides us the potential to process basic and complex aspects of music, a uniquely human capacity
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