36,785 research outputs found
Towards automatic extraction of expressive elements from motion pictures : tempo
This paper proposes a unique computational approach to extraction of expressive elements of motion pictures for deriving high level semantics of stories portrayed, thus enabling better video annotation and interpretation systems. This approach, motivated and directed by the existing cinematic conventions known as film grammar, as a first step towards demonstrating its effectiveness, uses the attributes of motion and shot length to define and compute a novel measure of tempo of a movie. Tempo flow plots are defined and derived for four full-length movies and edge analysis is performed leading to the extraction of dramatic story sections and events signaled by their unique tempo. The results confirm tempo as a useful attribute in its own right and a promising component of semantic constructs such as tone or mood of a film
K-Space at TRECVid 2007
In this paper we describe K-Space participation in
TRECVid 2007. K-Space participated in two tasks, high-level feature extraction and interactive search. We present our approaches for each of these activities and provide a brief analysis of our results. Our high-level feature submission utilized multi-modal low-level features which included visual, audio and temporal elements. Specific concept detectors (such as Face detectors) developed by K-Space partners were also used. We experimented with different machine learning approaches including logistic regression and support vector machines (SVM). Finally we also experimented with both early and late fusion for feature combination. This year we also participated in interactive search, submitting 6 runs. We developed two interfaces which both utilized the same retrieval functionality. Our objective was to measure the effect of context, which was supported to different degrees in each interface, on user performance.
The first of the two systems was a āshotā based interface,
where the results from a query were presented as a ranked
list of shots. The second interface was ābroadcastā based,
where results were presented as a ranked list of broadcasts.
Both systems made use of the outputs of our high-level feature submission as well as low-level visual features
Single-shot compressed ultrafast photography: a review
Compressed ultrafast photography (CUP) is a burgeoning single-shot computational imaging technique that provides an imaging speed as high as 10 trillion frames per second and a sequence depth of up to a few hundred frames. This technique synergizes compressed sensing and the streak camera technique to capture nonrepeatable ultrafast transient events with a single shot. With recent unprecedented technical developments and extensions of this methodology, it has been widely used in ultrafast optical imaging and metrology, ultrafast electron diffraction and microscopy, and information security protection. We review the basic principles of CUP, its recent advances in data acquisition and image reconstruction, its fusions with other modalities, and its unique applications in multiple research fields
Speaker-following Video Subtitles
We propose a new method for improving the presentation of subtitles in video
(e.g. TV and movies). With conventional subtitles, the viewer has to constantly
look away from the main viewing area to read the subtitles at the bottom of the
screen, which disrupts the viewing experience and causes unnecessary eyestrain.
Our method places on-screen subtitles next to the respective speakers to allow
the viewer to follow the visual content while simultaneously reading the
subtitles. We use novel identification algorithms to detect the speakers based
on audio and visual information. Then the placement of the subtitles is
determined using global optimization. A comprehensive usability study indicated
that our subtitle placement method outperformed both conventional
fixed-position subtitling and another previous dynamic subtitling method in
terms of enhancing the overall viewing experience and reducing eyestrain
One-shot Learning for iEEG Seizure Detection Using End-to-end Binary Operations: Local Binary Patterns with Hyperdimensional Computing
This paper presents an efficient binarized algorithm for both learning and
classification of human epileptic seizures from intracranial
electroencephalography (iEEG). The algorithm combines local binary patterns
with brain-inspired hyperdimensional computing to enable end-to-end learning
and inference with binary operations. The algorithm first transforms iEEG time
series from each electrode into local binary pattern codes. Then atomic
high-dimensional binary vectors are used to construct composite representations
of seizures across all electrodes. For the majority of our patients (10 out of
16), the algorithm quickly learns from one or two seizures (i.e., one-/few-shot
learning) and perfectly generalizes on 27 further seizures. For other patients,
the algorithm requires three to six seizures for learning. Overall, our
algorithm surpasses the state-of-the-art methods for detecting 65 novel
seizures with higher specificity and sensitivity, and lower memory footprint.Comment: Published as a conference paper at the IEEE BioCAS 201
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