33,667 research outputs found
Cross-calibration of Time-of-flight and Colour Cameras
Time-of-flight cameras provide depth information, which is complementary to
the photometric appearance of the scene in ordinary images. It is desirable to
merge the depth and colour information, in order to obtain a coherent scene
representation. However, the individual cameras will have different viewpoints,
resolutions and fields of view, which means that they must be mutually
calibrated. This paper presents a geometric framework for this multi-view and
multi-modal calibration problem. It is shown that three-dimensional projective
transformations can be used to align depth and parallax-based representations
of the scene, with or without Euclidean reconstruction. A new evaluation
procedure is also developed; this allows the reprojection error to be
decomposed into calibration and sensor-dependent components. The complete
approach is demonstrated on a network of three time-of-flight and six colour
cameras. The applications of such a system, to a range of automatic
scene-interpretation problems, are discussed.Comment: 18 pages, 12 figures, 3 table
Recent Advances in Multi-modal 3D Scene Understanding: A Comprehensive Survey and Evaluation
Multi-modal 3D scene understanding has gained considerable attention due to
its wide applications in many areas, such as autonomous driving and
human-computer interaction. Compared to conventional single-modal 3D
understanding, introducing an additional modality not only elevates the
richness and precision of scene interpretation but also ensures a more robust
and resilient understanding. This becomes especially crucial in varied and
challenging environments where solely relying on 3D data might be inadequate.
While there has been a surge in the development of multi-modal 3D methods over
past three years, especially those integrating multi-camera images (3D+2D) and
textual descriptions (3D+language), a comprehensive and in-depth review is
notably absent. In this article, we present a systematic survey of recent
progress to bridge this gap. We begin by briefly introducing a background that
formally defines various 3D multi-modal tasks and summarizes their inherent
challenges. After that, we present a novel taxonomy that delivers a thorough
categorization of existing methods according to modalities and tasks, exploring
their respective strengths and limitations. Furthermore, comparative results of
recent approaches on several benchmark datasets, together with insightful
analysis, are offered. Finally, we discuss the unresolved issues and provide
several potential avenues for future research
Contrast Enhanced Low-light Visible and Infrared Image Fusion
Multi-modal image fusion objective is to combine complementary information obtained from multiple modalities into a single representation with increased reliability and interpretation. The images obtained from low-light visible cameras containing fine details of the scene and infrared cameras with high contrast details are the two modalities considered for fusion. In this paper, the low-light images with low target contrast are enhanced by using the phenomenon of stochastic resonance prior to fusion. Entropy is used as a measure to tune iteratively the coefficients using bistable system parameters. The combined advantage of multi scale decomposition approach and principal component analysis is utilized for the fusion of enhanced low-light visible and infrared images. Experimental results were carried out on different image datasets and analysis of the proposed methods were discussed.
Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications
This paper presents a novel pairwise constraint propagation approach by
decomposing the challenging constraint propagation problem into a set of
independent semi-supervised learning subproblems which can be solved in
quadratic time using label propagation based on k-nearest neighbor graphs.
Considering that this time cost is proportional to the number of all possible
pairwise constraints, our approach actually provides an efficient solution for
exhaustively propagating pairwise constraints throughout the entire dataset.
The resulting exhaustive set of propagated pairwise constraints are further
used to adjust the similarity matrix for constrained spectral clustering. Other
than the traditional constraint propagation on single-source data, our approach
is also extended to more challenging constraint propagation on multi-source
data where each pairwise constraint is defined over a pair of data points from
different sources. This multi-source constraint propagation has an important
application to cross-modal multimedia retrieval. Extensive results have shown
the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201
Mixed reality participants in smart meeting rooms and smart home enviroments
Humanâcomputer interaction requires modeling of the user. A user profile typically contains preferences, interests, characteristics, and interaction behavior. However, in its multimodal interaction with a smart environment the user displays characteristics that show how the user, not necessarily consciously, verbally and nonverbally provides the smart environment with useful input and feedback. Especially in ambient intelligence environments we encounter situations where the environment supports interaction between the environment, smart objects (e.g., mobile robots, smart furniture) and human participants in the environment. Therefore it is useful for the profile to contain a physical representation of the user obtained by multi-modal capturing techniques. We discuss the modeling and simulation of interacting participants in a virtual meeting room, we discuss how remote meeting participants can take part in meeting activities and they have some observations on translating research results to smart home environments
Acoustic Scene Classification by Implicitly Identifying Distinct Sound Events
In this paper, we propose a new strategy for acoustic scene classification
(ASC) , namely recognizing acoustic scenes through identifying distinct sound
events. This differs from existing strategies, which focus on characterizing
global acoustical distributions of audio or the temporal evolution of
short-term audio features, without analysis down to the level of sound events.
To identify distinct sound events for each scene, we formulate ASC in a
multi-instance learning (MIL) framework, where each audio recording is mapped
into a bag-of-instances representation. Here, instances can be seen as
high-level representations for sound events inside a scene. We also propose a
MIL neural networks model, which implicitly identifies distinct instances
(i.e., sound events). Furthermore, we propose two specially designed modules
that model the multi-temporal scale and multi-modal natures of the sound events
respectively. The experiments were conducted on the official development set of
the DCASE2018 Task1 Subtask B, and our best-performing model improves over the
official baseline by 9.4% (68.3% vs 58.9%) in terms of classification accuracy.
This study indicates that recognizing acoustic scenes by identifying distinct
sound events is effective and paves the way for future studies that combine
this strategy with previous ones.Comment: code URL typo, code is available at
https://github.com/hackerekcah/distinct-events-asc.gi
Conceptual coordination bridges information processing and neurophysiology
Information processing theories of memory and skills can be reformulated in terms of how categories are physically and temporally related, a process called conceptual coordination. Dreaming can then be understood as a story understanding process in which two mechanisms found in everyday comprehension are missing: conceiving sequences (chunking categories in time as a higher-order categorization) and coordinating across modalities (e.g., relating the sound of a word and the image of its meaning). On this basis, we can readily identify isomorphisms between dream phenomenology and neurophysiology, and explain the function of dreaming as facilitating future coordination of sequential, cross-modal categorization (i.e., REM sleep lowers activation thresholds, ÂunlearningÂ)
Learning Aligned Cross-Modal Representations from Weakly Aligned Data
People can recognize scenes across many different modalities beyond natural
images. In this paper, we investigate how to learn cross-modal scene
representations that transfer across modalities. To study this problem, we
introduce a new cross-modal scene dataset. While convolutional neural networks
can categorize cross-modal scenes well, they also learn an intermediate
representation not aligned across modalities, which is undesirable for
cross-modal transfer applications. We present methods to regularize cross-modal
convolutional neural networks so that they have a shared representation that is
agnostic of the modality. Our experiments suggest that our scene representation
can help transfer representations across modalities for retrieval. Moreover,
our visualizations suggest that units emerge in the shared representation that
tend to activate on consistent concepts independently of the modality.Comment: Conference paper at CVPR 201
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