14,478 research outputs found
Indexing, browsing and searching of digital video
Video is a communications medium that normally brings together moving pictures with a synchronised audio track into a discrete piece or pieces of information. The size of a “piece ” of video can variously be referred to as a frame, a shot, a scene, a clip, a programme or an episode, and these are distinguished by their lengths and by their composition. We shall return to the definition of each of these in section 4 this chapter. In modern society, video is ver
Hybrid Scene Compression for Visual Localization
Localizing an image wrt. a 3D scene model represents a core task for many
computer vision applications. An increasing number of real-world applications
of visual localization on mobile devices, e.g., Augmented Reality or autonomous
robots such as drones or self-driving cars, demand localization approaches to
minimize storage and bandwidth requirements. Compressing the 3D models used for
localization thus becomes a practical necessity. In this work, we introduce a
new hybrid compression algorithm that uses a given memory limit in a more
effective way. Rather than treating all 3D points equally, it represents a
small set of points with full appearance information and an additional, larger
set of points with compressed information. This enables our approach to obtain
a more complete scene representation without increasing the memory
requirements, leading to a superior performance compared to previous
compression schemes. As part of our contribution, we show how to handle
ambiguous matches arising from point compression during RANSAC. Besides
outperforming previous compression techniques in terms of pose accuracy under
the same memory constraints, our compression scheme itself is also more
efficient. Furthermore, the localization rates and accuracy obtained with our
approach are comparable to state-of-the-art feature-based methods, while using
a small fraction of the memory.Comment: Published at CVPR 201
Efficient storage and decoding of SURF feature points
Practical use of SURF feature points in large-scale indexing and retrieval engines requires an efficient means for storing and decoding these features. This paper investigates several methods for compression and storage of SURF feature points, considering both storage consumption and disk-read efficiency. We compare each scheme with a baseline plain-text encoding scheme as used by many existing SURF implementations. Our final proposed scheme significantly reduces both the time required to load and decode feature points, and the space required to store them on disk
Coding local and global binary visual features extracted from video sequences
Binary local features represent an effective alternative to real-valued
descriptors, leading to comparable results for many visual analysis tasks,
while being characterized by significantly lower computational complexity and
memory requirements. When dealing with large collections, a more compact
representation based on global features is often preferred, which can be
obtained from local features by means of, e.g., the Bag-of-Visual-Word (BoVW)
model. Several applications, including for example visual sensor networks and
mobile augmented reality, require visual features to be transmitted over a
bandwidth-limited network, thus calling for coding techniques that aim at
reducing the required bit budget, while attaining a target level of efficiency.
In this paper we investigate a coding scheme tailored to both local and global
binary features, which aims at exploiting both spatial and temporal redundancy
by means of intra- and inter-frame coding. In this respect, the proposed coding
scheme can be conveniently adopted to support the Analyze-Then-Compress (ATC)
paradigm. That is, visual features are extracted from the acquired content,
encoded at remote nodes, and finally transmitted to a central controller that
performs visual analysis. This is in contrast with the traditional approach, in
which visual content is acquired at a node, compressed and then sent to a
central unit for further processing, according to the Compress-Then-Analyze
(CTA) paradigm. In this paper we experimentally compare ATC and CTA by means of
rate-efficiency curves in the context of two different visual analysis tasks:
homography estimation and content-based retrieval. Our results show that the
novel ATC paradigm based on the proposed coding primitives can be competitive
with CTA, especially in bandwidth limited scenarios.Comment: submitted to IEEE Transactions on Image Processin
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