436,332 research outputs found
River water-level estimation using visual sensing
This paper reports our initial work on the extraction of en-
vironmental information from images sampled from a camera deployed to monitor a river environment. It demonstrates very promising results for the use of a visual sensor in a smart multi-modal sensor network
Isolating contour information from arbitrary images
Aspects of natural vision (physiological and perceptual) serve as a basis for attempting the development of a general processing scheme for contour extraction. Contour information is assumed to be central to visual recognition skills. While the scheme must be regarded as highly preliminary, initial results do compare favorably with the visual perception of structure. The scheme pays special attention to the construction of a smallest scale circular difference-of-Gaussian (DOG) convolution, calibration of multiscale edge detection thresholds with the visual perception of grayscale boundaries, and contour/texture discrimination methods derived from fundamental assumptions of connectivity and the characteristics of printed text. Contour information is required to fall between a minimum connectivity limit and maximum regional spatial density limit at each scale. Results support the idea that contour information, in images possessing good image quality, is (centered at about 10 cyc/deg and 30 cyc/deg). Further, lower spatial frequency channels appear to play a major role only in contour extraction from images with serious global image defects
ViTOR: Learning to Rank Webpages Based on Visual Features
The visual appearance of a webpage carries valuable information about its
quality and can be used to improve the performance of learning to rank (LTR).
We introduce the Visual learning TO Rank (ViTOR) model that integrates
state-of-the-art visual features extraction methods by (i) transfer learning
from a pre-trained image classification model, and (ii) synthetic saliency heat
maps generated from webpage snapshots. Since there is currently no public
dataset for the task of LTR with visual features, we also introduce and release
the ViTOR dataset, containing visually rich and diverse webpages. The ViTOR
dataset consists of visual snapshots, non-visual features and relevance
judgments for ClueWeb12 webpages and TREC Web Track queries. We experiment with
the proposed ViTOR model on the ViTOR dataset and show that it significantly
improves the performance of LTR with visual featuresComment: In Proceedings of the 2019 World Wide Web Conference (WWW 2019), May
2019, San Francisc
Towards Robust Visual Information Extraction in Real World: New Dataset and Novel Solution
Visual information extraction (VIE) has attracted considerable attention
recently owing to its various advanced applications such as document
understanding, automatic marking and intelligent education. Most existing works
decoupled this problem into several independent sub-tasks of text spotting
(text detection and recognition) and information extraction, which completely
ignored the high correlation among them during optimization. In this paper, we
propose a robust visual information extraction system (VIES) towards real-world
scenarios, which is a unified end-to-end trainable framework for simultaneous
text detection, recognition and information extraction by taking a single
document image as input and outputting the structured information.
Specifically, the information extraction branch collects abundant visual and
semantic representations from text spotting for multimodal feature fusion and
conversely, provides higher-level semantic clues to contribute to the
optimization of text spotting. Moreover, regarding the shortage of public
benchmarks, we construct a fully-annotated dataset called EPHOIE
(https://github.com/HCIILAB/EPHOIE), which is the first Chinese benchmark for
both text spotting and visual information extraction. EPHOIE consists of 1,494
images of examination paper head with complex layouts and background, including
a total of 15,771 Chinese handwritten or printed text instances. Compared with
the state-of-the-art methods, our VIES shows significant superior performance
on the EPHOIE dataset and achieves a 9.01% F-score gain on the widely used
SROIE dataset under the end-to-end scenario.Comment: 8 pages, 5 figures, to be published in AAAI 202
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