31,805 research outputs found
EnsNet: Ensconce Text in the Wild
A new method is proposed for removing text from natural images. The challenge
is to first accurately localize text on the stroke-level and then replace it
with a visually plausible background. Unlike previous methods that require
image patches to erase scene text, our method, namely ensconce network
(EnsNet), can operate end-to-end on a single image without any prior knowledge.
The overall structure is an end-to-end trainable FCN-ResNet-18 network with a
conditional generative adversarial network (cGAN). The feature of the former is
first enhanced by a novel lateral connection structure and then refined by four
carefully designed losses: multiscale regression loss and content loss, which
capture the global discrepancy of different level features; texture loss and
total variation loss, which primarily target filling the text region and
preserving the reality of the background. The latter is a novel local-sensitive
GAN, which attentively assesses the local consistency of the text erased
regions. Both qualitative and quantitative sensitivity experiments on synthetic
images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet
is essential to achieve a good performance. Moreover, our EnsNet can
significantly outperform previous state-of-the-art methods in terms of all
metrics. In addition, a qualitative experiment conducted on the SMBNet dataset
further demonstrates that the proposed method can also preform well on general
object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can
preform at 333 fps on an i5-8600 CPU device.Comment: 8 pages, 8 figures, 2 tables, accepted to appear in AAAI 201
SpaceNet MVOI: a Multi-View Overhead Imagery Dataset
Detection and segmentation of objects in overheard imagery is a challenging
task. The variable density, random orientation, small size, and
instance-to-instance heterogeneity of objects in overhead imagery calls for
approaches distinct from existing models designed for natural scene datasets.
Though new overhead imagery datasets are being developed, they almost
universally comprise a single view taken from directly overhead ("at nadir"),
failing to address a critical variable: look angle. By contrast, views vary in
real-world overhead imagery, particularly in dynamic scenarios such as natural
disasters where first looks are often over 40 degrees off-nadir. This
represents an important challenge to computer vision methods, as changing view
angle adds distortions, alters resolution, and changes lighting. At present,
the impact of these perturbations for algorithmic detection and segmentation of
objects is untested. To address this problem, we present an open source
Multi-View Overhead Imagery dataset, termed SpaceNet MVOI, with 27 unique looks
from a broad range of viewing angles (-32.5 degrees to 54.0 degrees). Each of
these images cover the same 665 square km geographic extent and are annotated
with 126,747 building footprint labels, enabling direct assessment of the
impact of viewpoint perturbation on model performance. We benchmark multiple
leading segmentation and object detection models on: (1) building detection,
(2) generalization to unseen viewing angles and resolutions, and (3)
sensitivity of building footprint extraction to changes in resolution. We find
that state of the art segmentation and object detection models struggle to
identify buildings in off-nadir imagery and generalize poorly to unseen views,
presenting an important benchmark to explore the broadly relevant challenge of
detecting small, heterogeneous target objects in visually dynamic contexts.Comment: Accepted into IEEE International Conference on Computer Vision (ICCV)
201
Scene Text Eraser
The character information in natural scene images contains various personal
information, such as telephone numbers, home addresses, etc. It is a high risk
of leakage the information if they are published. In this paper, we proposed a
scene text erasing method to properly hide the information via an inpainting
convolutional neural network (CNN) model. The input is a scene text image, and
the output is expected to be text erased image with all the character regions
filled up the colors of the surrounding background pixels. This work is
accomplished by a CNN model through convolution to deconvolution with
interconnection process. The training samples and the corresponding inpainting
images are considered as teaching signals for training. To evaluate the text
erasing performance, the output images are detected by a novel scene text
detection method. Subsequently, the same measurement on text detection is
utilized for testing the images in benchmark dataset ICDAR2013. Compared with
direct text detection way, the scene text erasing process demonstrates a
drastically decrease on the precision, recall and f-score. That proves the
effectiveness of proposed method for erasing the text in natural scene images
WordSup: Exploiting Word Annotations for Character based Text Detection
Imagery texts are usually organized as a hierarchy of several visual
elements, i.e. characters, words, text lines and text blocks. Among these
elements, character is the most basic one for various languages such as
Western, Chinese, Japanese, mathematical expression and etc. It is natural and
convenient to construct a common text detection engine based on character
detectors. However, training character detectors requires a vast of location
annotated characters, which are expensive to obtain. Actually, the existing
real text datasets are mostly annotated in word or line level. To remedy this
dilemma, we propose a weakly supervised framework that can utilize word
annotations, either in tight quadrangles or the more loose bounding boxes, for
character detector training. When applied in scene text detection, we are thus
able to train a robust character detector by exploiting word annotations in the
rich large-scale real scene text datasets, e.g. ICDAR15 and COCO-text. The
character detector acts as a key role in the pipeline of our text detection
engine. It achieves the state-of-the-art performance on several challenging
scene text detection benchmarks. We also demonstrate the flexibility of our
pipeline by various scenarios, including deformed text detection and math
expression recognition.Comment: 2017 International Conference on Computer Visio
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