27,058 research outputs found
Detecting Oriented Text in Natural Images by Linking Segments
Most state-of-the-art text detection methods are specific to horizontal Latin
text and are not fast enough for real-time applications. We introduce Segment
Linking (SegLink), an oriented text detection method. The main idea is to
decompose text into two locally detectable elements, namely segments and links.
A segment is an oriented box covering a part of a word or text line; A link
connects two adjacent segments, indicating that they belong to the same word or
text line. Both elements are detected densely at multiple scales by an
end-to-end trained, fully-convolutional neural network. Final detections are
produced by combining segments connected by links. Compared with previous
methods, SegLink improves along the dimensions of accuracy, speed, and ease of
training. It achieves an f-measure of 75.0% on the standard ICDAR 2015
Incidental (Challenge 4) benchmark, outperforming the previous best by a large
margin. It runs at over 20 FPS on 512x512 images. Moreover, without
modification, SegLink is able to detect long lines of non-Latin text, such as
Chinese.Comment: To Appear in CVPR 201
Cleaning sky survey databases using Hough Transform and Renewal String approaches
Large astronomical databases obtained from sky surveys such as the
SuperCOSMOS Sky Survey (SSS) invariably suffer from spurious records coming
from artefactual effects of the telescope, satellites and junk objects in orbit
around earth and physical defects on the photographic plate or CCD. Though
relatively small in number these spurious records present a significant problem
in many situations where they can become a large proportion of the records
potentially of interest to a given astronomer. Accurate and robust techniques
are needed for locating and flagging such spurious objects, and we are
undertaking a programme investigating the use of machine learning techniques in
this context. In this paper we focus on the four most common causes of unwanted
records in the SSS: satellite or aeroplane tracks, scratches, fibres and other
linear phenomena introduced to the plate, circular halos around bright stars
due to internal reflections within the telescope and diffraction spikes near to
bright stars. Appropriate techniques are developed for the detection of each of
these. The methods are applied to the SSS data to develop a dataset of spurious
object detections, along with confidence measures, which can allow these
unwanted data to be removed from consideration. These methods are general and
can be adapted to other astronomical survey data.Comment: Accepted for MNRAS. 17 pages, latex2e, uses mn2e.bst, mn2e.cls,
md706.bbl, shortbold.sty (all included). All figures included here as low
resolution jpegs. A version of this paper including the figures can be
downloaded from http://www.anc.ed.ac.uk/~amos/publications.html and more
details on this project can be found at
http://www.anc.ed.ac.uk/~amos/sattrackres.htm
An Empirical Evaluation of Deep Learning on Highway Driving
Numerous groups have applied a variety of deep learning techniques to
computer vision problems in highway perception scenarios. In this paper, we
presented a number of empirical evaluations of recent deep learning advances.
Computer vision, combined with deep learning, has the potential to bring about
a relatively inexpensive, robust solution to autonomous driving. To prepare
deep learning for industry uptake and practical applications, neural networks
will require large data sets that represent all possible driving environments
and scenarios. We collect a large data set of highway data and apply deep
learning and computer vision algorithms to problems such as car and lane
detection. We show how existing convolutional neural networks (CNNs) can be
used to perform lane and vehicle detection while running at frame rates
required for a real-time system. Our results lend credence to the hypothesis
that deep learning holds promise for autonomous driving.Comment: Added a video for lane detectio
CoMaL Tracking: Tracking Points at the Object Boundaries
Traditional point tracking algorithms such as the KLT use local 2D
information aggregation for feature detection and tracking, due to which their
performance degrades at the object boundaries that separate multiple objects.
Recently, CoMaL Features have been proposed that handle such a case. However,
they proposed a simple tracking framework where the points are re-detected in
each frame and matched. This is inefficient and may also lose many points that
are not re-detected in the next frame. We propose a novel tracking algorithm to
accurately and efficiently track CoMaL points. For this, the level line segment
associated with the CoMaL points is matched to MSER segments in the next frame
using shape-based matching and the matches are further filtered using
texture-based matching. Experiments show improvements over a simple
re-detect-and-match framework as well as KLT in terms of speed/accuracy on
different real-world applications, especially at the object boundaries.Comment: 10 pages, 10 figures, to appear in 1st Joint BMTT-PETS Workshop on
Tracking and Surveillance, CVPR 201
An Image Understanding System for Detecting Indoor Features
The capability of identifying physical structures of an unknown environment is very important for vision based robot navigation and scene understanding. Among physical structures in indoor environments, corridor lines and doors are important visual landmarks for robot navigation since they show the topological structure in an indoor environment and establish connections among the different places or regions in the indoor environment. Furthermore, they provide clues for understanding the image. In this thesis, I present two algorithms to detect the vanishing point, corridor lines, and doors respectively using a single digital video camera. In both algorithms, we utilize a hypothesis generation and verification method to detect corridor and door structures using low level linear features. The proposed method consists of low, intermediate, and high level processing stages which correspond to the extraction of low level features, the formation of hypotheses, and verification of the hypotheses via seeking evidence actively. In particular, we extend this single-pass framework by employing a feedback strategy for more robust hypothesis generation and verification. We demonstrate the robustness of the proposed methods on a large number of real video images in a variety of corridor environments, with image acquisitions under different illumination and reflection conditions, with different moving speeds, and with different viewpoints of the camera. Experimental results performed on the corridor line detection algorithm validate that the method can detect corridor line locations in the presence of many spurious line features about one second. Experimental results carried on the door detection algorithm show that the system can detect visually important doors in an image with a very high accuracy rate when a robot navigates along a corridor environment
- …