11,680 research outputs found
A four-stage system for blind colour image segmentation
Abstract. This paper proposes a new method to split colour images into regions. The only input information is the image to be segmented. Hence, this is a blind colour image segmentation method. It consists of four subsystems: preprocessing, cluster detection, cluster fusion and postprocessing. Proofs are given for the significant properties that we have found. It is not necessary to specify the number of regions in advance, which is a significant improvement over the standard competitive-style strategies. Finally, simulation results are given to demonstrate the performance of this method for some images
Monocular Vision as a Range Sensor
One of the most important abilities for a mobile robot is detecting obstacles in order to avoid collisions. Building a map of these obstacles is the next logical step. Most robots to date have used sensors such as passive or active infrared, sonar or laser range finders to locate obstacles in their path. In contrast, this work uses a single colour camera as the only sensor, and consequently the robot must obtain range information from the camera images. We propose simple methods for determining the range to the nearest obstacle in any direction in the robotâs field of view, referred to as the Radial Obstacle Profile. The ROP can then be used to determine the amount of rotation between two successive images, which is important for constructing a 360Âș view of the surrounding environment as part of map construction
The integration of local chromatic motion signals is sensitive to contrast polarity
Peer reviewedPublisher PD
Copasetic analysis: a framework for the blind analysis of microarray imagery
The official published version can be found at the link below.From its conception, bioinformatics has been a multidisciplinary field which blends domain expert knowledge with new and existing processing techniques, all of which are focused on a common goal. Typically, these techniques have focused on the direct analysis of raw microarray image data. Unfortunately, this fails to utilise the image's full potential and in practice, this results in the lab technician having to guide the analysis algorithms. This paper presents a dynamic framework that aims to automate the process of microarray image analysis using a variety of techniques. An overview of the entire framework process is presented, the robustness of which is challenged throughout with a selection of real examples containing varying degrees of noise. The results show the potential of the proposed framework in its ability to determine slide layout accurately and perform analysis without prior structural knowledge. The algorithm achieves approximately, a 1 to 3 dB improved peak signal-to-noise ratio compared to conventional processing techniques like those implemented in GenePixÂź when used by a trained operator. As far as the authors are aware, this is the first time such a comprehensive framework concept has been directly applied to the area of microarray image analysis
Fast traffic sign recognition using color segmentation and deep convolutional networks
The use of Computer Vision techniques for the automatic
recognition of road signs is fundamental for the development of intelli-
gent vehicles and advanced driver assistance systems. In this paper, we
describe a procedure based on color segmentation, Histogram of Ori-
ented Gradients (HOG), and Convolutional Neural Networks (CNN) for
detecting and classifying road signs. Detection is speeded up by a pre-
processing step to reduce the search space, while classication is carried
out by using a Deep Learning technique. A quantitative evaluation of the
proposed approach has been conducted on the well-known German Traf-
c Sign data set and on the novel Data set of Italian Trac Signs (DITS),
which is publicly available and contains challenging sequences captured
in adverse weather conditions and in an urban scenario at night-time.
Experimental results demonstrate the eectiveness of the proposed ap-
proach in terms of both classication accuracy and computational speed
Space Warps II. New Gravitational Lens Candidates from the CFHTLS Discovered through Citizen Science
We report the discovery of 29 promising (and 59 total) new lens candidates
from the CFHT Legacy Survey (CFHTLS) based on about 11 million classifications
performed by citizen scientists as part of the first Space Warps lens search.
The goal of the blind lens search was to identify lens candidates missed by
robots (the RingFinder on galaxy scales and ArcFinder on group/cluster scales)
which had been previously used to mine the CFHTLS for lenses. We compare some
properties of the samples detected by these algorithms to the Space Warps
sample and find them to be broadly similar. The image separation distribution
calculated from the Space Warps sample shows that previous constraints on the
average density profile of lens galaxies are robust. SpaceWarps recovers about
65% of known lenses, while the new candidates show a richer variety compared to
those found by the two robots. This detection rate could be increased to 80% by
only using classifications performed by expert volunteers (albeit at the cost
of a lower purity), indicating that the training and performance calibration of
the citizen scientists is very important for the success of Space Warps. In
this work we present the SIMCT pipeline, used for generating in situ a sample
of realistic simulated lensed images. This training sample, along with the
false positives identified during the search, has a legacy value for testing
future lens finding algorithms. We make the pipeline and the training set
publicly available.Comment: 23 pages, 12 figures, MNRAS accepted, minor to moderate changes in
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Use of colour for hand-filled form analysis and recognition
Colour information in form analysis is currently under utilised. As technology has advanced and computing costs have reduced, the processing of forms in colour has now become practicable. This paper describes a novel colour-based approach to the extraction of filled data from colour form images. Images are first quantised to reduce the colour complexity and data is extracted by examining the colour characteristics of the images. The improved performance of the proposed method has been verified by comparing the processing time, recognition rate, extraction precision and recall rate to that of an equivalent black and white system
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