21,739 research outputs found
Fast Shadow Detection from a Single Image Using a Patched Convolutional Neural Network
In recent years, various shadow detection methods from a single image have
been proposed and used in vision systems; however, most of them are not
appropriate for the robotic applications due to the expensive time complexity.
This paper introduces a fast shadow detection method using a deep learning
framework, with a time cost that is appropriate for robotic applications. In
our solution, we first obtain a shadow prior map with the help of multi-class
support vector machine using statistical features. Then, we use a semantic-
aware patch-level Convolutional Neural Network that efficiently trains on
shadow examples by combining the original image and the shadow prior map.
Experiments on benchmark datasets demonstrate the proposed method significantly
decreases the time complexity of shadow detection, by one or two orders of
magnitude compared with state-of-the-art methods, without losing accuracy.Comment: 6 pages, 5 figures, Submitted to IROS 201
Survey of Object Detection Methods in Camouflaged Image
Camouflage is an attempt to conceal the signature of a target object into the background image. Camouflage detection
methods or Decamouflaging method is basically used to detect foreground object hidden in the background image. In this
research paper authors presented survey of camouflage detection methods for different applications and areas
A new approach for improving coronary plaque component analysis based on intravascular ultrasound images
Virtual histology intravascular ultrasound (VH-IVUS) is a clinically available technique for atherosclerosis plaque characterization. It, however, suffers from a poor longitudinal resolution due to electrocardiogram (ECG)-gated acquisition. This article presents an effective algorithm for IVUS image-based histology to overcome this limitation. After plaque area extraction within an input IVUS image, a textural analysis procedure consisting of feature extraction and classification steps is proposed. The pixels of the extracted plaque area excluding the shadow region were classified into one of the three plaque components of fibro-fatty (FF), calcification (CA) or necrotic core (NC) tissues. The average classification accuracy for pixel and region based validations is 75% and 87% respectively. Sensitivities (specificities) were 79% (85%) for CA, 81% (90%) for FF and 52% (82%) for NC. The kappa (kappa) = 0.61 and p value = 0.02 indicate good agreement of the proposed method with VH images. Finally, the enhancement in the longitudinal resolution was evaluated by reconstructing the IVUS images between the two sequential IVUS-VH images
A Fusion Framework for Camouflaged Moving Foreground Detection in the Wavelet Domain
Detecting camouflaged moving foreground objects has been known to be
difficult due to the similarity between the foreground objects and the
background. Conventional methods cannot distinguish the foreground from
background due to the small differences between them and thus suffer from
under-detection of the camouflaged foreground objects. In this paper, we
present a fusion framework to address this problem in the wavelet domain. We
first show that the small differences in the image domain can be highlighted in
certain wavelet bands. Then the likelihood of each wavelet coefficient being
foreground is estimated by formulating foreground and background models for
each wavelet band. The proposed framework effectively aggregates the
likelihoods from different wavelet bands based on the characteristics of the
wavelet transform. Experimental results demonstrated that the proposed method
significantly outperformed existing methods in detecting camouflaged foreground
objects. Specifically, the average F-measure for the proposed algorithm was
0.87, compared to 0.71 to 0.8 for the other state-of-the-art methods.Comment: 13 pages, accepted by IEEE TI
Efficient Evaluation of the Number of False Alarm Criterion
This paper proposes a method for computing efficiently the significance of a
parametric pattern inside a binary image. On the one hand, a-contrario
strategies avoid the user involvement for tuning detection thresholds, and
allow one to account fairly for different pattern sizes. On the other hand,
a-contrario criteria become intractable when the pattern complexity in terms of
parametrization increases. In this work, we introduce a strategy which relies
on the use of a cumulative space of reduced dimensionality, derived from the
coupling of a classic (Hough) cumulative space with an integral histogram
trick. This space allows us to store partial computations which are required by
the a-contrario criterion, and to evaluate the significance with a lower
computational cost than by following a straightforward approach. The method is
illustrated on synthetic examples on patterns with various parametrizations up
to five dimensions. In order to demonstrate how to apply this generic concept
in a real scenario, we consider a difficult crack detection task in still
images, which has been addressed in the literature with various local and
global detection strategies. We model cracks as bounded segments, detected by
the proposed a-contrario criterion, which allow us to introduce additional
spatial constraints based on their relative alignment. On this application, the
proposed strategy yields state-of the-art results, and underlines its potential
for handling complex pattern detection tasks
Interaction between high-level and low-level image analysis for semantic video object extraction
Authors of articles published in EURASIP Journal on Advances in Signal Processing are the copyright holders of their articles and have granted to any third party, in advance and in perpetuity, the right to use, reproduce or disseminate the article, according to the SpringerOpen copyright and license agreement (http://www.springeropen.com/authors/license)
Object-Based Greenhouse Classification from GeoEye-1 and WorldView-2 Stereo Imagery
Remote sensing technologies have been commonly used to perform greenhouse detection and mapping. In this research, stereo pairs acquired by very high-resolution optical satellites GeoEye-1 (GE1) and WorldView-2 (WV2) have been utilized to carry out the land cover classification of an agricultural area through an object-based image analysis approach, paying special attention to greenhouses extraction. The main novelty of this work lies in the joint use of single-source stereo-photogrammetrically derived heights and multispectral information from both panchromatic and pan-sharpened orthoimages. The main features tested in this research can be grouped into different categories, such as basic spectral information, elevation data (normalized digital surface model; nDSM), band indexes and ratios, texture and shape geometry. Furthermore, spectral information was based on both single orthoimages and multiangle orthoimages. The overall accuracy attained by applying nearest neighbor and support vector machine classifiers to the four multispectral bands of GE1 were very similar to those computed from WV2, for either four or eight multispectral bands. Height data, in the form of nDSM, were the most important feature for greenhouse classification. The best overall accuracy values were close to 90%, and they were not improved by using multiangle orthoimages
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