1,079 research outputs found
Deep Video Color Propagation
Traditional approaches for color propagation in videos rely on some form of
matching between consecutive video frames. Using appearance descriptors, colors
are then propagated both spatially and temporally. These methods, however, are
computationally expensive and do not take advantage of semantic information of
the scene. In this work we propose a deep learning framework for color
propagation that combines a local strategy, to propagate colors frame-by-frame
ensuring temporal stability, and a global strategy, using semantics for color
propagation within a longer range. Our evaluation shows the superiority of our
strategy over existing video and image color propagation methods as well as
neural photo-realistic style transfer approaches.Comment: BMVC 201
Review of Person Re-identification Techniques
Person re-identification across different surveillance cameras with disjoint
fields of view has become one of the most interesting and challenging subjects
in the area of intelligent video surveillance. Although several methods have
been developed and proposed, certain limitations and unresolved issues remain.
In all of the existing re-identification approaches, feature vectors are
extracted from segmented still images or video frames. Different similarity or
dissimilarity measures have been applied to these vectors. Some methods have
used simple constant metrics, whereas others have utilised models to obtain
optimised metrics. Some have created models based on local colour or texture
information, and others have built models based on the gait of people. In
general, the main objective of all these approaches is to achieve a
higher-accuracy rate and lowercomputational costs. This study summarises
several developments in recent literature and discusses the various available
methods used in person re-identification. Specifically, their advantages and
disadvantages are mentioned and compared.Comment: Published 201
Multilayer Complex Network Descriptors for Color-Texture Characterization
A new method based on complex networks is proposed for color-texture
analysis. The proposal consists on modeling the image as a multilayer complex
network where each color channel is a layer, and each pixel (in each color
channel) is represented as a network vertex. The network dynamic evolution is
accessed using a set of modeling parameters (radii and thresholds), and new
characterization techniques are introduced to capt information regarding within
and between color channel spatial interaction. An automatic and adaptive
approach for threshold selection is also proposed. We conduct classification
experiments on 5 well-known datasets: Vistex, Usptex, Outex13, CURet and MBT.
Results among various literature methods are compared, including deep
convolutional neural networks with pre-trained architectures. The proposed
method presented the highest overall performance over the 5 datasets, with 97.7
of mean accuracy against 97.0 achieved by the ResNet convolutional neural
network with 50 layers.Comment: 20 pages, 7 figures and 4 table
Enhanced target detection in CCTV network system using colour constancy
The focus of this research is to study how targets can be more faithfully detected in a multi-camera CCTV network system using spectral feature for the detection. The objective of the work is to develop colour constancy (CC) methodology to help maintain the spectral feature of the scene into a constant stable state irrespective of variable illuminations and camera calibration issues.
Unlike previous work in the field of target detection, two versions of CC algorithms have been developed during the course of this work which are capable to maintain colour constancy for every image pixel in the scene: 1) a method termed as Enhanced Luminance Reflectance CC (ELRCC) which consists of a pixel-wise sigmoid function for an adaptive dynamic range compression, 2) Enhanced Target Detection and Recognition Colour Constancy (ETDCC) algorithm which employs a bidirectional pixel-wise non-linear transfer PWNLTF function, a centre-surround luminance enhancement and a Grey Edge white balancing routine.
The effectiveness of target detections for all developed CC algorithms have been validated using multi-camera âImagery Library for Intelligent Detection Systemsâ (iLIDS), âPerformance Evaluation of Tracking and Surveillanceâ (PETS) and âGround Truth Colour Chartâ (GTCC) datasets. It is shown that the developed CC algorithms have enhanced target detection efficiency by over 175% compared with that without CC enhancement.
The contribution of this research has been one journal paper published in the Optical Engineering together with 3 conference papers in the subject of research
Strengthening the Effectiveness of Pedestrian Detection with Spatially Pooled Features
We propose a simple yet effective approach to the problem of pedestrian
detection which outperforms the current state-of-the-art. Our new features are
built on the basis of low-level visual features and spatial pooling.
Incorporating spatial pooling improves the translational invariance and thus
the robustness of the detection process. We then directly optimise the partial
area under the ROC curve (\pAUC) measure, which concentrates detection
performance in the range of most practical importance. The combination of these
factors leads to a pedestrian detector which outperforms all competitors on all
of the standard benchmark datasets. We advance state-of-the-art results by
lowering the average miss rate from to on the INRIA benchmark,
to on the ETH benchmark, to on the TUD-Brussels
benchmark and to on the Caltech-USA benchmark.Comment: 16 pages. Appearing in Proc. European Conf. Computer Vision (ECCV)
201
Evaluating color texture descriptors under large variations of controlled lighting conditions
The recognition of color texture under varying lighting conditions is still
an open issue. Several features have been proposed for this purpose, ranging
from traditional statistical descriptors to features extracted with neural
networks. Still, it is not completely clear under what circumstances a feature
performs better than the others. In this paper we report an extensive
comparison of old and new texture features, with and without a color
normalization step, with a particular focus on how they are affected by small
and large variation in the lighting conditions. The evaluation is performed on
a new texture database including 68 samples of raw food acquired under 46
conditions that present single and combined variations of light color,
direction and intensity. The database allows to systematically investigate the
robustness of texture descriptors across a large range of variations of imaging
conditions.Comment: Submitted to the Journal of the Optical Society of America
Example-based image colorization using locality consistent sparse representation
âImage colorization aims to produce a natural looking color image from a given grayscale image, which remains a challenging problem. In this paper, we propose a novel examplebased image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target grayscale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target grayscale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms state-ofthe-art methods, both visually and quantitatively using a user stud
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