47,831 research outputs found
Fine recycled concrete aggregate as a material replacement in concrete production
As a fast and rapid growing nation, Malaysia undergoes a lot of development especially in construction field. Most of the building nowadays are being made mainly using concrete as it provides many favorable features such as satisfactory compressive strength, durability, availability, versatility and cost effectiveness. However, in pursuing the development era, sometimes the authorities overlooked about the construction and demolition (C&D) waste that had been created along the development progress. Construction and demolition waste is becoming a vital issue especially to the environmental aspect in many large cities in the world (Chen et al., 2002). Shen [1] describe C&D waste as the waste which generated from renovation, site clearing, demolition, construction, roadwork, land excavation and civil and building construction. Construction and Demolition (C&D) waste constitutes a major portion of total solid waste production in the world, and most of it is used in landfills .
Deep View-Sensitive Pedestrian Attribute Inference in an end-to-end Model
Pedestrian attribute inference is a demanding problem in visual surveillance
that can facilitate person retrieval, search and indexing. To exploit semantic
relations between attributes, recent research treats it as a multi-label image
classification task. The visual cues hinting at attributes can be strongly
localized and inference of person attributes such as hair, backpack, shorts,
etc., are highly dependent on the acquired view of the pedestrian. In this
paper we assert this dependence in an end-to-end learning framework and show
that a view-sensitive attribute inference is able to learn better attribute
predictions. Our proposed model jointly predicts the coarse pose (view) of the
pedestrian and learns specialized view-specific multi-label attribute
predictions. We show in an extensive evaluation on three challenging datasets
(PETA, RAP and WIDER) that our proposed end-to-end view-aware attribute
prediction model provides competitive performance and improves on the published
state-of-the-art on these datasets.Comment: accepted BMVC 201
Multispectral Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection is essential for around-the-clock
applications, e.g., surveillance and autonomous driving. We deeply analyze
Faster R-CNN for multispectral pedestrian detection task and then model it into
a convolutional network (ConvNet) fusion problem. Further, we discover that
ConvNet-based pedestrian detectors trained by color or thermal images
separately provide complementary information in discriminating human instances.
Thus there is a large potential to improve pedestrian detection by using color
and thermal images in DNNs simultaneously. We carefully design four ConvNet
fusion architectures that integrate two-branch ConvNets on different DNNs
stages, all of which yield better performance compared with the baseline
detector. Our experimental results on KAIST pedestrian benchmark show that the
Halfway Fusion model that performs fusion on the middle-level convolutional
features outperforms the baseline method by 11% and yields a missing rate 3.5%
lower than the other proposed architectures.Comment: 13 pages, 8 figures, BMVC 2016 ora
Deformable Part-based Fully Convolutional Network for Object Detection
Existing region-based object detectors are limited to regions with fixed box
geometry to represent objects, even if those are highly non-rectangular. In
this paper we introduce DP-FCN, a deep model for object detection which
explicitly adapts to shapes of objects with deformable parts. Without
additional annotations, it learns to focus on discriminative elements and to
align them, and simultaneously brings more invariance for classification and
geometric information to refine localization. DP-FCN is composed of three main
modules: a Fully Convolutional Network to efficiently maintain spatial
resolution, a deformable part-based RoI pooling layer to optimize positions of
parts and build invariance, and a deformation-aware localization module
explicitly exploiting displacements of parts to improve accuracy of bounding
box regression. We experimentally validate our model and show significant
gains. DP-FCN achieves state-of-the-art performances of 83.1% and 80.9% on
PASCAL VOC 2007 and 2012 with VOC data only.Comment: Accepted to BMVC 2017 (oral
Play and Learn: Using Video Games to Train Computer Vision Models
Video games are a compelling source of annotated data as they can readily
provide fine-grained groundtruth for diverse tasks. However, it is not clear
whether the synthetically generated data has enough resemblance to the
real-world images to improve the performance of computer vision models in
practice. We present experiments assessing the effectiveness on real-world data
of systems trained on synthetic RGB images that are extracted from a video
game. We collected over 60000 synthetic samples from a modern video game with
similar conditions to the real-world CamVid and Cityscapes datasets. We provide
several experiments to demonstrate that the synthetically generated RGB images
can be used to improve the performance of deep neural networks on both image
segmentation and depth estimation. These results show that a convolutional
network trained on synthetic data achieves a similar test error to a network
that is trained on real-world data for dense image classification. Furthermore,
the synthetically generated RGB images can provide similar or better results
compared to the real-world datasets if a simple domain adaptation technique is
applied. Our results suggest that collaboration with game developers for an
accessible interface to gather data is potentially a fruitful direction for
future work in computer vision.Comment: To appear in the British Machine Vision Conference (BMVC), September
2016. -v2: fixed a typo in the reference
Coulomb interaction and stability of CE-type structure in half-doped manganites, reply
In his Comment (cond-mat/0104353), Shen points out that the on-site Coulomb
interaction, that can cause charge order in half-doped manganites, also
destabilizes the magnetic CE-phase observed in these systems. This is a valid
observation, but it is not a priori clear whether in the relevant parameter
regime the C-phase is indeed lower in energy then the CE-phase within our
model. We conclude that the proposed model, which correctly captures the
interplay of spin, charge and orbital degrees of freedom in the half-doped
manganites and gives a reasonable description of their electronic structure, is
by itself not sufficient for the precise determination of the regions of
stabilities of different phases. For this several other factors should be taken
into account.Comment: 1 page, to appear in Phys. Rev. Let
An extension of the Beurling-Chen-Hadwin-Shen theorem for noncommutative Hardyspaces associated with finite von Neumann algebras
In 2015, Yanni Chen, Don Hadwin and Junhao Shen proved a noncommutative version of Beurling\u27s theorem for a continuous unitarily invariant norm α on a tracial von Neumann algebra (M,τ)such that α is one dominating with respect to τ. The role of H^∞ is played by a maximal subdiagonal algebra A . In the talk, we first will show that if α is a continuous normalized unitarily invariant norm on (M,τ), then there exists a faithful normal tracial state ρ on M and a constant c \u3e0 such that α is a c times one norm-dominating norm on (M,ρ). Moreover, ρ (x)= τ (xg), where x in M, g is positive in L^1 (Z,τ), where Z is the center of M . Here c and ρ are not unique. However, if there is a c and ρ so that the Fuglede-Kadison determinant of g is positive, then Beurling-Chen-Hadwin-Shen theorem holds for L^(α ) (M,τ). The key ingredients in the proof of our result include a factorization theorem and a density theorem for for L^(α ) (M,ρ)
A slight improvement to Korenblum's constant
Let be the Bergman space over the open unit disk in the complex
plane. Korenblum conjectured that there is an absolute constant
such that whenever in the annulus then .In 2004 C.Wang gave an upper bound on ,that is, ,
and in 2006 A.Schuster gave a lower bound , .In this paper we
slightly improve the upper bound for
Class-Weighted Convolutional Features for Visual Instance Search
Image retrieval in realistic scenarios targets large dynamic datasets of
unlabeled images. In these cases, training or fine-tuning a model every time
new images are added to the database is neither efficient nor scalable.
Convolutional neural networks trained for image classification over large
datasets have been proven effective feature extractors for image retrieval. The
most successful approaches are based on encoding the activations of
convolutional layers, as they convey the image spatial information. In this
paper, we go beyond this spatial information and propose a local-aware encoding
of convolutional features based on semantic information predicted in the target
image. To this end, we obtain the most discriminative regions of an image using
Class Activation Maps (CAMs). CAMs are based on the knowledge contained in the
network and therefore, our approach, has the additional advantage of not
requiring external information. In addition, we use CAMs to generate object
proposals during an unsupervised re-ranking stage after a first fast search.
Our experiments on two public available datasets for instance retrieval,
Oxford5k and Paris6k, demonstrate the competitiveness of our approach
outperforming the current state-of-the-art when using off-the-shelf models
trained on ImageNet. The source code and model used in this paper are publicly
available at http://imatge-upc.github.io/retrieval-2017-cam/.Comment: To appear in the British Machine Vision Conference (BMVC), September
201
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