9,799 research outputs found
Albumentations: fast and flexible image augmentations
Data augmentation is a commonly used technique for increasing both the size
and the diversity of labeled training sets by leveraging input transformations
that preserve output labels. In computer vision domain, image augmentations
have become a common implicit regularization technique to combat overfitting in
deep convolutional neural networks and are ubiquitously used to improve
performance. While most deep learning frameworks implement basic image
transformations, the list is typically limited to some variations and
combinations of flipping, rotating, scaling, and cropping. Moreover, the image
processing speed varies in existing tools for image augmentation. We present
Albumentations, a fast and flexible library for image augmentations with many
various image transform operations available, that is also an easy-to-use
wrapper around other augmentation libraries. We provide examples of image
augmentations for different computer vision tasks and show that Albumentations
is faster than other commonly used image augmentation tools on the most of
commonly used image transformations. The source code for Albumentations is made
publicly available online at https://github.com/albu/albumentation
Real-Time and Efficient Method for Accuracy Enhancement of Edge Based License Plate Recognition System
License Plate Recognition plays an important role on the traffic monitoring
and parking management. Administration and restriction of those transportation
tools for their better service becomes very essential. In this paper, a fast
and real time method has an appropriate application to find plates that the
plat has tilt and the picture quality is poor. In the proposed method, at the
beginning, the image is converted into binary mode with use of adaptive
threshold. And with use of edge detection and morphology operation, plate
number location has been specified and if the plat has tilt; its tilt is
removed away. Then its characters are distinguished using image processing
techniques. Finally, K Nearest Neighbour (KNN) classifier was used for
character recognition. This method has been tested on available data set that
has different images of the background, considering distance, and angel of view
so that the correct extraction rate of plate reached at 98% and character
recognition rate achieved at 99.12%. Further we tested our character
recognition stage on Persian vehicle data set and we achieved 99% correct
recognition rate.Comment: 2013 First International Conference on computer, Information
Technology and Digital Media. arXiv admin note: substantial text overlap with
arXiv:1407.632
Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks
Diabetic Retinopathy (DR) is a prominent cause of blindness in the world. The
early treatment of DR can be conducted from detection of microaneurysms (MAs)
which appears as reddish spots in retinal images. An automated microaneurysm
detection can be a helpful system for ophthalmologists. In this paper, deep
learning, in particular convolutional neural network (CNN), is used as a
powerful tool to efficiently detect MAs from fundus images. In our method a new
technique is used to utilise a two-stage training process which results in an
accurate detection, while decreasing computational complexity in comparison
with previous works. To validate our proposed method, an experiment is
conducted using Keras library to implement our proposed CNN on two standard
publicly available datasets. Our results show a promising sensitivity value of
about 0.8 at the average number of false positive per image greater than 6
which is a competitive value with the state-of-the-art approaches
The application of deep convolutional neural networks to ultrasound for modelling of dynamic states within human skeletal muscle
This paper concerns the fully automatic direct in vivo measurement of active
and passive dynamic skeletal muscle states using ultrasound imaging. Despite
the long standing medical need (myopathies, neuropathies, pain, injury,
ageing), currently technology (electromyography, dynamometry, shear wave
imaging) provides no general, non-invasive method for online estimation of
skeletal intramuscular states. Ultrasound provides a technology in which static
and dynamic muscle states can be observed non-invasively, yet current
computational image understanding approaches are inadequate. We propose a new
approach in which deep learning methods are used for understanding the content
of ultrasound images of muscle in terms of its measured state. Ultrasound data
synchronized with electromyography of the calf muscles, with measures of joint
torque/angle were recorded from 19 healthy participants (6 female, ages: 30 +-
7.7). A segmentation algorithm previously developed by our group was applied to
extract a region of interest of the medial gastrocnemius. Then a deep
convolutional neural network was trained to predict the measured states (joint
angle/torque, electromyography) directly from the segmented images. Results
revealed for the first time that active and passive muscle states can be
measured directly from standard b-mode ultrasound images, accurately predicting
for a held out test participant changes in the joint angle, electromyography,
and torque with as little error as 0.022{\deg}, 0.0001V, 0.256Nm (root mean
square error) respectively.Comment: paper in preparation for submission to IEEE TM
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
This paper addresses the task of nuclei segmentation in high-resolution
histopathological images. We propose an auto- matic end-to-end deep neural
network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary
model is introduced to predict nuclei and their boundaries simultaneously using
a fully convolutional neural network. Given a color normalized image, the model
directly outputs an estimated nuclei map and a boundary map. A simple, fast and
parameter-free post-processing procedure is performed on the estimated nuclei
map to produce the final segmented nuclei. An overlapped patch extraction and
assembling method is also designed for seamless prediction of nuclei in large
whole-slide images. We also show the effectiveness of data augmentation methods
for nuclei segmentation task. Our experiments showed our method outperforms
prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000
image can be segmented in less than 5 seconds. This makes it possible to
precisely segment the whole-slide image in acceptable timeComment: 13 pages. 12 figure
Breast Tumor Classification and Segmentation using Convolutional Neural Networks
Breast cancer is considered as the most fatal type of cancer among women
worldwide and it is crucially important to be diagnosed at its early stages. In
the current study, we aim to represent a fast and efficient framework which
consists of two main parts:1- image classification, and 2- tumor region
segmentation. At the initial stage, the images are classified into the two
categories of normal and abnormal. Since the Deep Neural Networks have
performed successfully in machine vision task, we would employ the
convolutional neural networks for the classification of images. In the second
stage, the suggested framework is to diagnose and segment the tumor in the
mammography images. First, the mammography images are pre-processed by removing
noise and artifacts, and then, segment the image using the level-set algorithm
based on the spatial fuzzy c-means clustering. The proper initialization and
optimal configuration have strong effects on the performance of the level-set
segmentation. Thus, in our suggested framework, we have improved the level-set
algorithm by utilizing the spatial fuzzy c-means clustering which ultimately
results in a more precise segmentation. In order to evaluate the proposed
approach, we conducted experiments using the Mammographic Image Analysis (MIAS)
dataset. The tests have shown that the convolutional neural networks could
achieve high accuracy in classification of images. Moreover, the improved
level-set segmentation method, along with the fuzzy c-means clustering, could
perfectly do the segmentation on the tumor area. The suggested method has
classified the images with the accuracy of 78% and the AUC of 69%, which, as
compared to the previous methods, is 2% more accurate and 6% better AUC; and
has been able to extract the tumor area in a more precise way.Comment: 12 Page
Automatic Moth Detection from Trap Images for Pest Management
Monitoring the number of insect pests is a crucial component in
pheromone-based pest management systems. In this paper, we propose an automatic
detection pipeline based on deep learning for identifying and counting pests in
images taken inside field traps. Applied to a commercial codling moth dataset,
our method shows promising performance both qualitatively and quantitatively.
Compared to previous attempts at pest detection, our approach uses no
pest-specific engineering which enables it to adapt to other species and
environments with minimal human effort. It is amenable to implementation on
parallel hardware and therefore capable of deployment in settings where
real-time performance is required.Comment: Preprints accepted by Computers and electronics in agricultur
Real time face recognition using adaboost improved fast PCA algorithm
This paper presents an automated system for human face recognition in a real
time background world for a large homemade dataset of persons face. The task is
very difficult as the real time background subtraction in an image is still a
challenge. Addition to this there is a huge variation in human face image in
terms of size, pose and expression. The system proposed collapses most of this
variance. To detect real time human face AdaBoost with Haar cascade is used and
a simple fast PCA and LDA is used to recognize the faces detected. The matched
face is then used to mark attendance in the laboratory, in our case. This
biometric system is a real time attendance system based on the human face
recognition with a simple and fast algorithms and gaining a high accuracy
rate..Comment: 14 pages; ISSN : 0975-900X (Online), 0976-2191 (Print
1D Convolutional Neural Networks and Applications: A Survey
During the last decade, Convolutional Neural Networks (CNNs) have become the
de facto standard for various Computer Vision and Machine Learning operations.
CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating
convolutional and subsampling layers. Deep 2D CNNs with many hidden layers and
millions of parameters have the ability to learn complex objects and patterns
providing that they can be trained on a massive size visual database with
ground-truth labels. With a proper training, this unique ability makes them the
primary tool for various engineering applications for 2D signals such as images
and video frames. Yet, this may not be a viable option in numerous applications
over 1D signals especially when the training data is scarce or
application-specific. To address this issue, 1D CNNs have recently been
proposed and immediately achieved the state-of-the-art performance levels in
several applications such as personalized biomedical data classification and
early diagnosis, structural health monitoring, anomaly detection and
identification in power electronics and motor-fault detection. Another major
advantage is that a real-time and low-cost hardware implementation is feasible
due to the simple and compact configuration of 1D CNNs that perform only 1D
convolutions (scalar multiplications and additions). This paper presents a
comprehensive review of the general architecture and principals of 1D CNNs
along with their major engineering applications, especially focused on the
recent progress in this field. Their state-of-the-art performance is
highlighted concluding with their unique properties. The benchmark datasets and
the principal 1D CNN software used in those applications are also publically
shared in a dedicated website.Comment: 20 pages, 17 figures, MSSP (Elsevier) submissio
Learning a Rotation Invariant Detector with Rotatable Bounding Box
Detection of arbitrarily rotated objects is a challenging task due to the
difficulties of locating the multi-angle objects and separating them
effectively from the background. The existing methods are not robust to angle
varies of the objects because of the use of traditional bounding box, which is
a rotation variant structure for locating rotated objects. In this article, a
new detection method is proposed which applies the newly defined rotatable
bounding box (RBox). The proposed detector (DRBox) can effectively handle the
situation where the orientation angles of the objects are arbitrary. The
training of DRBox forces the detection networks to learn the correct
orientation angle of the objects, so that the rotation invariant property can
be achieved. DRBox is tested to detect vehicles, ships and airplanes on
satellite images, compared with Faster R-CNN and SSD, which are chosen as the
benchmark of the traditional bounding box based methods. The results shows that
DRBox performs much better than traditional bounding box based methods do on
the given tasks, and is more robust against rotation of input image and target
objects. Besides, results show that DRBox correctly outputs the orientation
angles of the objects, which is very useful for locating multi-angle objects
efficiently. The code and models are available at
https://github.com/liulei01/DRBox
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