1,945 research outputs found
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
In recent years, there has been an increasing interest in image-based plant
phenotyping, applying state-of-the-art machine learning approaches to tackle
challenging problems, such as leaf segmentation (a multi-instance problem) and
counting. Most of these algorithms need labelled data to learn a model for the
task at hand. Despite the recent release of a few plant phenotyping datasets,
large annotated plant image datasets for the purpose of training deep learning
algorithms are lacking. One common approach to alleviate the lack of training
data is dataset augmentation. Herein, we propose an alternative solution to
dataset augmentation for plant phenotyping, creating artificial images of
plants using generative neural networks. We propose the Arabidopsis Rosette
Image Generator (through) Adversarial Network: a deep convolutional network
that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a
recent adversarial network model using convolutional layers). Specifically, we
trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset,
containing Arabidopsis Thaliana plants. We show that our model is able to
generate realistic 128x128 colour images of plants. We train our network
conditioning on leaf count, such that it is possible to generate plants with a
given number of leaves suitable, among others, for training regression based
models. We propose a new Ax dataset of artificial plants images, obtained by
our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting
algorithm, showing that the testing error is reduced when Ax is used as part of
the training data.Comment: 8 pages, 6 figures, 1 table, ICCV CVPPP Workshop 201
Sensor-invariant Fingerprint ROI Segmentation Using Recurrent Adversarial Learning
A fingerprint region of interest (roi) segmentation algorithm is designed to
separate the foreground fingerprint from the background noise. All the learning
based state-of-the-art fingerprint roi segmentation algorithms proposed in the
literature are benchmarked on scenarios when both training and testing
databases consist of fingerprint images acquired from the same sensors.
However, when testing is conducted on a different sensor, the segmentation
performance obtained is often unsatisfactory. As a result, every time a new
fingerprint sensor is used for testing, the fingerprint roi segmentation model
needs to be re-trained with the fingerprint image acquired from the new sensor
and its corresponding manually marked ROI. Manually marking fingerprint ROI is
expensive because firstly, it is time consuming and more importantly, requires
domain expertise. In order to save the human effort in generating annotations
required by state-of-the-art, we propose a fingerprint roi segmentation model
which aligns the features of fingerprint images derived from the unseen sensor
such that they are similar to the ones obtained from the fingerprints whose
ground truth roi masks are available for training. Specifically, we propose a
recurrent adversarial learning based feature alignment network that helps the
fingerprint roi segmentation model to learn sensor-invariant features.
Consequently, sensor-invariant features learnt by the proposed roi segmentation
model help it to achieve improved segmentation performance on fingerprints
acquired from the new sensor. Experiments on publicly available FVC databases
demonstrate the efficacy of the proposed work.Comment: IJCNN 2021 (Accepted
Data Uncertainty Guided Noise-aware Preprocessing Of Fingerprints
The effectiveness of fingerprint-based authentication systems on good quality
fingerprints is established long back. However, the performance of standard
fingerprint matching systems on noisy and poor quality fingerprints is far from
satisfactory. Towards this, we propose a data uncertainty-based framework which
enables the state-of-the-art fingerprint preprocessing models to quantify noise
present in the input image and identify fingerprint regions with background
noise and poor ridge clarity. Quantification of noise helps the model two
folds: firstly, it makes the objective function adaptive to the noise in a
particular input fingerprint and consequently, helps to achieve robust
performance on noisy and distorted fingerprint regions. Secondly, it provides a
noise variance map which indicates noisy pixels in the input fingerprint image.
The predicted noise variance map enables the end-users to understand erroneous
predictions due to noise present in the input image. Extensive experimental
evaluation on 13 publicly available fingerprint databases, across different
architectural choices and two fingerprint processing tasks demonstrate
effectiveness of the proposed framework.Comment: IJCNN 2021 (Accepted
CoupleNet: Coupling Global Structure with Local Parts for Object Detection
The region-based Convolutional Neural Network (CNN) detectors such as Faster
R-CNN or R-FCN have already shown promising results for object detection by
combining the region proposal subnetwork and the classification subnetwork
together. Although R-FCN has achieved higher detection speed while keeping the
detection performance, the global structure information is ignored by the
position-sensitive score maps. To fully explore the local and global
properties, in this paper, we propose a novel fully convolutional network,
named as CoupleNet, to couple the global structure with local parts for object
detection. Specifically, the object proposals obtained by the Region Proposal
Network (RPN) are fed into the the coupling module which consists of two
branches. One branch adopts the position-sensitive RoI (PSRoI) pooling to
capture the local part information of the object, while the other employs the
RoI pooling to encode the global and context information. Next, we design
different coupling strategies and normalization ways to make full use of the
complementary advantages between the global and local branches. Extensive
experiments demonstrate the effectiveness of our approach. We achieve
state-of-the-art results on all three challenging datasets, i.e. a mAP of 82.7%
on VOC07, 80.4% on VOC12, and 34.4% on COCO. Codes will be made publicly
available.Comment: Accepted by ICCV 201
Biologically-inspired neural coding of sound onset for a musical sound classification task
A biologically-inspired neural coding scheme for the early auditory system is outlined. The cochlea response is simulated with a passive gammatone filterbank. The output of each bandpass filter is spike-encoded using a zero-crossing based method over a range of sensitivity levels. The scheme is inspired by the highly parallellised nature of the auditory nerve innervation within the cochlea. A key aspect of early auditory processing is simulated, namely that of onset detection, using leaky integrate-and-fire neuron models. Finally, a time-domain neural network (the echo state network) is used to tackle the what task of auditory perception using the output of the onset detection neuron alone
Online Visual Robot Tracking and Identification using Deep LSTM Networks
Collaborative robots working on a common task are necessary for many
applications. One of the challenges for achieving collaboration in a team of
robots is mutual tracking and identification. We present a novel pipeline for
online visionbased detection, tracking and identification of robots with a
known and identical appearance. Our method runs in realtime on the limited
hardware of the observer robot. Unlike previous works addressing robot tracking
and identification, we use a data-driven approach based on recurrent neural
networks to learn relations between sequential inputs and outputs. We formulate
the data association problem as multiple classification problems. A deep LSTM
network was trained on a simulated dataset and fine-tuned on small set of real
data. Experiments on two challenging datasets, one synthetic and one real,
which include long-term occlusions, show promising results.Comment: IEEE/RSJ International Conference on Intelligent Robots and Systems
(IROS), Vancouver, Canada, 2017. IROS RoboCup Best Paper Awar
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