472 research outputs found
Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection
Multispectral pedestrian detection has received extensive attention in recent
years as a promising solution to facilitate robust human target detection for
around-the-clock applications (e.g. security surveillance and autonomous
driving). In this paper, we demonstrate illumination information encoded in
multispectral images can be utilized to significantly boost performance of
pedestrian detection. A novel illumination-aware weighting mechanism is present
to accurately depict illumination condition of a scene. Such illumination
information is incorporated into two-stream deep convolutional neural networks
to learn multispectral human-related features under different illumination
conditions (daytime and nighttime). Moreover, we utilized illumination
information together with multispectral data to generate more accurate semantic
segmentation which are used to boost pedestrian detection accuracy. Putting all
of the pieces together, we present a powerful framework for multispectral
pedestrian detection based on multi-task learning of illumination-aware
pedestrian detection and semantic segmentation. Our proposed method is trained
end-to-end using a well-designed multi-task loss function and outperforms
state-of-the-art approaches on KAIST multispectral pedestrian dataset
Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection
Effective fusion of complementary information captured by multi-modal sensors
(visible and infrared cameras) enables robust pedestrian detection under
various surveillance situations (e.g. daytime and nighttime). In this paper, we
present a novel box-level segmentation supervised learning framework for
accurate and real-time multispectral pedestrian detection by incorporating
features extracted in visible and infrared channels. Specifically, our method
takes pairs of aligned visible and infrared images with easily obtained
bounding box annotations as input and estimates accurate prediction maps to
highlight the existence of pedestrians. It offers two major advantages over the
existing anchor box based multispectral detection methods. Firstly, it
overcomes the hyperparameter setting problem occurred during the training phase
of anchor box based detectors and can obtain more accurate detection results,
especially for small and occluded pedestrian instances. Secondly, it is capable
of generating accurate detection results using small-size input images, leading
to improvement of computational efficiency for real-time autonomous driving
applications. Experimental results on KAIST multispectral dataset show that our
proposed method outperforms state-of-the-art approaches in terms of both
accuracy and speed
Multi-branch Convolutional Neural Network for Multiple Sclerosis Lesion Segmentation
In this paper, we present an automated approach for segmenting multiple
sclerosis (MS) lesions from multi-modal brain magnetic resonance images. Our
method is based on a deep end-to-end 2D convolutional neural network (CNN) for
slice-based segmentation of 3D volumetric data. The proposed CNN includes a
multi-branch downsampling path, which enables the network to encode information
from multiple modalities separately. Multi-scale feature fusion blocks are
proposed to combine feature maps from different modalities at different stages
of the network. Then, multi-scale feature upsampling blocks are introduced to
upsize combined feature maps to leverage information from lesion shape and
location. We trained and tested the proposed model using orthogonal plane
orientations of each 3D modality to exploit the contextual information in all
directions. The proposed pipeline is evaluated on two different datasets: a
private dataset including 37 MS patients and a publicly available dataset known
as the ISBI 2015 longitudinal MS lesion segmentation challenge dataset,
consisting of 14 MS patients. Considering the ISBI challenge, at the time of
submission, our method was amongst the top performing solutions. On the private
dataset, using the same array of performance metrics as in the ISBI challenge,
the proposed approach shows high improvements in MS lesion segmentation
compared with other publicly available tools.Comment: This paper has been accepted for publication in NeuroImag
Unsupervised Domain Adaptation for Multispectral Pedestrian Detection
Multimodal information (e.g., visible and thermal) can generate robust
pedestrian detections to facilitate around-the-clock computer vision
applications, such as autonomous driving and video surveillance. However, it
still remains a crucial challenge to train a reliable detector working well in
different multispectral pedestrian datasets without manual annotations. In this
paper, we propose a novel unsupervised domain adaptation framework for
multispectral pedestrian detection, by iteratively generating pseudo
annotations and updating the parameters of our designed multispectral
pedestrian detector on target domain. Pseudo annotations are generated using
the detector trained on source domain, and then updated by fixing the
parameters of detector and minimizing the cross entropy loss without
back-propagation. Training labels are generated using the pseudo annotations by
considering the characteristics of similarity and complementarity between
well-aligned visible and infrared image pairs. The parameters of detector are
updated using the generated labels by minimizing our defined multi-detection
loss function with back-propagation. The optimal parameters of detector can be
obtained after iteratively updating the pseudo annotations and parameters.
Experimental results show that our proposed unsupervised multimodal domain
adaptation method achieves significantly higher detection performance than the
approach without domain adaptation, and is competitive with the supervised
multispectral pedestrian detectors
Dopamine, uncertainty and TD learning
Substantial evidence suggests that the phasic activities of dopaminergic neurons in the primate midbrain represent a temporal difference (TD) error in predictions of future reward, with increases above and decreases below baseline consequent on positive and negative prediction errors, respectively. However, dopamine cells have very low baseline activity, which implies that the representation of these two sorts of error is asymmetric. We explore the implications of this seemingly innocuous asymmetry for the interpretation of dopaminergic firing patterns in experiments with probabilistic rewards which bring about persistent prediction errors. In particular, we show that when averaging the non-stationary prediction errors across trials, a ramping in the activity of the dopamine neurons should be apparent, whose magnitude is dependent on the learning rate. This exact phenomenon was observed in a recent experiment, though being interpreted there in antipodal terms as a within-trial encoding of uncertainty
The Influence of Customer Satisfaction and Service Quality on Grand Royal Ceramic Purchase Decisions at Pt. Era Graha Perkasa Medan
Each company aims to achieve organizational goals by earning maximum sales. Sales are very important for a company because it can determine whether the company development is good or not. High customer satisfaction will increase purchase decisions, so is the quality of service. Starting from the brief description above, so the following problems are formulated by considering customer satisfaction and service quality for the purchase decisions of Grand Royal ceramics at PT Era Graha Perkasa Medan. The data was collected through interviews, questionnaires, and documentation studies. The data was then analyzed using multiple regression. The population of this study was all customers at PT Era Graha Perkasa Medan totaling 136 persons.The sample is determined by using Slovin formula which resulted in 101 persons. The research results indicate that customer satisfaction and service quality has an influence of purchase decisions because the Fcount value > Ftable (10.158> 3.09) with Sig. 0,000 <0,05. Partial is very significant for purchase decisions because customer satisfaction variable has a calculated value of 2.389 and ttable value of 1.984, then the value of tcount> ttable (2.389> 1.984) with a significant value of 0.019 <0.05 and ttable values of 1.984, then the value of tcount> ttable (2.885> 1.984) with a significant value of 0.005 <0.05, so the quality of service is the most dominant factor affecting the choice of satisfaction spending at PT Era Graha Perkasa Medan. This research concludes that simultaneous and partial customer satisfaction and service quality is very important to the purchase decision of Grand Royal ceramics at PT Era Graha Perkasa Medan. Keywords : Customer Satisfaction, Service Quality, Purchasing Decisions. DOI: 10.7176/RHSS/9-2-0
Optic radiation structure and anatomy in the normally developing brain determined using diffusion MRI and tractography
The optic radiation (OR) is a component of the visual system known to be myelin mature very early in life. Diffusion tensor imaging (DTI) and its unique ability to reconstruct the OR in vivo were used to study structural maturation through analysis of DTI metrics in a cohort of 90 children aged 5–18 years. As the OR is at risk of damage during epilepsy surgery, we measured its position relative to characteristic anatomical landmarks. Anatomical distances, DTI metrics and volume of the OR were investigated for age, gender and hemisphere effects. We observed changes in DTI metrics with age comparable to known trajectories in other white matter tracts. Left lateralization of DTI metrics was observed that showed a gender effect in lateralization. Sexual dimorphism of DTI metrics in the right hemisphere was also found. With respect to OR dimensions, volume was shown to be right lateralised and sexual dimorphism demonstrated for the extent of the left OR. The anatomical results presented for the OR have potentially important applications for neurosurgical planning
Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility
Experimental data have revealed that neuronal connection efficacy exhibits
two forms of short-term plasticity, namely, short-term depression (STD) and
short-term facilitation (STF). They have time constants residing between fast
neural signaling and rapid learning, and may serve as substrates for neural
systems manipulating temporal information on relevant time scales. The present
study investigates the impact of STD and STF on the dynamics of continuous
attractor neural networks (CANNs) and their potential roles in neural
information processing. We find that STD endows the network with slow-decaying
plateau behaviors-the network that is initially being stimulated to an active
state decays to a silent state very slowly on the time scale of STD rather than
on the time scale of neural signaling. This provides a mechanism for neural
systems to hold sensory memory easily and shut off persistent activities
gracefully. With STF, we find that the network can hold a memory trace of
external inputs in the facilitated neuronal interactions, which provides a way
to stabilize the network response to noisy inputs, leading to improved accuracy
in population decoding. Furthermore, we find that STD increases the mobility of
the network states. The increased mobility enhances the tracking performance of
the network in response to time-varying stimuli, leading to anticipative neural
responses. In general, we find that STD and STP tend to have opposite effects
on network dynamics and complementary computational advantages, suggesting that
the brain may employ a strategy of weighting them differentially depending on
the computational purpose.Comment: 40 pages, 17 figure
A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks
Understanding how the dynamics of a neural network is shaped by the network
structure, and consequently how the network structure facilitates the functions
implemented by the neural system, is at the core of using mathematical models
to elucidate brain functions. This study investigates the tracking dynamics of
continuous attractor neural networks (CANNs). Due to the translational
invariance of neuronal recurrent interactions, CANNs can hold a continuous
family of stationary states. They form a continuous manifold in which the
neural system is neutrally stable. We systematically explore how this property
facilitates the tracking performance of a CANN, which is believed to have clear
correspondence with brain functions. By using the wave functions of the quantum
harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is
decomposed into different motion modes, corresponding to distortions in the
amplitude, position, width or skewness of the network state. We then develop a
perturbative approach that utilizes the dominating movement of the network's
stationary states in the state space. This method allows us to approximate the
network dynamics up to an arbitrary accuracy depending on the order of
perturbation used. We quantify the distortions of a Gaussian bump during
tracking, and study their effects on the tracking performance. Results are
obtained on the maximum speed for a moving stimulus to be trackable and the
reaction time for the network to catch up with an abrupt change in the
stimulus.Comment: 43 pages, 10 figure
Scanner Invariant Multiple Sclerosis Lesion Segmentation from MRI
This paper presents a simple and effective generalization method for magnetic
resonance imaging (MRI) segmentation when data is collected from multiple MRI
scanning sites and as a consequence is affected by (site-)domain shifts. We
propose to integrate a traditional encoder-decoder network with a
regularization network. This added network includes an auxiliary loss term
which is responsible for the reduction of the domain shift problem and for the
resulting improved generalization. The proposed method was evaluated on
multiple sclerosis lesion segmentation from MRI data. We tested the proposed
model on an in-house clinical dataset including 117 patients from 56 different
scanning sites. In the experiments, our method showed better generalization
performance than other baseline networks
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