86 research outputs found
Self Control of Chaotic Dynamics using LTI Filters
In this brief, an algorithm for controlling chaotic systems using small,
continuous time perturbations is presented. Stabilisation is achieved by self
controlling feedback using low order LTI filters. The algorithm alleviates the
need of complex calculati ons or costly delay elements, and can be implemented
in a wide variety of systems using simple circuit elments only.Comment: 11 pages LaTex, composed with IEEE style file posted herewith.
Detailed informationls available with the author @ [email protected]
Visual saliency detection: a Kalman filter based approach
In this paper we propose a Kalman filter aided saliency detection model which
is based on the conjecture that salient regions are considerably different from
our "visual expectation" or they are "visually surprising" in nature. In this
work, we have structured our model with an immediate objective to predict
saliency in static images. However, the proposed model can be easily extended
for space-time saliency prediction. Our approach was evaluated using two
publicly available benchmark data sets and results have been compared with
other existing saliency models. The results clearly illustrate the superior
performance of the proposed model over other approaches
A dense subgraph based algorithm for compact salient image region detection
We present an algorithm for graph based saliency computation that utilizes
the underlying dense subgraphs in finding visually salient regions in an image.
To compute the salient regions, the model first obtains a saliency map using
random walks on a Markov chain. Next, k-dense subgraphs are detected to further
enhance the salient regions in the image. Dense subgraphs convey more
information about local graph structure than simple centrality measures. To
generate the Markov chain, intensity and color features of an image in addition
to region compactness is used. For evaluating the proposed model, we do
extensive experiments on benchmark image data sets. The proposed method
performs comparable to well-known algorithms in salient region detection.Comment: 33 pages, 18 figures, Single column manuscript pre-print, Accepted at
Computer Vision and Image Understanding, Elsevie
Visual Attention for Behavioral Cloning in Autonomous Driving
The goal of our work is to use visual attention to enhance autonomous driving
performance. We present two methods of predicting visual attention maps. The
first method is a supervised learning approach in which we collect eye-gaze
data for the task of driving and use this to train a model for predicting the
attention map. The second method is a novel unsupervised approach where we
train a model to learn to predict attention as it learns to drive a car.
Finally, we present a comparative study of our results and show that the
supervised approach for predicting attention when incorporated performs better
than other approaches.Comment: Paper Accepted at ICMV (2018
Trapezoidal Fuzzy Numbers for the Transportation Problem
Transportation Problem is an important problem which has been widely studied
in Operations Research domain. It has been often used to simulate different
real life problems. In particular, application of this Problem in NP Hard
Problems has a remarkable significance. In this Paper, we present the closed,
bounded and non empty feasible region of the transportation problem using fuzzy
trapezoidal numbers which ensures the existence of an optimal solution to the
balanced transportation problem. The multivalued nature of Fuzzy Sets allows
handling of uncertainty and vagueness involved in the cost values of each cells
in the transportation table. For finding the initial solution of the
transportation problem we use the Fuzzy Vogel Approximation Method and for
determining the optimality of the obtained solution Fuzzy Modified Distribution
Method is used. The fuzzification of the cost of the transportation problem is
discussed with the help of a numerical example. Finally, we discuss the
computational complexity involved in the problem. To the best of our knowledge,
this is the first work on obtaining the solution of the transportation problem
using fuzzy trapezoidal numbers.Comment: International Journal of Intelligent Computing and Applications,
Volume 1, Number 2, 200
Visualization Regularizers for Neural Network based Image Recognition
The success of deep neural networks is mostly due their ability to learn
meaningful features from the data. Features learned in the hidden layers of
deep neural networks trained in computer vision tasks have been shown to be
similar to mid-level vision features. We leverage this fact in this work and
propose the visualization regularizer for image tasks. The proposed
regularization technique enforces smoothness of the features learned by hidden
nodes and turns out to be a special case of Tikhonov regularization. We achieve
higher classification accuracy as compared to existing regularizers such as the
L2 norm regularizer and dropout, on benchmark datasets without changing the
training computational complexity
Ensemble of Deep Convolutional Neural Networks for Learning to Detect Retinal Vessels in Fundus Images
Vision impairment due to pathological damage of the retina can largely be
prevented through periodic screening using fundus color imaging. However the
challenge with large scale screening is the inability to exhaustively detect
fine blood vessels crucial to disease diagnosis. In this work we present a
computational imaging framework using deep and ensemble learning for reliable
detection of blood vessels in fundus color images. An ensemble of deep
convolutional neural networks is trained to segment vessel and non-vessel areas
of a color fundus image. During inference, the responses of the individual
ConvNets of the ensemble are averaged to form the final segmentation. In
experimental evaluation with the DRIVE database, we achieve the objective of
vessel detection with maximum average accuracy of 94.7\% and area under ROC
curve of 0.9283
Improving Consistency and Correctness of Sequence Inpainting using Semantically Guided Generative Adversarial Network
Contemporary benchmark methods for image inpainting are based on deep
generative models and specifically leverage adversarial loss for yielding
realistic reconstructions. However, these models cannot be directly applied on
image/video sequences because of an intrinsic drawback- the reconstructions
might be independently realistic, but, when visualized as a sequence, often
lacks fidelity to the original uncorrupted sequence. The fundamental reason is
that these methods try to find the best matching latent space representation
near to natural image manifold without any explicit distance based loss. In
this paper, we present a semantically conditioned Generative Adversarial
Network (GAN) for sequence inpainting. The conditional information constrains
the GAN to map a latent representation to a point in image manifold respecting
the underlying pose and semantics of the scene. To the best of our knowledge,
this is the first work which simultaneously addresses consistency and
correctness of generative model based inpainting. We show that our generative
model learns to disentangle pose and appearance information; this independence
is exploited by our model to generate highly consistent reconstructions. The
conditional information also aids the generator network in GAN to produce
sharper images compared to the original GAN formulation. This helps in
achieving more appealing inpainting performance. Though generic, our algorithm
was targeted for inpainting on faces. When applied on CelebA and Youtube Faces
datasets, the proposed method results in a significant improvement over the
current benchmark, both in terms of quantitative evaluation (Peak Signal to
Noise Ratio) and human visual scoring over diversified combinations of
resolutions and deformations
Faster learning of deep stacked autoencoders on multi-core systems using synchronized layer-wise pre-training
Deep neural networks are capable of modelling highly non-linear functions by
capturing different levels of abstraction of data hierarchically. While
training deep networks, first the system is initialized near a good optimum by
greedy layer-wise unsupervised pre-training. However, with burgeoning data and
increasing dimensions of the architecture, the time complexity of this approach
becomes enormous. Also, greedy pre-training of the layers often turns
detrimental by over-training a layer causing it to lose harmony with the rest
of the network. In this paper a synchronized parallel algorithm for
pre-training deep networks on multi-core machines has been proposed. Different
layers are trained by parallel threads running on different cores with regular
synchronization. Thus the pre-training process becomes faster and chances of
over-training are reduced. This is experimentally validated using a stacked
autoencoder for dimensionality reduction of MNIST handwritten digit database.
The proposed algorithm achieved 26\% speed-up compared to greedy layer-wise
pre-training for achieving the same reconstruction accuracy substantiating its
potential as an alternative
WEPSAM: Weakly Pre-Learnt Saliency Model
Visual saliency detection tries to mimic human vision psychology which
concentrates on sparse, important areas in natural image. Saliency prediction
research has been traditionally based on low level features such as contrast,
edge, etc. Recent thrust in saliency prediction research is to learn high level
semantics using ground truth eye fixation datasets. In this paper we present,
WEPSAM : Weakly Pre-Learnt Saliency Model as a pioneering effort of using
domain specific pre-learing on ImageNet for saliency prediction using a light
weight CNN architecture. The paper proposes a two step hierarchical learning,
in which the first step is to develop a framework for weakly pre-training on a
large scale dataset such as ImageNet which is void of human eye fixation maps.
The second step refines the pre-trained model on a limited set of ground truth
fixations. Analysis of loss on iSUN and SALICON datasets reveal that
pre-trained network converges much faster compared to randomly initialized
network. WEPSAM also outperforms some recent state-of-the-art saliency
prediction models on the challenging MIT300 dataset
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