Sorry, you need to enable JavaScript to visit this website.

Segmenting a document image into text-lines and words finds applications in many research areas of DIA(Document Image Analysis) such as OCR, Word Spotting, and document retrieval. However, carrying out segmentation operation directly in the compressed document images is still an unexplored and challenging research area. Since JPEG is most widely accepted compression algorithm, this research paper attempts to segment a JPEG compressed printed text document image into text-lines and words, without fully decompressing the image.


Subspace clustering algorithms provide the capability
to project a dataset onto bases that facilitate clustering.
Proposed in 2017, the subspace k-means algorithm simultaneously
performs clustering and dimensionality reduction with the goal
of finding the optimal subspace for the cluster structure; this
is accomplished by incorporating a trade-off between cluster
and noise subspaces in the objective function. In this study,
we improve subspace k-means by estimating a critical transformation


Crop type identification refers to distinguishing certain crop from other landcovers, which is an essential and crucial task in agricultural monitoring. Satellite images are good data input for identifying different crops since satellites capture relatively wider area and more spectral information. Based on prior knowledge of crop phenology, multi-temporal images are stacked to extract the growth pattern of varied crops.


This paper examines a deep feedforward network for beamforming with the single--snapshot Sample Covariance Matrix (SCM). The Conventional beamforming formulation, typically quadratic in the complex weight space, is reformulated as real and linear in the weight covariance and SCM. The reformulated SCMs are used as input to a deep feed--forward neural network (FNN) for two source localization. Simulations demonstrate the effect of source incoherence and performance in a noisy tracking example.


We focus on a wireless sensor network powered with an energy beacon, where sensors send their measurements to the sink using the harvested energy. The aim of the system is to estimate an unknown signal over the area of interest as accurately as possible. We investigate optimal energy beamforming at the energy beacon and optimal transmit power allocation at the sensors under non-linear energy harvesting models. We use a deep reinforcement learning (RL) based approach where multi-layer neural networks are utilized.


Semi-continuous data have a point mass at zero and are continuous with positive support. Such data arise naturally in several real-life situations like signals in a blind source separation problem, daily rainfall at a location, sales of durable goods among many others. Therefore, efficient estimation of the underlying probability density function is of significant interest.


In this paper we propose a Deep Autoencoder Mixture Clustering(DAMIC) algorithm based on a mixture of deep autoencoders whereeach cluster is represented by an autoencoder. A clustering networktransforms the data into another space and then selects one of theclusters. Next, the autoencoder associated with this cluster is usedto reconstruct the data-point. The clustering algorithm jointly learnsthe nonlinear data representation and the set of autoencoders. Theoptimal clustering is found by minimizing the reconstruction loss ofthe mixture of autoencoder network.


We present a novel variational generative adversarial network (VGAN) based on Wasserstein loss to learn a latent representation
from a face image that is invariant to identity but preserves head-pose information. This facilitates synthesis of a realistic face
image with the same head pose as a given input image, but with a different identity. One application of this network is in
privacy-sensitive scenarios; after identity replacement in an image, utility, such as head pose, can still