33 research outputs found
Morphological Network: How Far Can We Go with Morphological Neurons?
In recent years, the idea of using morphological operations as networks has
received much attention. Mathematical morphology provides very efficient and
useful image processing and image analysis tools based on basic operators like
dilation and erosion, defined in terms of kernels. Many other morphological
operations are built up using the dilation and erosion operations. Although the
learning of structuring elements such as dilation or erosion using the
backpropagation algorithm is not new, the order and the way these morphological
operations are used is not standard. In this paper, we have theoretically
analyzed the use of morphological operations for processing 1D feature vectors
and shown that this gets extended to the 2D case in a simple manner. Our
theoretical results show that a morphological block represents a sum of hinge
functions. Hinge functions are used in many places for classification and
regression tasks (Breiman (1993)). We have also proved a universal
approximation theorem -- a stack of two morphological blocks can approximate
any continuous function over arbitrary compact sets. To experimentally validate
the efficacy of this network in real-life applications, we have evaluated its
performance on satellite image classification datasets since morphological
operations are very sensitive to geometrical shapes and structures. We have
also shown results on a few tasks like segmentation of blood vessels from
fundus images, segmentation of lungs from chest x-ray and image dehazing. The
results are encouraging and further establishes the potential of morphological
networks.Comment: 35 pages, 19 figures, 7 table
Significance of Skeleton-based Features in Virtual Try-On
The idea of \textit{Virtual Try-ON} (VTON) benefits e-retailing by giving an
user the convenience of trying a clothing at the comfort of their home. In
general, most of the existing VTON methods produce inconsistent results when a
person posing with his arms folded i.e., bent or crossed, wants to try an
outfit. The problem becomes severe in the case of long-sleeved outfits. As
then, for crossed arm postures, overlap among different clothing parts might
happen. The existing approaches, especially the warping-based methods employing
\textit{Thin Plate Spline (TPS)} transform can not tackle such cases. To this
end, we attempt a solution approach where the clothing from the source person
is segmented into semantically meaningful parts and each part is warped
independently to the shape of the person. To address the bending issue, we
employ hand-crafted geometric features consistent with human body geometry for
warping the source outfit. In addition, we propose two learning-based modules:
a synthesizer network and a mask prediction network. All these together attempt
to produce a photo-realistic, pose-robust VTON solution without requiring any
paired training data. Comparison with some of the benchmark methods clearly
establishes the effectiveness of the approach
Robust extraction of text from camera images using colour and spatial information simultaneously
The importance and use of text extraction from camera based coloured scene images is rapidly increasing with time. Text within a camera grabbed image can contain a huge amount of meta data about that scene. Such meta data can be useful for identification, indexing and retrieval purposes. While the segmentation and recognition of text from document images is quite successful, detection of coloured scene text is a new challenge for all camera based images. Common problems for text extraction from camera based images are the lack of prior knowledge of any kind of text features such as colour, font, size and orientation as well as the location of the probable text regions. In this paper, we document the development of a fully automatic and extremely robust text segmentation technique that can be used for any type of camera grabbed frame be it single image or video. A new algorithm is proposed which can overcome the current problems of text segmentation. The algorithm exploits text appearance in terms of colour and spatial distribution. When the new text extraction technique was tested on a variety of camera based images it was found to out perform existing techniques (or something similar). The proposed technique also overcomes any problems that can arise due to an unconstraint complex background. The novelty in the works arises from the fact that this is the first time that colour and spatial information are used simultaneously for the purpose of text extraction