Search Results for author:
Found 34 papers, 5 papers with code
COVID-CT-H-UNet: a novel COVID-19 CT segmentation network based on attention mechanism and Bi-category Hybrid loss
The typical Binary cross entropy(BCE) based U-shaped network only concentrates on the entire CT images without emphasizing on the affected regions, which results in hazy borders and low contrast in the projected output.
Regularizing CNNs using Confusion Penalty Based Label Smoothing for Histopathology Images
Researchers propose regularizing techniques, such as Label Smoothing (LS), which introduces soft labels for training data, making the classifier more regularized.
Fuzzy Rank-based Late Fusion Technique for Cytology image Segmentation
In this paper, we have explored a fuzzy-based late fusion techniques for cytology image segmentation.
Could We Generate Cytology Images from Histopathology Images? An Empirical Study
Automation in medical imaging is quite challenging due to the unavailability of annotated datasets and the scarcity of domain experts.
Variational Augmentation for Enhancing Historical Document Image Binarization
Historical Document Image Binarization is a well-known segmentation problem in image processing.
Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for Cervical Cytology Image Classification
Cervical cancer is the fourth most common category of cancer, affecting more than 500, 000 women annually, owing to the slow detection procedure.
A distillation based approach for the diagnosis of diseases
Presently, Covid-19 is a serious threat to the world at large.
Cervical Cytology Classification Using PCA & GWO Enhanced Deep Features Selection
A non-redundant, optimal feature subset is selected from this feature space using an evolutionary optimization algorithm, the Grey Wolf Optimizer, thus improving the classification performance.
Ranked #1 on
Image Classification
on SIPaKMeD
GuideBP: Guiding Backpropagation Through Weaker Pathways of Parallel Logits
Convolutional neural networks often generate multiple logits and use simple techniques like addition or averaging for loss computation.
Exploring Knowledge Distillation of a Deep Neural Network for Multi-Script identification
In this paper, we explore dark knowledge transfer approach using long short-term memory(LSTM) and CNN based assistant model and various deep neural networks as the teacher model, with a simple CNN based student network, in this domain of multi-script identification from natural scene text images.
Multispectral Object Detection with Deep Learning
Moving outside the visible spectrum range, such as the thermal spectrum or the near-infrared (NIR) images, is much more beneficial in low visibility conditions, NIR images are very helpful for understanding the object's material quality.
RectiNet-v2: A stacked network architecture for document image dewarping
With the advent of mobile and hand-held cameras, document images have found their way into almost every domain.
Ranked #2 on
SSIM
on DocUNet
Two-phase Dynamic Routing for Micro and Macro-level Equivariance in Multi-Column Capsule Networks
To address this, we propose a novel two-phase dynamic routing protocol that computes agreements between neurons at various layers for micro and macro-level features, following a hierarchical learning paradigm.
A Gated and Bifurcated Stacked U-Net Module for Document Image Dewarping
Capturing images of documents is one of the easiest and most used methods of recording them.
Ranked #1 on
SSIM
on DocUNet
A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition
Finding local invariant patterns in handwrit-ten characters and/or digits for optical character recognition is a difficult task.
Optical Character Recognition
Optical Character Recognition (OCR)
Skin Diseases Detection using LBP and WLD- An Ensembling Approach
The ensemble approach clearly outperform all of the used deep learning networks.
Cytology Image Analysis Techniques Towards Automation: Systematically Revisited
Cytology is the branch of pathology which deals with the microscopic examination of cells for diagnosis of carcinoma or inflammatory conditions.
Multistage Curvilinear Coordinate Transform Based Document Image Dewarping using a Novel Quality Estimator
The present work demonstrates a fast and improved technique for dewarping nonlinearly warped document images.
SynCGAN: Using learnable class specific priors to generate synthetic data for improving classifier performance on cytological images
One of the most challenging aspects of medical image analysis is the lack of a high quantity of annotated data.
EDC3: Ensemble of Deep-Classifiers using Class-specific Copula functions to Improve Semantic Image Segmentation
In the literature, many fusion techniques are registered for the segmentation of images, but they primarily focus on observed output or belief score or probability score of the output classes.
A Genetic Algorithm based Kernel-size Selection Approach for a Multi-column Convolutional Neural Network
Deep neural network-based architectures give promising results in various domains including pattern recognition.
Understanding Deep Learning Techniques for Image Segmentation
This paper approaches these various deep learning techniques of image segmentation from an analytical perspective.
Using dynamic routing to extract intermediate features for developing scalable capsule networks
Capsule networks have gained a lot of popularity in short time due to its unique approach to model equivariant class specific properties as capsules from images.
Handwritten Indic Character Recognition using Capsule Networks
Convolutional neural networks(CNNs) has become one of the primary algorithms for various computer vision tasks.
Combining Multi-level Contexts of Superpixel using Convolutional Neural Networks to perform Natural Scene Labeling
Modern deep learning algorithms have triggered various image segmentation approaches.
Handwritten Isolated Bangla Compound Character Recognition: a new benchmark using a novel deep learning approach
In this work, a novel deep learning technique for the recognition of handwritten Bangla isolated compound character is presented and a new benchmark of recognition accuracy on the CMATERdb 3. 1. 3. 3 dataset is reported.
Deep learning for word-level handwritten Indic script identification
These are then used to train different CNNs to select features.
An Enhanced Harmony Search Method for Bangla Handwritten Character Recognition Using Region Sampling
A new region selection technique based on the idea of an enhanced Harmony Search methodology has been proposed here.
A GA Based approach for selection of local features for recognition of handwritten Bangla numerals
In the current work we have developed a two-pass approach where the first pass classifier performs a coarse classification, based on some global features of the input pattern by restricting the possibility of classification decisions within a group of classes, smaller than the number of classes considered initially.
An Improved Feature Descriptor for Recognition of Handwritten Bangla Alphabet
Appropriate feature set for representation of pattern classes is one of the most important aspects of handwritten character recognition.
Handwritten Devanagari Script Segmentation: A non-linear Fuzzy Approach
The segmentation accuracy achieved by the current technique is 94. 8%.
Design of a novel convex hull based feature set for recognition of isolated handwritten Roman numerals
In this work, 25 features are extracted based on different bays attributes of the convex hull of the digit patterns.
Recognition of Handwritten Bangla Basic Characters and Digits using Convex Hull based Feature Set
The current research aims to evaluate the performance of the convex hull based feature set, i. e. 125 features in all computed over different bays attributes of the convex hull of a pattern, for effective recognition of isolated handwritten Bangla basic characters and digits.
