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Found 66 papers, 4 papers with code
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.
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.
Hand Biometrics in Digital Forensics
Biometric is a better solution to win over the problems encountered by digital forensics.
Finger Biometric Recognition With Feature Selection
The best identification accuracy of 98. 67%, and equal error rate (EER) of 4. 6% have been achieved using the subset of 25 features which are selected by the rank-based local FoBa algorithm.
Deep Neural Networks Fused with Textures for Image Classification
Fine-grained image classification (FGIC) is a challenging task in computer vision for due to small visual differences among inter-subcategories, but, large intra-class variations.
Fine-Grained Sports, Yoga, and Dance Postures Recognition: A Benchmark Analysis
The proposed SYD-Net has achieved state-of-the-art accuracy on Yoga-82 using five base CNNs.
Variational Augmentation for Enhancing Historical Document Image Binarization
Historical Document Image Binarization is a well-known segmentation problem in image processing.
Spoofing Detection on Hand Images Using Quality Assessment
A presentation attack detection approach is addressed by assessing the visual quality of genuine and fake hand images.
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
Word Segmentation from Unconstrained Handwritten Bangla Document Images using Distance Transform
This paper addresses the automatic segmentation of text words directly from unconstrained Bangla handwritten document images.
Optical Character Recognition
Optical Character Recognition (OCR)
+1
A New Approach for Texture based Script Identification At Block Level using Quad Tree Decomposition
A considerable amount of success has been achieved in developing monolingual OCR systems for Indic scripts.
Handwritten Script Identification from Text Lines
In a multilingual country like India where 12 different official scripts are in use, automatic identification of handwritten script facilitates many important applications such as automatic transcription of multilingual documents, searching for documents on the web/digital archives containing a particular script and for the selection of script specific Optical Character Recognition (OCR) system in a multilingual environment.
Optical Character Recognition
Optical Character Recognition (OCR)
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 Hybrid Swarm and Gravitation based feature selection algorithm for Handwritten Indic Script Classification problem
In any multi-script environment, handwritten script classification is of paramount importance before the document images are fed to their respective Optical Character Recognition (OCR) engines.
Computational modeling of Human-nCoV protein-protein interaction network
With this hypothesis, the present work focuses on developing a computational model for nCoV-Human protein interaction network, using the experimentally validated SARS-CoV-Human protein interactions.
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.
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.
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.
Recognition of Offline Handwritten Devanagari Numerals using Regional Weighted Run Length Features
Recognition of handwritten Roman characters and numerals has been extensively studied in the last few decades and its accuracy reached to a satisfactory state.
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.
A Harmony Search Based Wrapper Feature Selection Method for Holistic Bangla word Recognition
In this paper, we introduced a Harmony Search (HS) algorithm based feature selection method for feature dimensionality reduction in handwritten Bangla word recognition problem.
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.
A two-pass fuzzy-geno approach to pattern classification
In this approach, an unknown pattern is classified by refining possible classification decisions obtained through coarse classification of the same.
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.
Robust 3D face recognition in presence of pose and partial occlusions or missing parts
Features are extracted from the reconstructed non-occluded face images in the form of face normals.
A Gabor block based Kernel Discriminative Common Vector (KDCV) approach using cosine kernels for Human Face Recognition
Secondly, the nonlinear discriminating features are analyzed and extracted from the selected low-energized blocks by the generalized Kernel Discriminative Common Vector (KDCV) method.
Face Recognition using Hough Peaks extracted from the significant blocks of the Gradient Image
Finally the best fitted Hough peaks are extracted from the Hough transformed significant blocks for efficient face recognition.
High Performance Human Face Recognition using Gabor based Pseudo Hidden Markov Model
Unlike the traditional zigzag scanning method for feature extraction a continuous scanning method from top-left corner to right then top-down and right to left and so on until right-bottom of the image i. e. a spiral scanning technique has been proposed for better feature selection.
A Face Recognition approach based on entropy estimate of the nonlinear DCT features in the Logarithm Domain together with Kernel Entropy Component Analysis
This paper exploits the feature extraction capabilities of the discrete cosine transform (DCT) together with an illumination normalization approach in the logarithm domain that increase its robustness to variations in facial geometry and illumination.
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition
Firstly, the new morphological features i. e., features based on number of runs of pixels in four directions (N, NE, E, NW) are extracted, together with the GLCM based statistical features and LDP features that are less sensitive to the noise and non-monotonic illumination changes, are extracted from the significant blocks of the gradient image.
Human Face Recognition using Gabor based Kernel Entropy Component Analysis
In this paper, we present a novel Gabor wavelet based Kernel Entropy Component Analysis (KECA) method by integrating the Gabor wavelet transformation (GWT) of facial images with the KECA method for enhanced face recognition performance.
Automatic White Blood Cell Measuring Aid for Medical Diagnosis
The objective of this work is to automate the blood related pathological investigation process.
Medical Aid for Automatic Detection of Malaria
The analysis and counting of blood cells in a microscope image can provide useful information concerning to the health of a person.
Detection of pose orientation across single and multiple axes in case of 3D face images
In this paper, we propose a new approach that takes as input a 3D face image across X, Y and Z axes as well as both Y and X axes and gives output as its pose i. e. it tells whether the face is oriented with respect the X, Y or Z axes or is it oriented across multiple axes with angles of rotation up to 42 degree.
A novel approach for nose tip detection using smoothing by weighted median filtering applied to 3D face images in variant poses
In this present smoothing technique we have built the neighborhood surrounding a particular point in 3D face and replaced that with the weighted value of the surrounding points in 3D face image.
A novel approach to nose-tip and eye corners detection using H-K Curvature Analysis in case of 3D images
In this paper we present a novel method that combines a HK curvature-based approach for three-dimensional (3D) face detection in different poses (X-axis, Y-axis and Z-axis).
A method for nose-tip based 3D face registration using maximum intensity algorithm
In this paper we present a novel technique of registering 3D images across pose.
A Novel Approach in detecting pose orientation of a 3D face required for face
All the experiments have been performed on the FRAV3D Database.
Automated Thermal Face recognition based on Minutiae Extraction
Image processing methods are used to pre-process the captured thermogram, from which different physiological features based on blood perfusion data are extracted.
Minutiae Based Thermal Human Face Recognition using Label Connected Component Algorithm
Secondly face features are extracted from the croped region, which will be taken as the input of the back propagation feed forward neural network in the third step and classification and recognition is carried out.
Minutiae Based Thermal Face Recognition using Blood Perfusion Data
A distribution of blood vessels are unique for each person and as a set of extracted minutiae points from a blood perfusion data of a human face should be unique for that face.
A Comparative Study of Human thermal face recognition based on Haar wavelet transform (HWT) and Local Binary Pattern (LBP)
In the first approach, the training images and the test images are processed with Haar wavelet transform and the LL band and the average of LH/HL/HH bands sub-images are created for each face image.
Thermal Human face recognition based on Haar wavelet transform and series matching technique
Therefore this paper describes an efficient approach of human face recognition based on wavelet transform from thermal IR images.
High Performance Human Face Recognition using Independent High Intensity Gabor Wavelet Responses: A Statistical Approach
In this paper, we present a technique by which high-intensity feature vectors extracted from the Gabor wavelet transformation of frontal face images, is combined together with Independent Component Analysis (ICA) for enhanced face recognition.
Next Level of Data Fusion for Human Face Recognition
This paper demonstrates two different fusion techniques at two different levels of a human face recognition process.
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition
This paper investigates the multiresolution level-1 and level-2 Quotient based Fusion of thermal and visual images.
Face Synthesis (FASY) System for Determining the Characteristics of a Face Image
This paper aims at determining the characteristics of a face image by extracting its components.
Image Pixel Fusion for Human Face Recognition
In this paper we present a technique for fusion of optical and thermal face images based on image pixel fusion approach.
Human Face Recognition using Line Features
Here, we have used thermal face images as those are capable to minimize the affect of illumination changes and occlusion due to moustache, beards, adornments etc.
Fusion of Daubechies Wavelet Coefficients for Human Face Recognition
The main advantage of using wavelet transform is that it is well-suited to manage different image resolution and allows the image decomposition in different kinds of coefficients, while preserving the image information.
Reduction of Feature Vectors Using Rough Set Theory for Human Face Recognition
To reduce further we have applied feature selection method to select indispensable features, which will remain in the final feature vectors.
Classification of Polar-Thermal Eigenfaces using Multilayer Perceptron for Human Face Recognition
This paper presents a novel approach to handle the challenges of face recognition.
Face Synthesis (FASY) System for Generation of a Face Image from Human Description
This paper aims at generating a new face based on the human like description using a new concept.
A novel scheme for binarization of vehicle images using hierarchical histogram equalization technique
Automatic License Plate Recognition system is a challenging area of research now-a-days and binarization is an integral and most important part of it.
Development of an automated Red Light Violation Detection System (RLVDS) for Indian vehicles
Integrated Traffic Management Systems (ITMS) are now implemented in different cities in India to primarily address the concerns of road-safety and security.
An Offline Technique for Localization of License Plates for Indian Commercial Vehicles
The technique is based on color based segmentation of vehicle images and identification of potential license plate regions.
