Titles with Abstracts_2023-2024_Digital Image processing.pdf

ELYSIUMPRO TITLES WITH ABSTRACTS 2023-2024

EPRO_DSP_001 Gender-Specific Characteristics for Hand-Vein Biometric Recognition: Analysis and
Exploitation
In recent years, vein-based biometric recognition has received ever-increasing attention from both academia
and industry, due to the advantages it offers over traditional biometric traits such as fingerprint, iris, and face.
Nonetheless, some issues related to the use of vein biometrics still need to be investigated and understood.
Specifically, in this study, we speculate about the gender-related variations in vein patterns, and their effects on
biometric verification performance. An analysis on the feasibility of recognizing male and female subjects
depending on their hand-vein patterns, and on the level of similarity characterizing the biometric templates
extracted from male and female populations, are here carried out considering three different databases.
Specifically, the public VERA dataset, containing samples of palm-vein patterns, and two datasets containing
images of finger-vein patterns, i.e., the UTFVP public database, and an in-house dataset collected with an on-
the-move contactless modality, are here considered.
EPRO_DSP_002 Automated Segmentation of Brain Tumor MRI Images Using DeepLearning
Segmenting brain tumors automatically using MR data is crucial for disease investigation and monitoring. Due
to the aggressive nature and diversity of gliomas, well-organized and exact segmentation methods are used to
classify tumors intra-tumorally. The proposed technique uses a Gray Level Co-occurrence matrix extraction of
features approach to strip out unwanted details from the images. In comparison with the current state of the art,
the accuracy of brain tumor segmentation was significantly improved using Convolutional Neural Networks,
which are frequently used in the field of biomedical image segmentation. By merging the results of two separate
segmentation networks, the proposed method demonstrates a major but simple combinatorial strategy that, as a
direct consequence, yields much more precise and complete estimates. A U-Net and a Three-Dimensional
Convolutional Neural Network. These networks are used to break up images into their component parts.
Following that, the prediction was constructed using two distinct models that were combined in a number of
ways. In comparison to existing state-of-the-art designs, the proposed method achieves the mean accuracy (%)
of 99.40, 98.46, 98.29, precision (%) of 99.41, 98.51, 98.35, F-Score (%) of 99.4, 98.29, 98.46 and sensitivity
(%) of 99.39, 98.41, 98.25 for the whole tumor, enhanced tumor, tumor core on the validation set, respectively.

EPRO_DSP_003 Glioma Grade Classification Using CNNs and Segmentation With an Adaptive
Approach Using Histogram Features in Brain MRIs
Artificial intelligence (AI) applications have become popular due to their advantages in solving health problems
with high accuracy and confidence. One such application is the diagnosis of brain tumors or anomalies. This
paper presents two new approaches for brain tumor grade classification and segmentation. Convolutional neural
network (CNN) models were used as the first approach to classify High-Grade Glioma (HGG) and Low-Grade
Glioma (LGG) tumors and achieved with 99.85% accuracy, 99.85% F1 and 99.92% AUC scores. A new
pipeline consisting of normalization, modality fusion and CNN model for HGG-LGG classification tasks was
also proposed and developed. A novel algorithm based on histograms, thresholding and morphological filtering
with feature fusion was also proposed and developed for the segmentation task. 70.58% Dice Similarity (DS)
on average was achieved with the complete tumor segmentation
EPRO_DSP_004 Automatic Segmentation of Kidney Volume Using Multi-Module Hybrid BasedU-
Shape in Polycystic Kidney Disease
Polycystic kidney disease (Autosomal Dominant Polycystic Kidney Disease, ADPKD) is the most common
genetic disease of the kidney, and the measurement of Total Kidney Volume (TKV) in clinical research of this
disease is essential to study the progression of ADPKD. At present, the volume segmentation of polycystic
kidneys mainly relies on doctors to manually outline the kidney boundary on the radiological image. This
process is time-consuming, labor-intensive, inefficient, subjective, and difficult to guarantee consistency. In the
research of this paper, A multi-module hybrid U-shape segmentation method is proposed (HUNet), which
introduces wavelet pooling, cascade residual, and efficient multi-head self-attention into the U-shape structure.
We use wavelet pooling instead of traditional down-sampling to reduce the loss of detailed features, the use of
cascaded residual modules can improve the ability of model feature reuse, and the use of efficient multi-head
self-attention modules can effectively capture global multi-scale information.

EPRO_DSP_005 Urinary Stones Segmentation in Abdominal X-Ray Images Using Cascaded U-Net
Pipeline With Stone-Embedding Augmentation and Lesion-Size Reweighting
Approach
In this research, we proposed a two-stage pipeline for segmenting urinary stones. The first stage U-Net
generated the map localizing the urinary organs in full abdominal x-ray images. Then, this map was used for
creating partitioned images input to the second stage U-Net to reduce class imbalance and was also used in
stone-embedding augmentation to increase a number of training data. The U-Net model was trained with the
combination of real stone-contained images and synthesized stone-embedded images to segment urinary stones
on the partitioned input images. In addition, we proposed to use an inverse weighting method in the focal
Tversky loss function in order to rebalance lesion size. The U-Net model using our proposed pipeline produced
a 71.28% pixel-wise F_{2} score and a 69.82% region-wise F_{2} score, which were 2.88% and 7.63%,
respectively, higher than those of a baseline method.
EPRO_DSP_006 Multi-Class Classification of Plant Leaf Diseases Using Feature Fusion of Deep
Convolutional Neural Network and Local Binary Pattern
Plant diseases are one of the primary causes of decreased agricultural production quality and quantity. With
ongoing changes in plant structure and cultivation techniques, new diseases are constantly arising on plant
leaves. Thus, accurate classification and detection of plant leaf diseases in their early stages will limit the spread
of the infection and support the healthy development of plant production. This work proposes a novel
lightweight deep convolutional neural network (CNN) model for obtaining high-level hidden feature
representations. The deep features are then fused with traditional handcrafted local binary pattern (LBP) features
to capture local texture information in plant leaf images. The proposed model is trained and tested on three
publicly available datasets (Apple Leaf, Tomato Leaf, and Grape Leaf). On the three datasets, the proposed
approach achieves 99%, 96.6%, and 98.5% validation accuracies, respectively, and 98.8%, 96.5%, and 98.3%
test accuracies, respectively. The results of the experiments show that the proposed approach can provide a
better control solution for plant diseases.

EPRO_DSP_007 Compressive Wavelet Domain DeepCNN for Image Classification Using Genetic
Algorithm BasedSensing Mask Learning
Using a novel Genetic Algorithm-based Compressive Learning (GACL), a compressed domain-learning
framework is proposed that is implemented on the Haar wavelet approximation coefficient images of the
standard kaggle RGB cat dog dataset with every images resized to 256×256×3 . The compressive sensing (CS)
measurements on the selected dataset is achieved by using a simple reduced pixel scheme by retaining
only P% of the pixels of the approximation coefficient images and forcing the remaining pixels to 0 using the
Primitive Walsh Hadamard (PWH) binary mask and the masked images are used for further learning. A
numerical experiment is conducted to analyze the image classification performance of deep convolution neural
network (DCNN) learning on compressive sensing (CS) measurements of wavelet approximation coefficient
image of the selected dataset. The unmasked wavelet approximation coefficients images are of size only one
fourth of the original image, but they visually resembles the original image.
EPRO_DSP_008 A Comprehensive Joint Learning System to Detect Skin Cancer
Skin, the body’s biggest organ and a barrier against heat, light, damage, and infection can be affected by many
diseases. However, a correct diagnosis can lead to proper treatment. Skin diseases must be identified early to
reduce skin lesion growth and spread. The medical field has a significant dependency on Information
Technology and in this era, there is a need for a mechanism that can detect skin diseases at an early stage with
higher accuracy capable of working with rapidly growing data. This research offers a joint learning system
using Convolutional Neural Networks (CNN) and Local Binary Pattern (LBP) followed by its concatenation of
all the extracted features through CNN and LBP architecture. The proposed system is trained and tested using
the widely used publicly accessible dataset for skin cancer detection to solve multiclass skin disease issues.

EPRO_DSP_009 A Variational Retinex Model With Structure-Awareness Regularization for Single-
Image Low-Light Enhancement
Low-light image enhancement (LLIE) is a method of improving the visual quality of images captured in weak
illumination conditions. In such conditions, the images tend to be noisy, hazy, and have low contrast, making
them difficult to distinguish details. LLIE techniques have many practical applications in various fields,
including surveillance, astronomy, medical imaging, and consumer photography. The total variational method
is a sound solution in this field. However, requirement of an overall spatial smoothness of the illumination map
leads to the failure of recovering intricate details. This paper proposes that the interaction between the global
spatial smoothness and the detail recovery in the total variational Retinex model can be optimized by adopting
a structure-awareness regularization term. The resultant non-linear model is more effective than the original one
for LLIE. As a model-based method, its performance does not rely on architecture engineering, super-parameter
tuning, or specific training dataset
EPRO_DSP_010 Performance Enhancement of Skin Cancer Classification Using Computer Vision
Nowadays, computer vision plays an essential role in disease detection, computer-aided diagnosis, and patient
risk identification. This is especially true for skin cancer, which can be fatal if not diagnosed in its early stages.
For this purpose, several computer-aided diagnostic and detection systems have been created in the past. They
were limited in their performance because of the complicated visual characteristics of skin lesion images, which
included inhomogeneous features and hazy borders. In this paper, we proposed two methods for detecting and
classifying dermoscopic images into benign and malignant tumors. The first method is using k-nearest neighbor
(KNN) as classifier when pretrained deep neural networks are used as feature extractors. The second one is
AlexNet with grey wolf optimizer, that optimizes AlexNet’s hyperparameters to get the best results. We also
tested two approaches in classifying skin cancer images, which are machine learning (ML) and deep learning
(DL). T

EPRO_DSP_011 Brain Tumor Detection and Classification Using Intelligence Techniques: An
Overview
A tumor is carried on by rapid and uncontrolled cell growth in the brain. If it is not treated in the initial phases,
it could prove fatal. Despite numerous significant efforts and encouraging outcomes, accurate segmentation and
classification continue to be a challenge. Detection of brain tumors is significantly complicated by the
distinctions in tumor position, structure, and proportions. The main disinterest of this study stays to offer
investigators, comprehensive literature on Magnetic Resonance (MR) imaging’s ability to identify brain tumors.
Using computational intelligence and statistical image processing techniques, this research paper proposed
several ways to detect brain cancer and tumors. This study also shows an assessment matrix for a specific system
using particular systems and dataset types. This paper also explains the morphology of brain tumors, accessible
data sets, augmentation methods, component extraction, and categorization among Deep Learning (DL),
Transfer Learning (TL), and Machine Learning (ML) models.
EPRO_DSP_012 Segmentation of White Blood Cells Basedon CBAM-DC-UNet
Monitoring the morphology of blood leukocytes, plays an important role in medical research, especially in the
treatment of diseases such as immunodeficiency. Traditional manual detection methods are susceptible to
numerous interference factors. Therefore, blood cells are often segmented using deep-learning algorithms. This
study proposes a U-Net model based on a combination of an attention mechanism and dilated convolutions.
First, the traditional convolution in a double convolutional module in a network is replaced by dilated
convolution, and multi-scale features are obtained by expanding the receptive field. Second, after each
convolution layer in the upsampling layer, an attention mechanism module is combined to refine the adaptive
features and improve the segmentation performance of the model. Finally, the RAdam optimizer was used to
enhance the robustness of the learning rate variations.

EPRO_DSP_013 DeepLearning and Optimization-Based Methods for Skin Lesions Segmentation: A
Review
Skin cancer is a senior public health issue that could profit from computer-aided diagnosis to decrease the
encumbrance of this widespread disease. Researchers have been more motivated to develop computer-aided
diagnosis systems because visual examination wastes time. The initial stage in skin lesion analysis is skin lesion
segmentation, which might assist in the following categorization task. It is a difficult task because sometimes
the whole lesion might be the same colors, and the borders of pigment regions can be foggy. Several studies
have effectively handled skin lesion segmentation; nevertheless, developing new methodologies to improve
efficiency is necessary. This work thoroughly analyzes the most advanced algorithms and methods for skin
lesion segmentation
EPRO_DSP_014 DeepSkin: A DeepLearning Approach for Skin Cancer Classification
Skin cancer is one of the most rapidly spreading illnesses in the world and because of the limited resources
available. Early detection of skin cancer is crucial accurate diagnosis of skin cancer identification for preventive
approach in general. Detecting skin cancer at an early stage is challenging for dermatologists, as well in recent
years, both supervised and unsupervised learning tasks have made extensive use of deep learning. One of these
models, Convolutional Neural Networks (CNN), has surpassed all others in object detection and classification
tests. The dataset is screened from MNIST: HAM10000 which consists of seven different types of skin lesions
with the sample size of 10015 is used for the experimentation. The data pre-processing techniques like sampling,
dull razor and segmentation using autoencoder and decoder is employed. Transfer learning techniques like
DenseNet169 and Resnet 50 were used to train the model to obtain the results.

EPRO_DSP_015 Feature Fusion Classifier With Dynamic Weights for Abnormality Detection of
Amniotic Fluid Cell Chromosome
Chromosomal karyotype is important to determine whether a newborn has a genetic disorder. There are two
main categories of chromosomal abnormalities: structural abnormalities, in which the chromosome structure is
altered, and chromosome number abnormalities. Manual karyotyping is complex and takes a lot of time because
it requires a high degree of domain expertise. Based on this investigation, we propose a new method of
chromosome defect detection based on deep learning with 20,299 chromosome images from Dongguan
Kanghua Hospital as data that integrates the diversity of chromosome features and trains a classifier model
based on feature fusion for chromosome abnormality detection. We put forward a feature fusion classifier with
dynamic weights (FFCDW) for chromosomal abnormality detection, after data augmentation with three deep
learning networks, ResNet, SENet, and VGG19, the three trained models are combined using a dynamic
weighting approach.
EPRO_DSP_016 Image Encryption Algorithm Basedon Chaotic Mapping and Binary Bidirectional
Zigzag Transform
With the continuous development of chaotic systems, they have increasingly become the core of the field of
image encryption, and the good performance of chaotic systems is crucial for image encryption. Some two-
dimensional chaotic maps still have drawbacks such as uneven distribution and small key space, which are
prone to destruction. To this end, a new two-dimensional logic infinite folding iterative mapping is proposed,
and an encryption algorithm is designed based on this. Experimental analysis shows that the chaotic map has
good chaotic characteristics. Secondly, a binary bidirectional zigzag transform image scrambling algorithm is
proposed. Compared with traditional zigzag transform, binary bidirectional zigzag transform has more sufficient
dislocation effects and greatly reduces the correlation between adjacent pixels in the image.

EPRO_DSP_017 Infrared and Visible Image Fusion Basedon Autoencoder Composed of CNN-
Transformer
With high sensitivity to capture rich details, visible imaging equipment can take images containing more
textures and contours which are important to visual perception. Unlike visible cameras, infrared imaging devices
can detect targets invisible in visible images, because the imaging principle of infrared sensors derives from
differences of thermal radiation. Thus, the purpose of image fusion is to merge as much meaningful feature
information from the infrared and visible images into the fused image as possible, such as contours as well as
textures of the visible image and thermal targets of the infrared image. In this paper, we propose an image fusion
network based on variational auto-encoder (VAE), which performs the image fusion process in deep hidden
layers. We divide the proposed network into image fusion network and infrared feature compensation network.
EPRO_DSP_018 Leaf Disease Detection Basedon Lightweight DeepResidual Network and Attention
Mechanism
In today’s leaf disease detection, the accuracy of recognition has never been of such importance as it is now. In
this aspect, leaf disease recognition method based on machine learning relies heavily on the size of the region
of interest and the dispersion of lesions. Professional instrument for leaf disease detection remains a challenging
task in accuracy and convenience. A new lightweight model based on advanced residual network and attention
mechanism for extracting more accurate region of interest and the lesion, SE-VRNet, was proposed. The
proposed SE-VRNet incorporated deep variant residual network (VRNet) and a squeeze-and-excitation (SE)
module with attention mechanism, in order to solve the problem that the feature extraction was difficult due to
the dispersed location of the leaf disease. The accuracy of top-1 and top-3 obtained by the model SE-VRNet on
NewData is 99.73% and 99.98%, respectively, and the accuracy of top-1 and top-3 obtained by the model on
SelfData is 95.71% and 99.89%, respectively. The experimental results on the datasets of PlantVillage, OriData,
NewData and SelfData were better than other state-of-the-art methods, demonstrating the effectiveness and
feasibility of the proposed SE-VRNet in identifying leaf diseases with mobile devices.

EPRO_DSP_019 Malaria Disease Cell Classification With Highlighting Small Infected Regions
Deep learning-based methods have become an active research area in medical imaging. Malaria is diagnosed
by testing red blood cells. Deep learning methods can be used to distinguish malaria infected cell images from
non-infected cell images. The small number of malaria dataset may limit the application of deep learning.
Moreover, the infected area in the cell images is generally vague and small, requiring more complex models
and a larger dataset to train on. Motivated by the tendency of humans to highlight important words when
reading, we propose a simple neural network training strategy for highlighting the infected pixel regions that
are mainly responsible for malaria cell classification. In our experiments on the NIH(National Institutes of
Health) malaria dataset available in public domain, the proposed method significantly improved classification
accuracy for our four different sized models, ranging from simple to complex including Resnet and Mobilenet.
Our proposed method significantly improved classification accuracy.
EPRO_DSP_020 Multiple Types of Cancer Classification Using CT/MRI Images Basedon Learning
Without Forgetting Powered DeepLearning Models
Cancer is the second biggest cause of death worldwide, accounting for one of every six deaths. On the other
hand, early detection of the disease significantly improves the chances of survival. The use of Artificial
Intelligence (AI) to automate cancer detection might allow us to evaluate more cases in less time. In this
research, AI-based deep learning models are proposed to classify the images of eight kinds of cancer, such as
lung, brain, breast, and cervical cancer. This work evaluates the deep learning models, namely Convolutional
Neural Networks (CNN), against classifying images with cancer traits. Pre-trained CNN variants such as
MobileNet, VGGNet, and DenseNet are employed to transfer the knowledge they learned with the ImageNet
dataset to detect different kinds of cancer cells. We use Bayesian Optimization to find the suitable values for
the hyperparameters. However, transfer learning could make it so that models can no longer classify the datasets
they were initially trained. So, we use Learning without Forgetting (LwF), which trains the network using only
new task data while keeping the network’s original abilities.

EPRO_DSP_021 Diagnosis of Malaria Using Double Hidden Layer Extreme Learning Machine
Algorithm With CNN Feature Extraction and Parasite Inflator
Malaria, a life-threatening disease worldwide, can be diagnosed using antigen tests and microscopy tests.
However, both of them are erroneous and time-consuming. Therefore, a trustworthy and fast early malaria
prognosis infrastructure is required. In this age of machine learning (ML), there are several ML-based methods
to do the task. This paper proposes an unorthodox method for malaria prognosis based on an extreme learning
machine (ELM) algorithm. In this regard, Convolutional Neural Networks (CNN), ELM, and double hidden
layer (DELM) have been used as classifiers. A CNN model has been used as a feature extractor and also as a
classifier to perform a comparative study. The derived features have been used to train ELM and DELM. Two
versions of the malaria image dataset have been used: one is the original dataset, and the other is a modified
dataset where ambiguous samples have been removed.
EPRO_DSP_022 A Real-Time Face DetectionMethod Basedon Blink Detection
Face anti-spoofing refers to the computer determining whether the face detected is a real face or a forged face.
In user authentication scenarios, photo fraud attacks are easy to occur, where an illegal user logs into the system
using a legitimate user’s picture. Aiming at this problem and the influence of illumination in real-time video
face recognition, this paper proposes a real-time face detection method based on blink detection. The method
first extracts the image texture features through the LBP algorithm, which eliminates the problem of
illumination changes to a certain extent. Then the extracted features are input into the ResNet network, and the
facial feature extraction is enhanced by adding an attention mechanism is added to enhance the face feature
extraction. Meanwhile, the BiLSTM method is used to extract the temporal characteristics of images from
different angles or at different times to obtain more facial details. In addition, the fusion of local and global
features is realized by SPP pooling, which enriches the expression ability of feature maps and improves
detection accuracy.

EPRO_DSP_023 Accuracy Enhancement of Hand Gesture Recognition Using CNN
Human gestures are immensely significant in human-machine interactions. Complex hand gesture input and
noise caused by the external environment must be addressed in order to improve the accuracy of hand gesture
recognition algorithms. To overcome this challenge, we employ a combination of 2D-FFT and convolutional
neural networks (CNN) in this research. The accuracy of human-machine interactions is improved by using
Ultra Wide Bandwidth (UWB) radar to acquire image data, then transforming it with 2DFFT and bringing it
into CNN for classification. The classification results of the proposed method revealed that it required less time
to learn than prominent models and had similar accuracy
EPRO_DSP_024 Change Detection Method for Wavelength Resolution SAR Images Basedon Bayes’
Theorem: An Iterative Approach
This paper presents an iterative change detection (CD) method based on Bayes’ theorem for very high-
frequency (VHF) ultra-wideband (UWB) SAR images considering commonly used clutter-plus-noise statistical
models. The proposed detection technique uses the information of the detected changes to iteratively update the
data and distribution information, obtaining more accurate clutter-plus-noise statistics resulting in false alarm
reduction. The Bivariate Rayleigh and Bivariate Gaussian distributions are investigated as candidates to model
the clutter-plus-noise, and the Anderson-Darling goodness-of-fit test is used to investigate three scenarios of
interest. Different aspects related to the distributions are discussed, the observed mismatches are analyzed, and
the impact of the distribution chosen for the proposed iterative change detection method is analyzed

EPRO_DSP_025 CNN Learning Strategy for Recognizing Facial Expressions
The ability to recognize facial expressions using computer vision is a crucial task that has numerous potential
applications. Although deep neural networks have achieved high performance, their use in the recognition of
facial expressions is still challenging. This is because different facial expressions have varying degrees of
similarities among themselves, and numerous variations cause diversity in the same facial images. In this study,
we propose a novel divide-and-conquer-based learning strategy to improve the performance of facial expression
recognition (FER). The face area in an image was detected using MobileNet, and a ResNet-18 model was
employed as a backbone deep neural network for recognizing facial expressions. Subsequently, groups
containing similar facial expressions were categorized by analyzing the confusion matrix, which represents the
inference results of the trained ResNet-18 model, and these similar facial expression groups were then utilized
to re-train the deep learning model.
EPRO_DSP_026 Facial Expression Recognition in the Wild Using Face Graph and Attention
Facial expression recognition (FER) in the wild from various viewpoints, lighting conditions, face poses, scales,
and occlusions is an extremely challenging field of research. In this study, we construct a face graph by selecting
action units that play an important role in changing facial expressions, and we propose an algorithm for
recognizing facial expressions using a graph convolutional network (GCN). We first generated an attention map
that can highlight action units to extract important facial expression features from faces in the wild. After feature
extraction, a face graph is constructed by combining the attention map with face patches, and changes in
expression in the wild are recognized using a GCN. Through comparative experiments conducted using both
lab-controlled and wild datasets, we prove that the proposed method is the most suitable FER approach for use
with image datasets captured in the wild and those under well-controlled indoor conditions.

EPRO_DSP_027 Facial Expression Transfer Basedon Conditional Generative Adversarial Networks
With the development of computer vision and image transfer, facial expression transfer has been more and more
widespread applications. But there are still some problems, such as lack of realistic expression, poor retention
of facial identity features and low synthesis efficiency. In order to solve the problems of facial expression
transfer, the paper proposes a facial expression transfer model based on conditional generative adversarial
network, which can generate a highly realistic face image with source facial expression and target facial identity
features, when gave a source face image and a target face image. The model consists of two parts: the facial
feature point fusion module and the expression transfer module. Among them, the facial feature point fusion
module uses an auto-encoder to encode the face key feature point image of the source facial expression and the
face feature key point image of the target face
EPRO_DSP_028 Hand Gesture Recognition for Characters Understanding Using Convex Hull
Landmarks and Geometric Features
With the latest advancements, hand gesture recognition is becoming an effective way of communication and
gaining popularity from a research point of view. Hearing impaired people around the world need assistance,
while sign language is only understood by a few people around the globe. It becomes challenging for
untrained people to communicate easily, research community has tried to train systems with a variety of
models to facilitate communication with hearing impaired people and also human-computer interaction.
Researchers have detected gestures with numerous recognition rates; however, the recognition rate still needs
improvement. As the images captured via cameras possess multiple issues, the light intensity variation makes
it a challenging task to extract gestures from such images, extra information in captured images, such as noise
hinders the computation time, and complex backgrounds make the extraction of gestures difficult. A novel
approach is proposed in this paper for character detection and recognition. The proposed system is divided
into five steps for hand gesture recognition

EPRO_DSP_029 Road Crack Detection Using DeepNeural Network Basedon Attention Mechanism
and Residual Structure
Intelligent detection of road cracks is crucial for road maintenance and safety. because of the interference of
illumination and totally different background factors, the road crack extraction results of existing deep learning
ways square measure incomplete, and therefore the extraction accuracy is low. we tend to designed a brand new
network model, referred to as AR-UNet, that introduces a convolutional block attention module (CBAM) within
the encoder and decoder of U-Net to effectively extract global and local detail information. The input and output
CBAM features of the model are connected to increase the transmission path of features. The BasicBlock is
adopted to replace the convolutional layer of the original network to avoid network degradation caused by
gradient disappearance and network layer growth. we tested our method on DeepCrack, Crack Forest Dataset,
and our own tagged road image dataset (RID). The experimental results show that our method focuses additional
on crack feature info and extracts cracks with higher integrity. The comparison with existing deep learning ways
conjointly demonstrates the effectiveness of our projected technique. The code is out there at:
https://github.com/18435398440/ARUnet .
EPRO_DSP_030 Role of Zoning in Facial Expression Using DeepLearning
Facial expression is an unspoken message essential to collaboration and effective discourse. An inner emotional
state of a human is expressed using facial expressions and is very effective for communication with actual
emotions. Anger, happiness, sadness, contempt, surprise, fear, disgust, and neutral are eight common
expressions of humans. Scientific community proposed several face emotion recognition techniques. However,
due to fewer face landmarks and their intensity for deep learning models, performance improvement for facial
expression recognition still needs to be improved for accurately predicting facial emotion recognition. This
study proposes a zoning-based face expression recognition (ZFER) to locate more face landmarks to perceive
deep face emotions indemnity through zoning. After face extraction, landmarks from the face, such as the eyes,
eyebrows, nose, forehead, and mouth, are extracted

EPRO_DSP_031 Universal Domain Adaptation for Remote Sensing Image Scene Classification
The domain adaptation (DA) approaches available to date are usually not well suited for practical DA scenarios
of remote sensing image classification, since these methods (such as unsupervised DA) rely on rich prior
knowledge about the relationship between label sets of source and target domains, and source data are often not
accessible due to privacy or confidentiality issues. To this end, we propose a practical universal domain
adaptation setting for remote sensing image scene classification that requires no prior knowledge on the label
sets. Furthermore, a novel universal domain adaptation method without source data is proposed for cases when
the source data is unavailable. The architecture of the model is divided into two parts: the source data generation
stage and the model adaptation stage. The first stage estimates the conditional distribution of source data from
the pre-trained model using the knowledge of class-separability in the source domain and then synthesizes the
source data. With this synthetic source data in hand, it becomes a universal DA task to classify a target sample
correctly if it belongs to any category in the source label set, or mark it as ``unknown" otherwise. In the second
stage
EPRO_DSP_032 Classification of Liver Fibrosis From Heterogeneous Ultrasound Image
With the advances in deep learning, including Convolutional Neural Networks (CNN), automated diagnosis
technology using medical images has received considerable attention in medical science. In particular, in the
field of ultrasound imaging, CNN trains the features of organs through an amount of image data, so that an
expert-level automatic diagnosis is possible only with images of actual patients. However, CNN models are
also trained on the features that reflect the inherent bias of the imaging machine used for image acquisition. In
other words, when the domain of data used for training is different from that of data applied for an actual
diagnosis, it is unclear whether consistent performance can be provided by the domain bias.

EPRO_DSP_033 Noise-Against Skeleton Extraction Framework and Application on Hand Gesture
Recognition
Extracting stable skeletons from noisy images is a challenging problem since the skeletonization method is
prone to be affected by inner and border noise. Although many methods have been proposed in the past for
increasing the antinoise ability of skeletonization methods, most of them either only overcome border noise or,
at the cost of lost topology, degrade the effects of two noises. In this paper, we propose a skeleton extraction
framework to enhance the robustness of the existing skeletonization method against both inner and border noise.
In our approach, we first use the different scales of Gaussian filters to smooth the input image and obtain
multiple representations. Then, binarization and skeletonization were performed to produce a series of binary
images and a series of skeletal images. Next, we use our measure on these binary and skeletal images to find
the most suitable skeleton. Since our measure considers both the skeleton image changes and binary image
changes caused by using a filter, the selected skeleton is sufficiently robust and has all the necessary skeletal
branches..
EPRO_DSP_034 Road Crack Detection Using DeepNeural Network Basedon Attention Mechanism
and Residual Structure
Intelligent detection of road cracks is crucial for road maintenance and safety. because of the interference of
illumination and totally different background factors, the road crack extraction results of existing deep learning
ways square measure incomplete, and therefore the extraction accuracy is low. we tend to designed a brand new
network model, referred to as AR-UNet, that introduces a convolutional block attention module (CBAM) within
the encoder and decoder of U-Net to effectively extract global and local detail information. The input and output
CBAM features of the model are connected to increase the transmission path of features. The BasicBlock is
adopted to replace the convolutional layer of the original network to avoid network degradation caused by
gradient disappearance and network layer growth. we tested our method on DeepCrack, Crack Forest Dataset,
and our own tagged road image dataset (RID). The experimental results show that our method focuses additional
on crack feature info and extracts cracks with higher integrity. The comparison with existing deep learning ways
conjointly demonstrates the effectiveness of our projected technique. The code is out there at:
https://github.com/18435398440/ARUnet .

EPRO_DSP_035 Multi-Orientation Local Texture Features for Guided Attention -BasedFusion in
Lung Nodule Classification
Computerized tomography (CT) scan images are widely used in automatic lung cancer detection and
classification. The lung nodules’ texture distribution throughout the CT scan volume can vary significantly, and
accurate identification and consideration of discriminative information in this volume can greatly help the
classification process. Deep stacks of recurrent and convolutional operations cannot entirely represent such
variations, especially in the size and location of the nodules. To model this complex pattern of inter/intra
dependencies in the CT slices of each nodule, a multi-orientation-based guided-attention module (MOGAM) is
proposed in this paper, which provides high flexibility in concentrating on the relevant information extracted
from different regions of the nodule in a non-local manner. Moreover, to provide the model with finer-grained
discriminative information from the nodule volume, specifically-designed local texture feature descriptors
(TFDs) are extracted from the nodule slices in multiple orientations. These TFDs not only represent the
distribution of textural information across multiple slices of a nodule but also encode and approximate this
distribution within each slice.

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