All Outputs (62)

Visual Attention Assisted Games (2023)
Conference Proceeding

In this work, we propose a committee of attention models developed for improving the deep reinforcement learning frequently used for games. The game environment is manifested with spatial and temporal attention mechanisms so as to focus on important... Read More about Visual Attention Assisted Games.

Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction (2023)
Conference Proceeding

Traffic flow prediction has been regarded as a critical problem in intelligent transportation systems. An accurate prediction can help mitigate congestion and other societal problems while facilitating safer, cost and time-efficient travel. However,... Read More about Optimization and Performance Evaluation of Hybrid Deep Learning Models for Traffic Flow Prediction.

Deep Neural Network Based Attention Model for Structural Component Recognition (2023)
Conference Proceeding

The recognition of structural components from images/videos is a highly complex task because of the appearance of huge components and their extended existence alongside, which are relatively small components. The latter is frequently overestimated or... Read More about Deep Neural Network Based Attention Model for Structural Component Recognition.

Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition (2022)
Journal Article

Image/video based defect recognition is a crucial task in automating visual inspection of concrete structures. Although some progress has been made to automatically recognize defects in concrete structural images, significant challenges still exist.... Read More about Kernelized dynamic convolution routing in spatial and channel interaction for attentive concrete defect recognition.

MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification (2022)
Journal Article

AbstractIn this work, we propose an attention-based deep convolutional neural network (CNN) model as an assistive computer-aided tool to classify common types of macular diseases: age-related macular degeneration, diabetic macular edema, diabetic ret... Read More about MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification.

StructureNet: Deep Context Attention Learning for Structural Component Recognition (2022)
Conference Proceeding

Structural component recognition using images is a very challenging task due to the appearance of large components and their long continuation, existing jointly with very small components, the latter are often outcasted/missed by the existing methodo... Read More about StructureNet: Deep Context Attention Learning for Structural Component Recognition.

Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification (2021)
Journal Article

In this article, we propose a deep architecture stemming from a perturbed composite attention mechanism with the following two novel attention modules: Multilevel perturbed spatial attention (MPSA) and multidimension attention (MDA) for macular optic... Read More about Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification.

Stand-Alone Composite Attention Network for Concrete Structural Defect Classification (2021)
Journal Article

Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification. (2021)
Journal Article

Automatic machine classification of concrete structural defects in images poses significant challenges because of multitude of problems arising from the surface texture, such as presence of stains, holes, colors, poster remains, graffiti, marking and... Read More about Interleaved Deep Artifacts-Aware Attention Mechanism for Concrete Structural Defect Classification..

Deep Regularized Discriminative Network (2021)
Journal Article

Traditional linear discriminant analysis (LDA) approach discards the eigenvalues which are very small or equivalent to zero, but quite often eigenvectors corresponding to zero eigenvalues are the important dimensions for discriminant analysis. We pro... Read More about Deep Regularized Discriminative Network.

GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment (2021)
Journal Article

Glaucoma is a chronic eye condition causing irreversible vision damage and presently stands as the second leading cause of blindness worldwide. Damaged optic disc and optic cup assessment in color fundus image has been shown to be a promising method... Read More about GlaucoNet: Patch-Based Residual Deep Learning Network for Optic Disc and Cup Segmentation Towards Glaucoma Assessment.

Variance-guided attention-based twin deep network for cross-spectral periocular recognition (2020)
Journal Article

Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification (2020)
Journal Article

In this work, we propose a deep multi-deformation aware attention learning (MDAL) architecture comprising of multi-scale committee of attention (MSCA) and fine-grained feature induced attention (FGIA) modules to classify multi-target multi-class defe... Read More about Multi-Deformation Aware Attention Learning for Concrete Structural Defect Classification.

Deep Convolutional Neural Network for Double-Identity Fingerprint Detection (2020)
Journal Article

Twin Deep Convolutional Neural Network-based Cross-spectral Periocular Recognition (2020)
Conference Proceeding

Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification (2019)
Journal Article

In this letter, we propose a multi-level dual-attention model to classify two common macular diseases, age-related macular degeneration (AMD) and diabetic macular edema (DME) from normal macular eye conditions using optical coherence tomography (OCT)... Read More about Multi-level Dual-attention Based CNN for Macular Optical Coherence Tomography Classification.

Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism (2019)
Presentation / Conference

Autism spectrum disorders (ASD) impact the cognitive, social, communicative and behavioral abilities of an individual. The development of new clinical decision support systems is of importance in reducing the delay between presentation of symptoms an... Read More about Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism.

Improved Lifelog Ego-centric Video Summarization Using Ensemble of Deep Learned Object Features (2019)
Presentation / Conference

The ImageCLEF 2017 lifelog summarization challenge [10, 12] was established to develop a benchmark for summarizing egocentric lifelogging videos based on our daily activities, such as ‘commute to work’ or ‘cooking at home’. In this paper, we propose... Read More about Improved Lifelog Ego-centric Video Summarization Using Ensemble of Deep Learned Object Features.

Enhanced Deep Video Summarization Network (2019)
Presentation / Conference

Video summarization is understanding video which aims to get an abstract view of the original video sequence by the concatenation of keyframes representing the highlights of the video. In this work, we propose an enhanced deep summarization network (... Read More about Enhanced Deep Video Summarization Network.

Cross-spectral Periocular Recognition: a Survey (2019)
Conference Proceeding

Among many biometrics such as face, iris, fingerprint and others, periocular region has the advantages over other biometrics because it is non-intrusive and serves as a balance between iris or eye region (very stringent, small area) and the whole fac... Read More about Cross-spectral Periocular Recognition: a Survey.