Sajid Javed

Sajid Javed
Khalifa University of Science and Technology · Electrical Engineering and Computer Science

Assistant Professor of Computer Vision
Computer Vision, Computational Pathology

About

87
Publications
30,875
Reads
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2,336
Citations
Citations since 2016
73 Research Items
2302 Citations
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20162017201820192020202120220100200300400500
20162017201820192020202120220100200300400500

Publications

Publications (87)
Article
Background modeling constitutes the building block of many computer-vision tasks. Traditional schemes model the background as a low rank matrix with corrupted entries. These schemes operate in batch mode and do not scale well with the data size. Moreover, without enforcing spatiotemporal information in the low-rank component, and because of occlusi...
Article
Full-text available
Principal Components Analysis (PCA) is one of the most widely used dimension reduction techniques. Given a matrix of clean data, PCA is easily accomplished via singular value decomposition (SVD) on the data matrix. While PCA for relatively clean data is an easy and solved problem, it becomes much harder if the data is corrupted by even a few outlie...
Article
Moving object detection is a fundamental step in various computer vision applications. Robust Principal Component Analysis (RPCA) based methods have often been employed for this task. However, the performance of these methods deteriorates in the presence of dynamic background scenes, camera jitter, camouflaged moving objects, and/or variations in i...
Article
Full-text available
Visual object tracking is an essential task for many computer vision applications. It becomes very challenging when the target appearance changes especially in the presence of occlusion, background clutter, and sudden illumination variations. Methods, that incorporate sparse representation and low-rank assumptions on the target particles have achie...
Conference Paper
Objectives/Scope The inspection of flare stacks operation is a challenging task that requires time and human effort. Flare stack systems undergo various types of faults, including cracks in the flare stack's structure and incomplete combustion of the flared gas, which need to be monitored in a timely manner to avoid costly and dangerous accidents....
Article
Full-text available
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep S...
Chapter
Nucleus detection in histopathology images is an instrumental step for the assessment of a tumor. Nonetheless, nucleus detection is a laborious and expensive task if done manually by experienced clinicians, and is also prone to subjectivity and inconsistency. Alternatively, the advancement in computer vision-based analysis enables the automatic det...
Article
Full-text available
Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the computer vision community and is serving as a key enabler for a wide range of applications. This technology has offered significant advantages, including reduced power consumption, reduced processing needs, and communication speedups. However, neuromorphic c...
Article
Full-text available
Despite being the most rapidly evolving biometric trait, the ear suffers from a few drawbacks, such as being affected by posture, illumination, and scaling in the two-dimensional domain. To address these issues, researchers focused on the 3D domain, as the intrinsic features of the 3D ear have significantly contributed to performance enhancement. H...
Preprint
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Full body trackers are utilized for surveillance and security purposes, such as person-tracking robots. In the Middle East, uniform crowd environments are the norm which challenges state-of-the-art trackers. Despite tremendous improvements in tracker technology documented in the past literature, these trackers have not been trained using a dataset...
Article
Recent advancements have increased the prevalence of digital image tampering. Anyone can manipulate multimedia content using editing software to alter the semantic meaning of images to deceive viewers. Since manipulations appear realistic, both humans and machines face challenges detecting forgeries. We propose a novel algorithm for authenticating...
Preprint
Full-text available
Recent years have seen an increased interest in establishing association between faces and voices of celebrities leveraging audio-visual information from YouTube. Prior works adopt metric learning methods to learn an embedding space that is amenable for associated matching and verification tasks. Albeit showing some progress, such formulations are,...
Preprint
Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage, shadow, dynamic backgrounds, and lighting variations, to name a few. Deep learning methods have been successfully applied to ad...
Preprint
Full-text available
Person-tracking robots have many applications, such as in security, elderly care, and socializing robots. Such a task is particularly challenging when the person is moving in a Uniform crowd. Also, despite significant progress of trackers reported in the literature, state-of-the-art trackers have hardly addressed person following in such scenarios....
Article
Identification of nuclear components in the histology landscape is an important step towards developing computational pathology tools for the profiling of tumor micro-environment. Most existing methods for the identification of such components are limited in scope due to heterogeneous nature of the nuclei. Graph-based methods offer a natural way to...
Preprint
Full-text available
Over the past few decades, interest in algorithms for face recognition has been growing rapidly and has even surpassed human-level performance. Despite their accomplishments, their practical integration with a real-time performance-hungry system is not feasible due to high computational costs. So in this paper, we explore the recent, fast, and accu...
Preprint
Full-text available
Robustly tracking a person of interest in the crowd with a robotic platform is one of the cornerstones of human-robot interaction. The robot platform which is limited by the computational power, rapid movements, and occlusions of the target requires an efficient and robust framework to perform tracking. This paper proposes a deep learning framework...
Preprint
Full-text available
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical exam...
Article
Nucleus detection is an important step for the analysis of histology images in the field of computational pathology. Pathologists use quantitative nuclear morphology for better cancer grading and prognostication. The nucleus detection becomes very challenging because of the large morphological variations across different types of nuclei, nuclei clu...
Article
Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vascula...
Article
Full-text available
Recent methods for visual tracking exploit a multitude of information obtained from combinations of handcrafted and/or deep features. However, the response maps derived from these feature combinations are often fused using simple strategies such as winner-takes-all or weighted sum approaches. Although some efficient fusion methods have also been pr...
Chapter
The goal of this work is to apply a denoising image transformer to remove the distortion from underwater images and compare it with other similar approaches. Automatic restoration of underwater images plays an important role since it allows to increase the quality of the images, without the need for more expensive equipment. This is a critical exam...
Article
Full-text available
Object segmentation in cluttered environments is a fundamental pre-processing step for many perception-related tasks such as vision-based robotic grasping. Most of the existing object segmentation methods are incapable of precisely segmenting unknown objects, particularly in scenarios exhibiting significant occlusion. In this paper, we propose a no...
Preprint
Full-text available
Neuromorphic vision is a bio-inspired technology that has triggered a paradigm shift in the computer-vision community and is serving as a key-enabler for a multitude of applications. This technology has offered significant advantages including reduced power consumption, reduced processing needs, and communication speed-ups. However, neuromorphic ca...
Article
Full-text available
Automatic gender classification has many potential applications including automatic annotation of images, video surveillance, security, and human-computer interaction. In the last decades, many research works focused on classifying gender using the cues from 2D images of the person’s frontal view. This limits their application in the real world. Al...
Preprint
Full-text available
Accurate and robust visual object tracking is one of the most challenging and fundamental computer vision problems. It entails estimating the trajectory of the target in an image sequence, given only its initial location, and segmentation, or its rough approximation in the form of a bounding box. Discriminative Correlation Filters (DCFs) and deep S...
Conference Paper
Full-text available
Moving Object Detection (MOD) is a fundamental step for many computer vision applications. MOD becomes very challenging when a video sequence captured from a static or moving camera suffers from the challenges: camouflage , shadow, dynamic backgrounds, and lighting variations , to name a few. Deep learning methods have been successfully applied to...
Article
Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications. It is typically formulated as estimating the target appearance model between consecutive frames. Discriminative correlation filters and their variants have achieved promising speed and accuracy for visual tracking in many challenging sc...
Article
Nucleus detection in histology images is a fundamental step for cellular-level analysis in computational pathology. In clinical practice, quantitative nuclear morphology can be used for diagnostic decision making, prognostic stratification, and treatment outcome prediction. Nucleus detection is a challenging task because of large variations in the...
Chapter
Full-text available
Analysis of fine needle aspiration cytology FNAC slides of thyroid nodules is a very crucial test before the preoperative diagnosis of thyroid malignancy. Cytology slides may be composed of different types of cells. Differentiating between cancerous cells and healthy cells plays an important role in the treatment. However, the conventional visual i...
Chapter
In Visual object tracking (VOT), Discriminative correlation filters trackers have achieved promising results for VOT in many complex scenarios. However, because of the unwanted boundary effects and lack of structural constraints, these methods suffer from performance degradation. In the current work, we propose a spatial graph-regularized correlati...
Conference Paper
Full-text available
Moving Object Segmentation (MOS) is an important topic in computer vision. MOS becomes a challenging problem in the presence of dynamic background and moving camera videos such as Pan-Tilt-Zoom cameras (PTZ). The MOS problem has been solved using unsupervised and supervised learning strategies. Recently, new ideas to solve MOS using semi-supervised...
Article
Full-text available
Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presen...
Article
Full-text available
In computational pathology, automated tissue phenotyping in cancer histology images is a fundamental tool for profiling tumor microenvironments. Current tissue phenotyping methods use features derived from image patches which may not carry biological significance. In this work, we propose a novel multiplex cellular community-based algorithm for tis...
Conference Paper
Full-text available
In this paper, we propose a deep-learning approach for human gender classification on RGB-D images. Unlike most of the existing methods, which use hand-crafted features from the human face, we exploit local information from the head and global information from whole body to classify people’s gender. A head detector is fine-tuned on YOLO to detect t...
Conference Paper
Full-text available
Visual Object Tracking (VOT) is an essential task for many computer vision applications. VOT becomes challenging when a target object faces severe occlusion, drastic illumination changes, and scale variation problems. In the literature, Discriminative Correlation Filters (DCFs)-based tracking methods have achieved promising results in terms of accu...
Conference Paper
Full-text available
Moving object detection (MOD) is an important step for many computer vision applications. In the last decade, it is evident that RPCA has shown to be a potential solution for MOD and achieved a promising performance under various challenging background scenes. However, because of the lack of different types of features, RPCA still shows degraded pe...
Conference Paper
Visual object tracking is an essential task for many computer vision applications. It becomes very challenging when the target appearance changes especially in the presence of oc-clusion, background clutter, and sudden illumination variations. Methods, which incorporate sparse representation and low-rank assumptions on the target particles have ach...
Conference Paper
Full-text available
Person re-identification (re-ID), is the task of associating the relationship among the images of a person captured from different cameras with non-overlapping field of view. Fundamental and yet an open issue in re-ID is extraction of powerful features in low resolution surveillance videos. In order to solve this, a novel Two Stream Convolutional R...
Conference Paper
Full-text available
Tissue phenotyping in cancer histology images is a fundamental step in computational pathology. Automatic tools for tissue phenotyping assist pathologists for digital profiling of the tumor microenvironment. Recently, deep learning and classical machine learning methods have been proposed for tissue phenotyping. However, these methods do not integr...
Conference Paper
Full-text available
Visual object tracking is an important step for many computer vision applications. Visual tracking becomes more challenging when the target object observes severe occlusion, lighting variations, background clutter, and deformation difficulties to name a few. In the literature, low-rank matrix decomposition methods have shown to be a potential solut...
Conference Paper
Full-text available
Robust Structural Low-Rank Tracking
Conference Paper
Full-text available
Visual object tracking is an important step for many computer vision applications. The task becomes very challenging when the target undergoes heavy occlusion, background clutters, and sudden illumination variations. Methods that incorporate sparse representation and low-rank assumptions on the target particles have achieved promising results. Howe...
Article
Full-text available
Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning met...
Article
Full-text available
Background estimation is a fundamental step in many high-level vision applications, such as tracking and surveillance. Existing background estimation techniques suffer from performance degradation in the presence of challenges such as dynamic backgrounds, photometric variations, camera jitters, and shadows. To handle these challenges for the purpos...
Article
Full-text available
In recent years visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance and security just to name a few. In the c...
Preprint
Full-text available
[Accepted in ACM Computing Surveys, February 2019]. In recent years visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real world problems such as human-computer interaction, autonomous vehicles, robotics, su...
Preprint
Full-text available
Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning met...
Preprint
Full-text available
Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texture-based techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of...
Conference Paper
Full-text available
Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texture-based techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of...
Chapter
Full-text available
A primary aim of detailed analysis of multi-gigapixel histology images is assisting pathologists for better cancer grading and prognostication. Several methods have been proposed for the analysis of histology images in the literature. However, these methods are often limited to the classification of two classes i.e., tumor and stroma. Also, most ex...
Conference Paper
A primary aim of detailed analysis of multi-gigapixel histology images is assisting pathologists for better cancer grading and prognostication. Several methods have been proposed for the analysis of histology images in the literature. However, these methods are limited for the classification of two classes i.e., tumor and stroma. Also, most existin...
Article
Full-text available
Robust PCA (RPCA) via decomposition into low-rank plus sparse matrices offers a powerful framework for a large variety of applications such as image processing, video processing and 3D computer vision. Indeed, most of the time these applications require to detect sparse outliers from the observed imagery data that can be approximated by a low-rank...
Conference Paper
Full-text available
This paper provides a comparative theoretical and experimental evaluation of solutions for robust PCA and robust subspace tracking (dynamic RPCA) that rely on the sparse+lowrank matrix decomposition formulation. The emphasis is on simple and provably correct methods. Experimental comparisons are shown for video layering (separate a given video into...
Preprint
Full-text available
In many advanced video based applications background modeling is a pre-processing step to eliminate redundant data, for instance in tracking or video surveillance applications. Over the past years background subtraction is usually based on low level or hand-crafted features such as raw color components, gradients, or local binary patterns. The back...
Article
Full-text available
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades. Recent deep learning based trackers have shown good performance on various tracking challenges. A tracking method should track objects in sequential frames accurately in challenges such as deformation, lo...
Conference Paper
Full-text available
Object tracking is one of the most challenging task and has secured significant attention of computer vision researchers in the past two decades. Recent deep learning based trackers have shown good performance on various tracking challenges. A tracking method should track objects in sequential frames accurately in challenges such as deformation, lo...
Conference Paper
Moving Object Detection (MOD) is a fundamental step in various computer vision and video surveillance systems. Methods based on Robust Principal Component Analysis (RPCA) have often been used for MOD. If the low-rank and sparse matrices are relatively coherent, e.g., if there are similarities between the moving objects and the background regions, a...
Conference Paper
Full-text available
Moving object detection is the fundamental step for various computer vision tasks. Many existing methods are still limited in accurately detecting the moving objects because of complex background scenes such as illumination condition, color saturation, and shadows etc. RPCA models have shown potential for moving object detection, where input data m...