Muhammad Shehzad Hanif

King Abdulaziz University · Department of Electrical and Computer Engineering

Smart grid power networks are essential for addressing the global energy crisis and combating climate change. In the past few decades, information and communication infrastructure have improved a lot. As a result, studying the characteristics of smart grids has become important. To accurately represent the connectivity of different components in po...

As the world’s population rises, there will be a greater need for food, which will have repercussions on the environment and on crop yields. Increased production, efficient resource allocation, climate change adaptation, and diminished food waste are the four cornerstones of Agriculture 4.0’s vision for the future of farming. Agriculture 4.0 makes...

Artificial intelligence (AI) related to intelligent control in healthcare denotes using AI techniques to enhance the management and control of healthcare processes and systems. Damage to the inner and middle ear caused by accidents and diseases even causes hearing impairment in the ear that has been harmed or injured. Traditional otoscopy devices w...

Image dehazing is a crucial task in the early stages of real-time image processing pipelines used in applications such as surveillance and advance driver assistance systems. Dehazing algorithms mitigate the spatially selective degradation of image details caused by natural phenomenon such as fog and sandstorms. The problem is exacerbated in the pre...

COVID-19 is a contagious disease distinguished by its elevated transmissibility and notable fatality rate, posing a considerable threat to the general welfare of the global populace. Accurately and efficiently detecting COVID-19 is paramount to effectively executing preventive measures and treatment methods. Medical imaging modalities, particularly...

A two-stage framework for crowd anomaly detection in single-scene or scene-dependent surveillance videos is proposed in this article. The first stage generates several hypotheses corresponding to potential anomalous regions in a video frame and the second stage verifies them to reduce false alarms and identifies crowd anomalies. In the hypotheses g...

Geometry-based Visual Odometry (VO) techniques are renowned in the fields of computer vision and robotics. They use methods from multi-view geometry to estimate camera motion from visual data obtained from one or more cameras. Tracking the camera motion precisely between different views is dependent on the correct estimation of correspondences betw...

Diabetic Retinopathy (DR) is an eye disorder in patients with diabetes. Detection of DR presence and its complications using fundus images at an early stage helps prevent its progression to the advanced levels. In the recent years, several well-designed Convolutional Neural Networks (CNN) have been proposed to detect the presence of DR with the hel...

Many countries use traffic enforcement camera to monitor the speed limit and capture over speed violations. The main objective of such a system is to enforce the speed limits which results in the reduction of number of accidents, fatalities, and serious injuries. Traditionally, the task is carried out manually by the enforcement agencies with the h...

Diabetic retinopathy, an eye disease commonly afflicting diabetic patients, can result in loss of vision if prompt detection and treatment are not done in the early stages. Once the symptoms are identified, the severity level of the disease needs to be classified for prescribing the right medicine. This study proposes a deep learning-based approach...

Crowd density estimation is a challenging research problem in computer vision and has many applications in commercial as well as defense sectors. Various crowd density estimation methods have been proposed by researchers in the past but there is an utmost need for accurate, robust and efficient crowd density estimation techniques for its practical...

An efficient offline system for writer independent signature verification is proposed in this work which is quite a difficult task in computer vision. The proposed system employs a novel global representation of signatures followed by the Mahalanobis distance based dissimilarity score to discriminate between the original signatures and their skille...

We propose a novel residual network called competitive residual network (CoRN) for image classification. The proposed network is composed of residual units having two identical blocks each containing convolutional filters, batch normalization, and a maxout unit. The maxout unit enables the competition among the convolutional filters and reduces the...

The problem of learning a similarity measure for cross-view person identification in a disjointed camera network is addressed. Our learning framework is based on the projected gradient approach and is suitable for large-scale applications. The hinge loss with the triplet constraints is used as the objective function. Contrary to other approaches wh...

Image stabilization aims to compensate and smoothen the effects of undesired trembling motion of cameras mounted on non-static platforms. It becomes quite a challenging task in the case of moving platforms, such as ground vehicles, unmanned aerial vehicles and handheld devices. Many satisfactory solutions to the image stabilization problem are prop...

Histogram of oriented gradient (HOG) features in combination with support vector machine (SVM) classification are still widely reported as a de facto standard benchmark to evaluate contemporary pedestrian detectors employing a vast spectrum of sophisticated features and classification schemes. In this paper, however, it has been shown that these be...

Fast and robust video based pedestrian detection has been an active research area in computer vision for the past many years and is still considered a challenging task. Despite appearance of several sophisticated algorithms performing conspicuously on standard datasets, the goal of achieving an acceptable detection performance in real-time scenario...

Descriptor and metric learning using deep convolutional neural networks (CNNs) have drawn attention of researchers in the domain of computer vision due to their remarkable performance over traditional methods. Different networks like two-channel, Siamese and triplet, etc., have been proposed recently with the aim to learn a metric or a low dimensio...

Integrated metric combining both difference and commonness of image pairs has shown to achieve superior performance over difference-only based metrics in similarity learning. The integrated metric can be learned quickly by computing the log-likelihood ratio between the probability distribution functions of similar and dissimilar image pairs. Under...

Simultaneous Localization and Mapping (SLAM) is a vital task for autonomous robots moving in unknown environments. It is a rigorous and complex problem where the signals of different modalities originating from sensors as diverse as laser range finders, IR sensors, ultrasonic transducers, odometers and inertial measurement units are used in its dif...

In this article, we propose two kinds of improvements to a baseline tracker that employs the tracking-by-detection framework. First, we explore different feature spaces by employing features commonly used in object detection to improve the performance of detector in feature space. Second, we propose a robust scale estimation algorithm that estimate...

We present here a complete system for the localization of facial features in frontal face images. In the first step, face detection is performed using Viola & Jones state of art algorithm. Then, a cascade of neural networks localizes precisely 28 facial features. The first network performs a coarse detection of three areas in the image correspondin...

We present a robust and efficient method for extracting textual information in the metro and train stations. The textual information in the train stations is destined to guide passengers about the directions, name of the station, etc. Our proposed method for text detection is based on the adaptive boosting method (AdaBoost algorithm) to construct a...

We present in this paper a new facial feature localizer. It uses a kind of auto-associative neural network trained to localize specific facial features (like eyes and mouth corners) in orientation-free face-images (i.e. images where faces are rotated in-plane and out-of-plane). To increase localization accuracy, two extensions are presented. The fi...

We present an algorithm for the on-board vision vehicle detection problem using a cascade of boosted classifiers. Three families of features are compared: the rectangular filters (Haar-like features), the histograms of oriented gradient (HoG), and their combination (a concatenation of the two preceding features). A comparative study of the results...

In this paper, we propose a texture based technique to detect text in grey level natural scene images. This work is a part of the project called Intelligent Glasses. It is a wearable system to facilitate navigation and to assist the blind and visually impaired persons in real world. It has three parts, a bank of stereovision, a processing unit for...

In this paper, we present a texture-based text detection scheme for detecting text in natural scene images. This is a preliminary work towards a complete system of text detection, localization and recognition in order to help visually impaired persons. We have employed spatial histograms computed from gray-level co-occurrence matrices for texture c...

Binarization of document images is mostly used as a preprocessing step in document image analysis. Proper binarization is very important so as to save all or maximum subcomponents such as text, background and picture. The work of Niblack is significant and has most acceptable results out of many techniques of thresholding. In this paper, we have pr...