Syed Afaq Ali Shah

• PhD (3D Computer Vision/Machine Learning), MS & BSc (Electrical Engineering)
• Senior Lecturer at Edith Cowan University

Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by capturing pixel intensity changes with exceptional dynamic range, minimal latency, and energy efficiency, setting them apart from conventional frame-based cameras. The distinctive capabilities of event cameras have ignited significant interest in the domain o...

Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpre...

The astounding success made by artificial intelligence in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. Many junior researchers faced a lack of data, because of a variety of reasons. Me...

Deep learning, a branch of artificial intelligence, has achieved unprecedented performance in several domains including medicine to assist with efficient diagnosis of diseases, prediction of disease progression and pre-screening step for physicians. Due to its significant breakthroughs, deep learning is now being used for the diagnosis of arthritis...

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields, such as computer vision and healthcare. Particularly, DL is experiencing an increasing development in advanced medical image analysis applications in terms of segmentation, classification, detection, and other tasks. On the one hand, tremendous needs that...

The physical and textural attributes of objects have been widely studied for recognition, detection and segmentation tasks in computer vision. A number of datasets, such as large scale ImageNet, have been proposed for feature learning using data hungry deep neural networks and for hand-crafted feature extraction. To intelligently interact with obje...

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally, producing new variants and has become a pandemic. People have lost their lives not only due to the virus but also because of the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop robus...

Australia has a reputation for producing a reliable supply of high-quality barley in a contaminant-free climate. As a result, Australian barley is highly sought after by malting, brewing, distilling, and feed industries worldwide. Barley is traded as a variety-specific commodity on the international market for food, brewing and distilling end-use,...

Deep learning techniques have led to remarkable breakthroughs in the field of generic object detection and have spawned a lot of scene-understanding tasks in recent years. Scene graph has been the focus of research because of its powerful semantic representation and applications to scene understanding. Scene Graph Generation (SGG) refers to the tas...

Alzheimer’s disease (AD) is the most common type of dementia that still has no effective treatment. Accurate classification of AD can help in its diagnosis and selection of the most effective treatment options. In the last decade, several studies have proven the effectiveness of deep learning algorithms for AD diagnosis. In this paper, we propose a...

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not require any training. We represent the gallery image sets as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate regression models for each i...

Deep learning has become an integral part of various computer vision systems in recent years due to its outstanding achievements for object recognition, facial recognition, and scene understanding. However, deep neural networks (DNNs) are susceptible to be fooled with nearly high confidence by an adversary. In practice, the vulnerability of deep le...

The astounding success made by artificial intelligence (AI) in healthcare and other fields proves that AI can achieve human-like performance. However, success always comes with challenges. Deep learning algorithms are data-dependent and require large datasets for training. The lack of data in the medical imaging field creates a bottleneck for the a...

Liver lesion segmentation is an essential process to assist doctors in hepatocellular carcinoma diagnosis and treatment planning. Multi-modal positron emission tomography and computed tomography (PET-CT) scans are widely utilized due to their complementary feature information for this purpose. However, current methods ignore the interaction of info...

Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time test...

Lung cancer is a life-threatening disease and its diagnosis is of great significance. Data scarcity and unavailability of datasets is a major bottleneck in lung cancer research. In this paper, we introduce a dataset of pulmonary lesions for designing the computer-aided diagnosis (CAD) systems. The dataset has fine contour annotations and nine attri...

The fusion of temporal consistency and semantic information with limited foreground information for background segmentation using deep learning is an underinvestigated problem. In this paper, we explore the relation between temporal consistency and semantic information based on the law of total probability. A highly concise framework is proposed to...

Existing Image Captioning (IC) systems model words as atomic units in captions and are unable to exploit the structural information in the words. This makes representation of rare words very difficult and out-of-vocabulary words impossible. Moreover, to avoid computational complexity, existing IC models operate over a modest sized vocabulary of fre...

Image captioning is a challenging vision-to-language task, which has garnered a lot of attention over the past decade. The introduction of Encoder-Decoder based architectures expedited the research in this area and provided the backbone of the most recent systems. Moreover, leveraging relationships between objects for holistic scene understanding,...

Existing Image Captioning (IC) systems model words as atomic units in captions and are unable to exploit the structural information in the words. This makes representation of rare words very difficult and out-of-vocabulary words impossible. Moreover, to avoid computational complexity, existing IC models operate over a modest sized vocabulary of fre...

People with Color Vision Deficiency (CVD) cannot distinguish some color combinations under normal situations. Recoloring becomes a necessary adaptation procedure. In this paper, in order to adaptively find the key color components in an image, we first propose a self-adapting recoloring method with an Improved Octree Quantification Method (IOQM). S...

The area of automatic image caption evaluation is still undergoing intensive research to address the needs of generating captions which can meet adequacy and fluency requirements. Based on our past attempts at developing highly sophisticated learning-based metrics, we have discovered that a simple cosine similarity measure using the Mean of Word Em...

Effective mining of social media, which consists of a large number of users is a challenging task. Traditional approaches rely on the analysis of text data related to users to accomplish this task. However, text data lacks significant information about the social users and their associated groups. In this paper, we propose CommuNety, a deep learnin...

SARS-CoV2, which causes coronavirus disease (COVID-19) is continuing to spread globally and has become a pandemic. People have lost their lives due to the virus and the lack of counter measures in place. Given the increasing caseload and uncertainty of spread, there is an urgent need to develop machine learning techniques to predict the spread of C...

Alan Turing’s pioneering vision of machines in the 1950s, that are capable of thinking like humans is still what Artificial Intelligence (AI) and Deep Learning research aspires to manifest, 70 years on. With replicating or modeling human intelligence as the ultimate goal, AI’s Holy Grail is to create systems that can perceive and reason about the w...

In the past decade, deep learning (DL) has achieved unprecedented success in numerous fields including computer vision, natural language processing, and healthcare. In particular, DL is experiencing an increasing development in applications for advanced medical image analysis in terms of analysis, segmentation, classification, and furthermore. On t...

The broad learning system (BLS) is designed based on the technology of compressed sensing and pseudo-inverse theory, and consists of feature nodes and enhancement nodes, has been proposed recently. Compared with the popular deep learning structures, such as deep neural networks, BLS has the ability of rapid incremental learning and can remodel the...

Graph convolutional networks (GCNs) generalize convolutional neural networks into irregular graph-like structures. Generally, graph topologies are set by hand and fixed over all layers. Handcrafted connections may not be optimal and cannot fully use the self-learning ability of deep learning. In this work, we explore a topology-learnable graph conv...

The aim of this paper is to classify conductive material corrosion by eddy current pulsed thermography. Thermal transient images generate a large of amount of data which is difficult for accurate detection and classification of the different corrosion materials, especially with the hidden corrosion. We apply Deep Boltzmann Machines (DBM) network to...

Multi-organ segmentation is a challenging task due to the label imbalance and structural differences between different organs. In this work, we propose an efficient cascaded V-Net model to improve the performance of multi-organ segmentation by establishing dense Block Level Skip Connections (BLSC) across cascaded V-Net. Our model can take full adva...

Various methods to deal with graph data have been proposed in recent years. However, most of these methods focus on graph feature aggregation rather than graph pooling. Besides, the existing top-k selection graph pooling methods have a few problems. First, to construct the pooled graph topology, current top-k selection methods evaluate the importan...

Scene text detection has received attention for years and achieved an impressive performance across various benchmarks. In this work, we propose an efficient and accurate approach to detect multioriented text in scene images. The proposed feature fusion mechanism allows us to use a shallower network to reduce the computational complexity. A self-at...

Deep learning based object recognition methods have achieved unprecedented success in the recent years. However, this level of success is yet to be achieved on multimodal RGB-D images. The latter can play an important role in several computer vision and robotics applications. In this paper, we present spatial hierarchical analysis deep neural netwo...

Automatic evaluation metrics hold a fundamental importance in the development and fine-grained analysis of captioning systems. While current evaluation metrics tend to achieve an acceptable correlation with human judgements at the system level, they fail to do so at the caption level. In this work, we propose a neural network-based learned metric t...

Automatic evaluation metrics hold a fundamental importance in the development and fine-grained analysis of captioning systems. While current evaluation metrics tend to achieve an acceptable correlation with human judgements at the system level, they fail to do so at the caption level. In this work, we propose a neural network-based learned metric t...

Convolutional long short-term memory (ConvLSTM) networks have been widely used for action/gesture recognition, and different attention mechanisms have also been embedded into ConvLSTM networks. This paper explores the redundancy of spatial convolutions and the effects of the attention mechanism in ConvLSTM, based on our previous gesture recognition...

Detecting relations among objects is a crucial task for image understanding. However, each relationship involves different objects pair combinations, and different objects pair combinations express diverse interactions. This makes the relationships, based just on visual features, a challenging task. In this paper, we propose a simple yet effective...

This paper proposes novel machine learning approaches to predict the outcome of facial rejuvenation prior to a cosmetic procedure. This is achieved by estimating the required amount of dermal filler volume which needs to be applied on the face by learning the underlying structural mapping from the pre and post treatment 3D face images. We develop a...

The automatic evaluation of image descriptions is an intricate task, and it is highly important in the development and fine-grained analysis of captioning systems. Existing metrics to automatically evaluate image captioning systems fail to achieve a satisfactory level of correlation with human judgements at the sentence level. Moreover, these metri...

Continuous gesture recognition aims at recognizing the ongoing gestures from continuous gesture sequences, and is more meaningful for the scenarios where the start and end frames of each gesture instance are generally unknown in practical applications. This paper presents an effective deep architecture for continuous gesture recognition. Firstly, c...

Scene understanding is a significant research topic in computer vision, especially for robots to understand their environment intelligently. Semantic scene segmentation can help robots to identify the objects that are present in their surroundings, while semantic scene completion can enhance the ability of the robot to infer the object shape, which...

This paper proposes a novel image set classification technique based on the concept of linear regression. Unlike most other approaches, the proposed technique does not involve any training or feature extraction. The gallery image sets are represented as subspaces in a high dimensional space. Class specific gallery subspaces are used to estimate reg...

Deep convolutional neural networks (DCNNs) have been driving significant advances in semantic image segmentation due to their powerful feature representation for recognition. However, their performance in preserving object boundaries is still not satisfactory. Visual mechanism theory indicates that image segmentation tasks require not only recognit...

We discussed neural networks in Chapter 3. CNNs are one of the most popular categories of neural networks, especially for high-dimensional data (e.g., images and videos). CNNs operate in a way that is very similar to standard neural networks. A key difference, however, is that each unit in a CNN layer is a two- (or high-) dimensional filter which i...

We have covered the basic modules in the previous chapters which can be joined together to develop CNN-based deep learning models. Among these modules, we covered convolution, subsampling and several other layers which form large-scale CNN architectures. We noticed that the loss functions are used during training to measure the difference between t...

There have been a lot of interest from academics (e.g., The University of California Berkeley, New York University, The University of Toronto, The University of Montreal) and industry groups (e.g., Google, Facebook, Microsoft) to develop deep learning frameworks. It is mainly due to their popularity in many applications domains over the last few ye...

The application of deep learning algorithms, especially CNNs, to computer vision problems have seen a rapid progress. This has led to highly robust, efficient, and flexible vision systems. This book aimed to introduce different aspects of CNNs in computer vision problems. The first part of this book (Chapter 1 and Chapter 2) introduced computer vis...

In Chapter 4, we discussed different architecture blocks of the CNN and their operational details. Most of these CNN layers involve parameters which are required to be tuned appropriately for a given computer vision task (e.g., image classification and object detection). In this chapter, we will discuss various mechanisms and techniques that are us...

Before going into the details of the CNNs, we provide in this chapter an introduction to artificial neural networks, their computational mechanism, and their historical background. Neural networks are inspired by the working of cerebral cortex in mammals. It is important to note, however, that these models do not closely resemble the working, scale...

Feature extraction and classification are two key stages of a typical computer vision system. In this chapter, we provide an introduction to these two steps: their importance and their design challenges for computer vision tasks.

Computer vision is a very broad research area which covers a wide variety of approaches not only to process images but also to understand their contents. It is an active research field for convolutional neural network applications. The most popular of these applications include, classification, segmentation, detection, and scene understanding. Most...

Three-dimensional (3D) object recognition is a challenging task for many applications including autonomous robot navigation and scene understanding. Accurate recognition relies on the selection/learning of discriminative features that are in turn used to uniquely characterize the objects. This paper proposes a novel Evolutionary Feature Learning (E...

Colour vision deficiency (CVD) is a genetic condition that has troubled people for a long time. This study proposes an improved colour-to-grey method for CVD using image segmentation and a colour difference model. In this method, the colour image is first segmented using a region growing method so that each region corresponds to one colour. Next, t...

With the continuous development of the Internet and information technology, more and more mobile terminals, wear equipment etc. contribute to the tremendous data. Thanks to the distributed computing, we can analyze the big data with quite high speed. However, many kinds of big data have an obvious common character that the datasets grow incremental...

We propose a novel image set classification technique using linear regression models. Downsampled gallery image sets are interpreted as subspaces of a high dimensional space to avoid the computationally expensive training step. We estimate regression models for each test image using the class specific gallery subspaces. Images of the test set are t...

The three-dimensional (3D) modeling and recognition of 3D objects have been traditionally performed using local features to represent the underlying 3D surface. Extraction of features requires cropping of several local surface patches around detected keypoints. Although an important step, the extraction and representation of such local patches adds...

We present a novel technique for image set based face/object recognition, where each gallery and query example contains a face/object image set captured from different viewpoints, background, facial expressions, resolution and illumination levels. While several image set classification approaches have been proposed in recent years, most of them rep...

We present a novel technique for image set based face/object recognition, where each gallery and query example contains a face/object image set captured from different viewpoints, background, facial expressions, resolution and illumination levels. While several image set classification approaches have been proposed in recent years, most of them rep...

Object segmentation is a fundamental research topic in computer vision. While, only the color information for object segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth segmentation is now attracting significant attention. This paper presents a novel algorithm for depth segmentation. T...

Object segmentation is a fundamental research topic in computer vision. While, only the color information for object segmentation has been the main focus of research, with the availability of low cost color plus range sensors, depth segmentation is now attracting significant attention. This paper presents a novel algorithm for depth segmentation. T...

This paper tackles the problem of feature matching and range image registration. Our approach is based on a novel set of discriminating three-dimensional (3D) local features, named 3D-Vor (Vorticity). In contrast to conventional local feature representation techniques, which use the vector field (i.e. surface normals) to just construct their local...

Despite the advent and popularity of low-cost commercial sensors (e.g., Microsoft Kinect), research in 3D vision still primarily focuses on the development of advanced algorithms geared towards high resolution data. This paper presents a comparative performance evaluation of renowned state-of-the-art 3D local surface descriptors for the task of reg...

Landmark detection has proven to be a very challenging task in biometrics. In this paper, we address the task of facial component-landmark detection. By “component” we refer to a rectangular subregion of the face, containing an anatomical component (e.g., “eye”). We present a fully-automated system for facial component-landmark detection based on m...

In this chapter, we present advances that aid in overcoming the challenges encountered in 3D face recognition. First, we present a fully automatic 3D face recognition system, UR3D, which has been proven to be robust under variations in expressions. Second, we demonstrate how to handle pose variations. Finally, we demonstrate how the problems relate...