Computer vision systems are major research items for autonomous vehicles. However, it is often challenging to understand the road scene, especially when objects are small and overlapping. To address these problems, this paper proposes a deep learning-based pedestrian detection method for small and overlapping objects. The proposed method adopts a parallel feature pyramid network with multi-scale feature

In intelligent transport systems, detection and identification of vehicle types enact a substantial role. In this context, this paper addresses the detection and pose classification of a specific vehicle type: auto-rickshaws which have been heavily neglected by the publicly available vehicle datasets, but remains the most commonly used and cheap form of transportation in south Asian countries. Here

Shortly after deep learning algorithms were applied to Image Analysis, and more importantly to medical imaging, their applications increased significantly to become a trend. Likewise, deep learning applications (DL) on pulmonary medical images emerged to achieve remarkable advances leading to promising clinical trials. Yet, coronavirus can be the real trigger to open the route for fast integration

With the emergence in modern dentistry, the study of dental occlusion has been a subject of major interest. The aim of the present study is to investigate the capabilities of deep learning for the classification of dental occlusion using 3D images that has an exciting impact in several fields of dental anatomy. In present work, the 3D stereolithography (STL) files depicting the dental structures are

Over the past two decades, researchers in the field of biometrics have presented a wide variety of coding-based palmprint recognition methods. These approaches mainly rely on extracting the texture features, e.g. line orientations, and phase information, using different filters. In this paper, we propose a new efficient palmprint recognition method based on the Different of Block Means. In the proposed

Wildfires are a serious threat to ecosystems and human life. Usually, smoke is generated before the flame, and due to the diffusing nature of the smoke, we can detect smoke from a distance, so wildfire smoke detection is especially important for early warning systems. In this paper, we propose a 3D convolution-based encoder–decoder network architecture for video semantic segmentation in wildfire smoke

In this work, we investigate semi-supervised learning (SSL) for image classification using adversarial training. Previous results have illustrated that generative adversarial networks (GANs) can be used for multiple purposes in SSL . Triple-GAN, which aims to jointly optimize model components by incorporating three players, generates suitable image-label pairs to compensate for the lack of labeled

Autonomous detection of structural defect from images is a promising, but also challenging task to replace manual inspection. With the development of deep learning algorithms, several studies have adopted deep convolutional neural networks (CNN) or fully convolutional networks (FCN) to detect cracks in pixel-level. However, a fundamental property of cracks, that they are rotation invariant, has never

The accurate estimation of camera pose using numerous line correspondences in real time is a challenging task. This paper presents a non-iterative approach to solve the Perspective-n-Line (PnL) problem. The method can provide high speed and global optimality, as well as linear complexity. A nonlinear least squares (non-LLS) objective function is first formulated by parameterizing the rotation matrix

In contemporary times, machine learning is being used in almost every field due to its better performance. Here, we consider different machine learning methods such as logistic regression, random forest, support vector classifier (SVC), AdaBoost classifier, bagging classifier, voting classifier, and Xception model to classify the breast cancer tumor and evaluate their performances. We used a standard

Different approaches were proposed to design deep CNNs for semantic segmentation. Usually, they are built upon an encoder–decoder architecture and require computationally expensive operations on high-resolution activation maps. Since for real-time segmentation the costs are critical, efficient approaches compromise spatial information to achieve real-time segmentation but with a considerable drop in

In recent years, domain adaptation and transfer learning are known as promising techniques with admirable performance to deal with problems with distribution difference between the training (source domain) and test (target domain) data. In this paper, a novel unsupervised multi-source transductive transfer learning approach, referred to as multi-source domain adaptation for image classification (MDA)

In this paper, multiple instance learning (MIL) algorithms to automatically perform root detection and segmentation in minirhizotron imagery using only image-level labels are proposed. Root and soil characteristics vary from location to location, and thus, supervised machine learning approaches that are trained with local data provide the best ability to identify and segment roots in minirhizotron

In this work, we present a new, algorithm for multi-domain learning. Given a pretrained architecture and a set of visual domains received sequentially, the goal of multi-domain learning is to produce a single model performing a task in all the domains together. Recent works showed how we can address this problem by masking the internal weights of a given original convnet through learned binary variables

We present an efficient and accurate people detection approach based on deep learning to detect people attacks and intrusion in video surveillance scenarios Unlike other approaches using background segmentation and pre-processing techniques, which are not able to distinguish people from other elements in the scene, we propose WatchNet++ that is a depth-based and sequential network that localizes people

Probabilistic topic modelings, such as latent Dirichlet allocation (LDA) and correlated topic models (CTM), have recently emerged as powerful statistical tools for processing video content. They share an important property, i.e., using a common set of topics to model all data. However, such property can be too restrictive for modeling complex visual data such as crowd scenes where multiple fields of

A significant bottleneck in building large-scale systems for image and video categorization is the requirement of labeled data. Manual labeling effort could be overcome by using the massive amount of web data. However, this type of data is collected through searching on the category names and is likely to inherit noise. In this study, (1) the primary objective is to improve utilizing weakly labeled

Glaucoma is the second-most dominant cause for irreversible blindness, resulting in damage to the optic nerve. Ophthalmologist diagnoses this disease using a retinal examination of the dilated pupil. Since the diagnosis is a manual and laborious procedure, an automated approach for faster diagnosis is desirable. Convolutional neural networks (CNN) could allow automation of the diagnosis procedure due

Style transfer is to render given image contents in given styles, and it has an important role in both computer vision fundamental research and industrial applications. Following the success of deep learning-based approaches, this problem has been re-launched recently, but still remains a difficult task because of trade-off between preserving contents and faithful rendering of styles. Indeed, how well-balanced

This article studies the problem of detecting cerebral micro-bleeds (CMBs) using a convolutional neural network (CNN). Cerebral micro-bleeds (CMBs) are increasingly recognized neuroimaging findings, occurring with cerebrovascular diseases, dementia, and normal aging. Naturally enough, it becomes necessary to detect CMBs in the early stages of life. The focus of this article is to infuse new techniques

The paper presents a fast method of fully automatic localization and classification of defects in aluminium castings based on computed microtomography images. In the light of current research and based on available publications, where such analysis is made on the basis of images obtained from standard radiography (x-ray), this is a new approach which uses microtomographic images (\(\mu \)-CT). In addition

Glaucoma is a retinal disease caused due to increased intraocular pressure in the eyes. It is the second most dominant cause of irreversible blindness after cataract, and if this remains undiagnosed, it may become the first common cause. Ophthalmologists use different comprehensive retinal examinations such as ophthalmoscopy, tonometry, perimetry, gonioscopy and pachymetry to diagnose glaucoma. But

Single image dehazing is a challenging task due to its ambiguous nature. In this paper we present a new model based on generative adversarial networks (GANs) for single image dehazing, called as dehazing GAN. In contrast to estimating the transmission map and the atmospheric light separately as most existing deep learning methods, dehazing GAN restores the corresponding hazy-free image directly from

Neurodegenerative disorder such as Parkinson’s disease (PD) is among the severe health problems in our aging society. It is a neural disorder that affects people socially as well as economically. It occurs due to the failure of the brain’s dopamine-producing cells to produce enough dopamine to enable the motor movement of the body. This disease primarily affects vision, speech, movement problems, and

Traditional detection methods are not sensitive to small-sized tomato organs (flowers and fruits), because the immature green tomatoes are highly similar to the background color. The overlap among fruits and the occlusion of stems and leaves on tomato organs can lead to false and missing detection, which decreases the accuracy and generalization ability of the model. Therefore, a tomato organ recognition

There is a growing interest in computational approaches permitting accurate detection of nonverbal signs of depression and related symptoms (i.e., anxiety and distress) that may serve as minimally intrusive means of monitoring illness progression. The aim of the present work was to develop a methodology for detecting such signs and to evaluate its generalizability and clinical specificity for detecting

Multi-view triangulation is an essential step in recovering three-dimensional structure from a set of images. It is a well-studied problem in computer vision with many suboptimal and optimal methods based on different optimality criteria. In this paper, we assess the ability of evolutionary computation (EC) methods in finding highly accurate solutions to this problem. We use an overlaying Luus–Jaakola

Traditional level set-based image segmentation method has to solve the level set evolution equation which is the Euler–Lagrange equation of the energy functional defined on the image domain. Solving level set evolution equation is very time-consuming, and reinitialization is usually needed. The level set evolution equation can also be solved by mathematical morphology. The morphological implementation

In this paper, we propose a method to select parameter values for stereo matching methods. The proposed method was trained in a supervised manner, and an evolutionary algorithm is used to select optimized parameter values for a given domain and a cost function constructed to measure the goodness level of candidate parameter values. Performance of the proposed method is compared to that of five current

Crowd behaviour analysis using vision has been subject to many different approaches. Multi-purpose crowd descriptors are one of the more recent approaches. These descriptors provide an opportunity to compare and categorize various types of crowds as well as classify their respective behaviours. Nevertheless, the automated calculation of descriptors which are expressed as measurements with accurate

With the increasing number of cases as well as care costs, Alzheimer’s disease has gained more interest in several scientific communities especially medical and computer science. Clinical and analytical tests are widely accepted techniques for detecting Alzheimer cases. However, early detection can help prevent damage to brain tissue and heal it with proper treatment. Interpreting brain images is considered

The tracking-by-detection strategy is the backbone of many methods for tracking living cells in time-lapse microscopy. An object detector is first applied to the input images, and the resulting detection candidates are then linked by a data association module. The performance of such methods strongly depends on the quality of the detector because detection errors propagate to the linking step. To tackle

This paper addresses the problem of appearance matching across different challenges while doing visual face tracking in real-world scenarios. In this paper, FaceTrack is proposed that utilizes multiple appearance models with its long-term and short-term appearance memory for efficient face tracking. It demonstrates robustness to deformation, in-plane and out-of-plane rotation, scale, distractors and

The face super-resolution method is used for generating high-resolution images from low-resolution ones for better visualization. The super-resolution generative adversarial network (SRGAN) can generate a single super-resolution image with realistic textures, which is a groundbreaking work. Based on SRGAN, we proposed improved face super-resolution generative adversarial networks. The super-resolution

In recent decades, huge volumes of data are available to inspect human brain activities for disease detection. Specifically, the functional magnetic resonance imaging (fMRI) is a powerful tool to enquire the brain functions. In fMRI, identifying the active patterns of the specific cognitive state is one of the emerging concerns for neuroscientists. The high-dimensional features make fMRI data difficult

MR brain image categorization has been an active research domain from the last decade. Several techniques have been devised in the past for MR image categorization, starting from classical to the deep learning methods like convolutional neural networks (CNNs). Classical machine learning methods need handcrafted features to perform classification. The CNNs, on the other hand, perform classification

In the field of face recognition, a key issue is whether there are a sufficient number of face training samples with valid information. Due to the complexity of human face images, face recognition is easy to be affected by the external environment such as light intensity, gesture expression, hairstyle, and occlusion. Therefore, it is difficult to obtain enough effective samples in practical applications

This preface acts as an introduction to the special issue on Advanced Machine Vision. It highlights the goal of this special issue on Advanced Machine Vision as well as discusses the reviewing process. On top of that, it highlights the selected submissions and describes them briefly.

Cervical cancer is one among the trivial forms of cancer that counts for 6.6% of all females cancers with an estimated 570,000 new cases in 2018. The mortality rate due to cervical cancer is approximately 90% in low or middle income countries due to lack of suitable pre-screening procedures and experienced medical staff. Colposcopy images or cervigrams, are the images that capture the cervical region

Given an image sequence featuring a portion of a sports field filmed by a moving and uncalibrated camera, such as the one of the smartphones, our goal is to compute automatically in real time the focal length and extrinsic camera parameters for each image in the sequence without using a priori knowledges of the position and orientation of the camera.To this end, we propose a novel framework that combines

Salient object detection is the process of locating prominent objects in an image. In this field, deep learning methods are providing outstanding results. One way of finding salient objects is to first obtain a bounding box for the prominent object in the image and then use the bounding box to form the actual shape of the salient object. In this work, we find an object bounding box using YOLOv2 network

On specified scenarios, models trained on specific datasets (source domain) can generalize well to novel scenes (target domain) via knowledge transfer. However, these source detectors might not be perfectly aligned with a low target resource due to the imbalanced and inconsistent domain shift involved. In this paper, we propose a semi-supervised detector that adapts the domain shifts on both appearance

The articles listed below were published in Issue January 2020, Issue 1, instead of Issue February 2020, Issues 1–2.

Unfortunately, the co-author name was incorrectly spelled.

Reed–Xiaoli detector (RXD) is recognized as the benchmark algorithm for image anomaly detection; however, it presents known limitations, namely the dependence over the image following a multivariate Gaussian model, the estimation and inversion of a high-dimensional covariance matrix, and the inability to effectively include spatial awareness in its evaluation. In this work, a novel graph-based solution

Image registration is a viable task in the field of computer vision with many applications. When images are captured under different spectrum conditions, a challenge is imposed on the task of registration. Researchers carefully handcraft a local module insensitive to illumination changes across cross-spectral image pairs to tackle this challenge. We, in this paper, develop an optimized feature-based

The efficiency in breast cancer from imaging-based computer-aided diagnosis (CAD) has been revealed in recent years. As a fact, the methods grounded on a single modality constantly lack behind multimodal CAD imaging. However, owing to the restrictions of imaging devices, expressly in rural hospitals, single-modal imaging becomes a favorite in clinical practice for diagnosis. A fresh learning model

The establishment of image correspondence through robust image registration is critical to many clinical tasks such as image fusion, organ atlas creation, and tumor growth monitoring and is a very challenging problem. Since the beginning of the recent deep learning renaissance, the medical imaging research community has developed deep learning-based approaches and achieved the state-of-the-art in many

We propose a full processing pipeline to acquire anthropometric measurements from 3D measurements. The first stage of our pipeline is a commercial point cloud scanner. In the second stage, a pre-defined body model is fitted to the captured point cloud. We have generated one male and one female model from the SMPL library. The fitting process is based on non-rigid iterative closest point algorithm that

Whenever a patient needs to enter the operating room, in case the surgery requires general anesthesia, he/she must be intubated, and an anesthesiologist has to make a previous check to the patient in order to evaluate his/her airway. This process should be done to the patient to anticipate any problem, such as a difficult airway at the time of being anesthetized. In fact, the inadequate detection of

In this work, we explore the problems of recommending and visualising makeup products based on images of customers. Focusing on mascara, we propose a two-stage approach that first recommends products to a new customer based on the preferences of other customers with similar visual appearance and then visualises how the recommended products might look on the customer. For the initial product recommendation

Plant image analysis plays an important role in agriculture. It is used to record the morphological plant traits regularly and accurately. The plant growth is one of the key traits to be analyzed, which relies on leaf area (i.e., leaf region or plant region) and leaf count. One of the ways to find the leaf count is counting the leaves using segmented plant region. In this paper, a new plant region

Cellular processes are governed by macromolecular complexes inside the cell. Study of the native structures of macromolecular complexes has been extremely difficult due to lack of data. With recent breakthroughs in Cellular Electron Cryo-Tomography (CECT) 3D imaging technology, it is now possible for researchers to gain accesses to fully study and understand the macro-molecular structures single cells

Visual object tracking is a key factor for unmanned aerial vehicles (UAVs). In this paper, we propose a robust and effective visual object tracking method with an appearance model based on the locally adaptive regression kernel (LARK). The proposed appearance model encodes the geometric structure of the target. The tracking problem is formulated as two binary classifiers via two support vector machines

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging

Characterizing the dignity of breast lesions as benign or malignant is specifically difficult for small lesions; they don't exhibit typical characteristics of malignancy and are harder to segment since margins are harder to visualize. Previous attempts at using dynamic or morphologic criteria to classify small lesions (mean lesion diameter of about 1 cm) have not yielded satisfactory results. The goal

Independent travel is a well known challenge for blind and visually impaired persons. In this paper, we propose a proof-of-concept computer vision-based wayfinding aid for blind people to independently access unfamiliar indoor environments. In order to find different rooms (e.g. an office, a lab, or a bathroom) and other building amenities (e.g. an exit or an elevator), we incorporate object detection

In this work we explored class separability in feature spaces built on extended representations of pixel planes (EPP) produced using scale pyramid, subband pyramid, and image transforms. The image transforms included Chebyshev, Fourier, wavelets, gradient and Laplacian; we also utilized transform combinations, including Fourier, Chebyshev and wavelets of the gradient transform, as well as Fourier of