Nevrez imamoğlu

• Doctor of Philosophy
• Researcher at National Institute of Advanced Industrial Science and Technology, Tokyo

Recently, the number of studies focusing on 3D anatomically-based models have been increasing rapidly. The purpose of the models is to understand the underlying principles behind the biomechanical dynamics of the musculoskeletal system that can support many research fields to increase the quality of life. 3D models are constructed using 2D medical...

Studies on the Human Visual System (HVS) have demonstrated that human eyes are more attentive to spatial or spectral components with irregularities on the scene. This fact was modeled differently in many computational methods such as saliency residual (SR) approach, which tries to find the irregularity of frequency components by subtracting average...

Researchers have been taking advantage of visual attention in various image processing applications such as image retargeting, video coding, etc. Recently, many saliency detection algorithms have been proposed by extracting features in spatial or transform domains. In this paper, a novel saliency detection model is introduced by utilizing low-level...

Foundation model approaches such as masked auto-encoders (MAE) or its variations are now being successfully applied to satellite imagery. Most of the ongoing technical validation of foundation models have been applied to optical images like RGB or multi-spectral images. Due to difficulty in semantic labeling to create datasets and higher noise cont...

LiDAR odometry estimation and 3D semantic segmentation are crucial for autonomous driving, which has achieved remarkable advances recently. However, these tasks are challenging due to the imbalance of points in different semantic categories for 3D semantic segmentation and the influence of dynamic objects for LiDAR odometry estimation, which increa...

LiDAR odometry estimation and 3D semantic segmentation are crucial for autonomous driving, which has achieved remarkable advances recently. However, these tasks are challenging due to the imbalance of points in different semantic categories for 3D semantic segmentation and the influence of dynamic objects for LiDAR odometry estimation, which increa...

The growing interest in omnidirectional videos (ODVs) that capture the full field-of-view (FOV) has gained 360-degree saliency prediction importance in computer vision. However, predicting where humans look in 360-degree scenes presents unique challenges, including spherical distortion, high resolution, and limited labelled data. We propose a novel...

Omnidirectional images, aka 360 images, can deliver immersive and interactive visual experiences. As their popularity has increased dramatically in recent years, evaluating the quality of 360 images has become a problem of interest since it provides insights for capturing, transmitting, and consuming this new media. However, directly adapting quali...

Electrical properties (EPs) of tissues facilitate early detection of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is a technique to non-invasively probe the EPs of tissues from MRI measurements. Most MREPT methods rely on numerical differentiation (ND) to solve partial differential Equations (PDEs) to reconstruct t...

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based o...

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic relationships and potential benefits of feedback connections in vision tasks such as saliency detection, despite their significant representation capabilities. In this work, we propose a feedback-recursive convolutional framework (SalFBNet) for saliency detection. The propo...

Utilizing multi-level features has been proven to improve RGB-D scene recognition performance. However, simply fusing features after conducting RGB and depth data separately may not satisfy multi-modal integrity. In this work, we propose an effective multi-modal RGB-D scene recognition model that integrates global or local multi-scale/multi-semanti...

Deep learning models as an emerging topic have shown great progress in various fields. Especially, visualization tools such as class activation mapping methods provided visual explanation on the reasoning of convolutional neural networks (CNNs). By using the gradients of the network layers, it is possible to demonstrate where the networks pay atten...

Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), have become widespread and have...

Feed-forward only convolutional neural networks (CNNs) may ignore intrinsic relationships and potential benefits of feedback connections in vision tasks such as saliency detection, despite their significant representation capabilities. In this work, we propose a feedback-recursive convolutional framework (SalFBNet) for saliency detection. The propo...

The electrical property (EP) of human tissues is a quantitative biomarker that facilitates early diagnosis of cancerous tissues. Magnetic resonance electrical properties tomography (MREPT) is an imaging modality that reconstructs EPs by the radio-frequency field in an MRI system. MREPT reconstructs EPs by solving analytic models numerically based o...

In this work, we propose and investigate a user-centric framework for the delivery of omnidirectional video (ODV) on VR systems by taking advantage of visual attention (saliency) models for bitrate allocation module. For this purpose, we formulate a new bitrate allocation algorithm that takes saliency map and nonlinear sphere-to-plane mapping into...

Chlorophyll content is one of the essential parameters to assess the growth process of the fruit trees. This present study developed a model for estimation of canopy averaged chlorophyll content (CACC) of pear trees using the convolutional auto-encoder (CAE) features of hyperspectral data. This study also demonstrated the inspection of anomaly amon...

Recognizing objects and scenes are two challenging but essential tasks in image understanding. In particular, the use of RGB-D sensors in handling these tasks has emerged as an important area of focus for better visual understanding. Meanwhile, deep neural networks, specifically convolutional neural networks (CNNs), have become widespread and have...

Image saliency detection has been widely explored in recent decades, but computational modeling of visual attention for video sequences is limited due to complicated temporal saliency extraction and fusion of spatial and temporal saliency. Inspired by Gestalt theory, we introduce a novel spatiotemporal saliency detection model in this study. First,...

Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects....

Land cover classification and investigation of temporal changes are considered to be common applications of remote sensing. Water/non-water region estimation is one of the most fundamental classification tasks, analyzing the occurrence of water on the Earth’s surface. However, common remote sensing practices such as thresholding, spectral analysis,...

Accurate attitude information from a satellite image sensor is essential for accurate map projection and reducing computational cost for post-processing of image registration, which enhance image usability, such as change detection. We propose a robust attitude-determination method for pushbroom sensors onboard spacecraft by matching land features...

Effective visual attention modeling is a key factor that helps enhance the overall Quality of Experience (QoE) of VR/AR data. Although a huge number of algorithms have been developed in recent years to detect salient regions in flat-2D images, the research on 360-degree image saliency is limited. In this study, we propose a superpixel-level salienc...

Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-based saliency) or contextual / task-dependent features (object based saliency). Saliency models generally incorporate salient cues either in bottom-up or top-down norm se...

Many works have been done on salient object detection using supervised or unsupervised approaches on colour images. Recently, a few studies demonstrated that efficient salient object detection can also be implemented by using spectral features in visible spectrum of hyperspectral images from natural scenes. However, these models on hyperspectral sa...

Bottom-up and top-down visual cues are two types of information that helps the visual saliency models. These salient cues can be from spatial distributions of the features (space-based saliency) or contextual/task-dependent features (object-based saliency). Saliency models generally incorporate salient cues either in bottom-up or top-down norm sepa...

The emergence of deep learning applications such as convolutional neural networks (CNNs) have resulted in huge improvements on computer vision applications in a wide variety of fields. However, several works demonstrated that low-quality or noisy data (even including perceptually not visible noises) may have a huge impact on the accuracy of CNN mod...

Most of the traditional convolutional neural networks (CNNs) implements bottom-up approach (feedforward) for image classifications. However, many scientific studies demonstrate that visual perception in primates rely on both bottom-up and top-down connections. Therefore, in this work, we propose a CNN network with feedback structure for Solar power...

As prior knowledge of objects or object features helps us make relations for similar objects on attentional tasks, pre-trained deep convolutional neural networks (CNNs) can be used to detect salient objects on images regardless of the object class is in the network knowledge or not. In this paper, we propose a top-down saliency model using CNN, a w...

It has been shown that mobile robots could be a potential solution to home bio-monitoring for the elderly. Through our previous studies, a mobile robot system that is able to recognize daily living activities of a target person has been developed. However, in a home environment, there are several factors of uncertainty, such as confusion with surro...

Small satellites have limited payload and their attitudes are sometimes difficult to determine from the limited onboard sensors alone. Wrong attitudes lead to inaccurate map projections and measurements that require post-processing correction. In this study, we propose an automated and robust scheme that derives the satellite attitude from its obse...

Increasing population age demands more services in healthcare domain. It has been shown that mobile robots could be a potential solution to home biomonitoring for the elderly. Through our previous studies, a mobile robot system that is able to track a subject and identify his daily living activities has been developed. However, the system has not b...

As the first and important step of modeling to explore biomechanical dynamics of the human body, the regions of interest, such as muscles, bones, nerves, and etc., should be extracted from slices of MRI or CT data. Fast and automatic region segmentation would speed up an online process of building physically based models. In this work, a new automa...

Previous studies demonstrated that bimanual coordination can assist to rehabilitation program for patients with hemiplegia by improving their motor functions. Moreover, in addition to the rehabilitation assistance, bimanual coordination can also be used for prosthesis users to improve the usability of prosthesis. Intention detection and motion cont...

Saliency maps as visual attention computational models can reveal novel regions within a scene (as in the human visual system), which can decrease the amount of data to be processed in task specific computer vision applications. Most of the saliency computation models do not take advantage of prior spatial memory by giving priority to spatial or ob...

Our research is focused on the development of an at-home health care biomonitoring mobile robot for the people in demand. Main task of the robot is to detect and track a designated subject while recognizing his/her activity for analysis and to provide warning in an emergency. In order to push forward the system towards its real application, in this...

The aging population and the escalation of healthcare costs are leading concerns for many developed countries, such as Japan. The government has attempted to reduce hospital costs, which can easily be done by using home healthcare. In this study, we developed a non-contact vital sign monitoring system (Vital-CUBE) that uses microwave radar for ubiq...

Assistive robotics technologies have been growing impact on at-home monitoring services to support daily life. One of the main research fields is to develop an autonomous mobile robot with the tasks detection, tracking, observation and analysis of the subject of interest in the indoor environment. The main challenges in such daily monitoring applic...

Ultrasound imaging is an effective way to measure the muscle activity in electrical stimulation studies. However, it is a time consuming task to manually measure pennation angle and muscle thickness, which are the benchmark features to analyze muscle activity from the ultrasound imaging. In previous studies, the muscle features were measured by cal...

An aerial surveillance method is proposed for a predefined single object tracking. The algorithm takes advantage of template matching to be able to track the selected object in video sequences. The selected templates are chosen from the images by utilizing gradient operation on the Gabor Wavelet representations. The algorithm achieves the tracking...

Our previous studies demonstrated that the idea of bio-monitoring home healthcare mobile robots is feasible. Therefore, by developing algorithms for mobile robot based tracking, measuring, and activity recognition of human subjects, we would be able to help impaired people (MIPs) to spend more time focusing in their motor function rehabilitation pr...

Our ultimate goal is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients’ motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. In our previous study, we developed basic algorithms for tracking, measuring, and behavio...

This research work is focused on development of autonomous bio-monitoring mobile robot for Motor function Impaired Persons (MIPs). The developed robot is capable of tracking MIPs from suitable viewpoints in indoor environment, recognizing their behavior based on the collected data. In our previous research work, a full framework was established tow...

This research work is aimed to develop autonomous bio-monitoring mobile robots, which are capable of tracking and measuring patients' motions, recognizing the patients' behavior based on observation data, and providing calling for medical personnel in emergency situations in home environment. The robots to be developed will bring about cost-effecti...

Purpose The aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients’ motions, recognizing the patients’ behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost‐effective, safe an...

Our research is focused on the home healthcare support system for motor function impaired persons (MIPs) whose motor function should be closely monitored during either in-hospital or at-home training therapy process. Especially, for the at-home monitoring, the demand of which is increasing, not only close observation, but also accurate behavior rec...

Our ultimate goal is to develop autonomous mobile home healthcare robots which closely monitor and evaluate the patients’ motor function, and their at-home training therapy process, providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe and easier at-home rehab...

The goal of our research is to develop an autonomous at home health care mobile robot for motor-function impaired patients (MIPs). This paper focuses on one of the key component, human gait behaviour classification, based on the side view observation. The purpose is to identify gestures such as walking, impaired walking, standing, and sitting where...

Purpose – The aim of this paper is to develop autonomous mobile home healthcare robots, which are capable of observing patients' motions, recognizing the patients' behaviours based on observation data, and providing automatically calling for medical personnel in emergency situations. The robots to be developed will bring about cost-effective, safe...

In this paper, vision-based autonomous flight with a quadrotor type unmanned aerial vehicle (UAV) is presented. Automatic detection of obstacles and junctions are achieved by the use of optical flow velocities. Variation in the optical flow is used to determine the reference yaw angle. Path to be followed is generated autonomously and the path foll...

Visual attention models generate saliency maps in which attentive regions are more distinctive with respect to remaining parts of the scene. In this work, a new model of orientation conspicuity map (OCM) is presented for the computation of saliency. The proposed method is based on the difference of the Gabor filter outputs with orthogonal orientati...

Feature extraction techniques play a vital part in pattern recognition applications. In order to achieve the best performance in a particular classification problem, the most appropriate feature extractor for the problem is pursued. In this paper, a Pseudo-Zernike Moments based model is used as the feature extractor due to its reliability in illumi...

One of the common research topics for surveillance systems is tracking of a target object. Motions of both observer and target make the problem difficult to handle. In this paper, proposed algorithm tracks a target object on consecutive frames taken from a camera embedded on an unmanned aerial vehicle. Adaptive-fuzzy weighted sum of square distance...