Dimitris Diamantis

Dimitris Diamantis
Technological Educational Institute of Lamia | TEILAM

MSc, PhD

About

18
Publications
2,346
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177
Citations

Publications

Publications (18)
Chapter
The assistive navigation of visually impaired individuals requires the development of different algorithms for obstacle detection, recognition, avoidance, and path planning. The assessment and optimization of such algorithms in the real world is a painstaking process that requires repetitive measurements under stable conditions, which is usually di...
Preprint
Full-text available
Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of the CNNs has been highlighted in several previous works, and a...
Article
Full-text available
Machine Learning (ML) applications are growing in an unprecedented scale. The development of easy-to-use machine-learning application frameworks has enabled the development of advanced artificial intelligence (AI) applications with only a few lines of self-explanatory code. As a result, ML-based AI is becoming approachable by mainstream developers...
Article
Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarc...
Article
Full-text available
Bone metastasis is among the most frequent in diseases to patients suffering from metastatic cancer, such as breast or prostate cancer. A popular diagnostic method is bone scintigraphy where the whole body of the patient is scanned. However, hot spots that are presented in the scanned image can be misleading, making the accurate and reliable diagno...
Article
Convolutional Neural Networks (CNNs) are artificial learning systems typically based on two operations: convolution, which implements feature extraction through filtering, and pooling, which implements dimensionality reduction. The impact of pooling in the classification performance of the CNNs has been highlighted in several previous works, and a...
Article
Full-text available
Several studies have addressed the problem of abnormality detection in medical images using computer-based systems. The impact of such systems in clinical practice and in the society can be high, considering that they can contribute to the reduction of medical errors and the associated adverse events. Today, most of these systems are based on binar...
Article
Full-text available
Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid...
Article
Neural network‐based solutions are under development to alleviate physicians from the tedious task of small‐bowel capsule endoscopy reviewing. Computer‐assisted detection is a critical step, aiming to reduce reading times while maintaining accuracy. Weakly supervised solutions have shown promising results; however, video‐level evaluations are scarc...
Chapter
Visual impairment restricts everyday mobility and limits the accessibility of places, which for the non-visually impaired is taken for granted. A short walk to a close destination, such as a market or a school becomes an everyday challenge. In this chapter, we present a novel solution to this problem that can evolve into an everyday visual aid for...
Chapter
Staircase detection in natural images has several applications in the context of robotics and visually impaired navigation. Previous works are mainly based on handcrafted feature extraction and supervised learning using fully annotated images. In this work we address the problem of staircase detection in weakly labeled natural images, using a novel...
Article
In this paper, we propose a novel Fully Convolutional Neural Network (FCN) architecture aiming to aid the detection of abnormalities, such as polyps, ulcers and blood, in gastrointestinal (GI) endoscopy images. The proposed architecture, named Look-Behind FCN (LB-FCN), is capable of extracting multi-scale image features by using blocks of parallel...
Article
Full-text available
The increasing use of digital video everyday in a multitude of electronic devices, including mobile phones, tablets and laptops, poses the need for quick development of cross-platform video software. However current approaches to this direction usually require a long learning curve, and their development lacks standardization. This results in softw...
Conference Paper
Full-text available
Wireless capsule endoscopy (WCE) is a minimally invasive procedure enabling visual examination of the entire gastrointestinal tract. It is performed by a swallow able camera-equipped capsule wirelessly transmitting color video frames. Reading the resulting video is a challenging process for theendoscopists. It requires a considerable amount of time...
Conference Paper
Full-text available
Wireless capsule endoscopy (WCE) is performed by a swallowable pill capsule equipped with a camera wirelessly transmitting color video frames to an external receiver. The resulting video consists usually of several thousands of frames and its visual examination requires hours of endoscopists' undivided attention. In this paper we propose a novel vi...
Conference Paper
Full-text available
Computational analysis of wireless capsule endoscopy (WCE) videos has already proved its potentials in the discovery or characterization of lesions and in the reduction of the time required by the endoscopists to perform the examination. An open problem that has only partially been addressed is the localization of the capsule endoscope in the gastr...

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Projects

Project (1)
Project
The aim of ENORASI project is the development of a novel wearable system offering accessibility and guidance to open environments of high cultural and tourist value, while it provides a unique user experience, including users with limited sight or total blindness. The system will enable the user’s localisation in an area of interest, recognition of his/her environment, objects of cultural / tourist interest, obstacles or dangerous situations around. It provides directions not only for navigation but also for personalised guided touring. The proposed system integrates computer vision and voice-based interaction within a cloud information processing and sharing environment.