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This work is about the semantic segmentation of skin lesion boundary and their attributes using Image-to-Image Translation with Conditional Adversarial Nets. Melanoma is a type of skin cancer that can be cured if detected in time.…
Sketch-based image editing aims to synthesize and modify photos based on the structural information provided by the human-drawn sketches. Since sketches are difficult to collect, previous methods mainly use edge maps instead of sketches to…
In this work, a real-time hand gesture recognition system-based human-computer interface (HCI) is presented. The system consists of six stages: (1) hand detection, (2) gesture segmentation, (3) use of five pre-trained convolutional neural…
AI algorithms have become valuable in aiding professionals in healthcare. The increasing confidence obtained by these models is helpful in critical decision demands. In clinical dermatology, classification models can detect malignant…
In this work we present a method of automatic segmentation of defective skulls for custom cranial implant design and 3D printing purposes. Since such tissue models are usually required in patient cases with complex anatomical defects and…
Recent 3D-based manipulation methods either directly predict the grasp pose using 3D neural networks, or solve the grasp pose using similar objects retrieved from shape databases. However, the former faces generalizability challenges when…
This paper proposes an automatic method for scapula bone segmentation from Magnetic Resonance (MR) images using deep learning. The purpose of this work is to incorporate anatomical priors into a conditional adversarial framework, given a…
We present 2 statistical models of the skull and mandible built upon an elastic registration method of 3D meshes. The aim of this work is to relate degrees of freedom of skull anatomy, as static relations are of main interest for…
3D hand pose estimation has garnered great attention in recent years due to its critical applications in human-computer interaction, virtual reality, and related fields. The accurate estimation of hand joints is essential for high-quality…
In this paper, we investigate hand gesture classifiers that rely upon the abstracted 'skeletal' data recorded using the RGB-Depth sensor. We focus on 'skeletal' data represented by the body joint coordinates, from the Praxis dataset. The…
Plain X-ray is one of the most common image modalities for clinical diagnosis (e.g. bone fracture, pneumonia, cancer screening, etc.). X-ray image segmentation is an essential step for many computer-aided diagnostic systems, yet it remains…
Recent advances have enabled 3d object reconstruction approaches using a single off-the-shelf RGB-D camera. Although these approaches are successful for a wide range of object classes, they rely on stable and distinctive geometric or…
Existing RGB-based 2D hand pose estimation methods learn the joint locations from a single resolution, which is not suitable for different hand sizes. To tackle this problem, we propose a new deep learning-based framework that consists of…
In medical image analysis, constructing an atlas, i.e. a mean representative of an ensemble of images, is a critical task for practitioners to estimate variability of shapes inside a population, and to characterise and understand how…
Current anthropomorphic robotic hands mainly focus on improving their dexterity by devising new mechanical structures and actuation systems. However, most of them rely on a single structure/system (e.g., bone-only) and ignore the fact that…
With the advancement in computing and robotics, it is necessary to develop fluent and intuitive methods for interacting with digital systems, augmented/virtual reality (AR/VR) interfaces, and physical robotic systems. Hand motion…
Objectives: Computerized phantoms play an essential role in various applications of medical imaging research. Although the existing computerized phantoms can model anatomical variations through organ and phantom scaling, this does not…
Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the…
High-quality 3D reconstruction of pulmonary segments plays a crucial role in segmentectomy and surgical planning for the treatment of lung cancer. Due to the resolution requirement of the target reconstruction, conventional deep…
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for…