相关论文: Two-view 3D Reconstruction for Food Volume Estimat…
Understanding 3D medical image volumes is a critical task in the medical domain. However, existing 3D convolution and transformer-based methods have limited semantic understanding of an image volume and also need a large set of volumes for…
Food image classification is a fundamental step of image-based dietary assessment, enabling automated nutrient analysis from food images. Many current methods employ deep neural networks to train on generic food image datasets that do not…
This study presents the first report on the development of an artificial intelligence (AI) for automatic region segmentation of four-dimensional computer tomography (4D-CT) images during swallowing. The material consists of 4D-CT images…
Nutrition estimation is an important component of promoting healthy eating and mitigating diet-related health risks. Despite advances in tasks such as food classification and ingredient recognition, progress in nutrition estimation is…
Thin leaves, fine stems, self-occlusion, non-rigid and slowly changing structures make plants difficult for three-dimensional (3D) scanning and reconstruction -- two critical steps in automated visual phenotyping. Many current solutions…
Recent advances in artificial intelligence promote a wide range of computer vision applications in many different domains. Digital cameras, acting as human eyes, can perceive fundamental object properties, such as shapes and colors, and can…
Food crystal agglomeration is a phenomenon occurs during crystallization which traps water between crystals and affects food product quality. Manual annotation of agglomeration in 2D microscopic images is particularly difficult due to the…
Fish products account for about 16 percent of the human diet worldwide, as of 2017. The counting action is a significant component in growing and producing these products. Growers must count the fish accurately, to do so technological…
Computer-aided diagnosis for low-dose computed tomography (CT) based on deep learning has recently attracted attention as a first-line automatic testing tool because of its high accuracy and low radiation exposure. However, existing methods…
Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two…
The present study presents a simple but effective method for 3D visualization of the two-phase flow in the micro-tube. An isosceles right-angle prism combined with a mirror located 45^o bevel to the prism is employed to obtain synchronously…
Accurate food intake detection is vital for dietary monitoring and chronic disease prevention. Traditional self-report methods are prone to recall bias, while camera-based approaches raise concerns about privacy. Furthermore, existing…
We propose a novel point cloud based 3D organ segmentation pipeline utilizing deep Q-learning. In order to preserve shape properties, the learning process is guided using a statistical shape model. The trained agent directly predicts…
Two-dimensional (2D) freehand ultrasonography is one of the most commonly used medical imaging modalities, particularly in obstetrics and gynaecology. However, it only captures 2D cross-sectional views of inherently 3D anatomies, losing…
Endoscopic diagnosis is an important means for gastric polyp detection. In this paper, a panoramic image of gastroscopy is developed, which can display the inner surface of the stomach intuitively and comprehensively. Moreover, the proposed…
The advancement of artificial intelligence (AI) in food and nutrition research is hindered by a critical bottleneck: the lack of annotated food data. Despite the rise of highly efficient AI models designed for tasks such as food…
Nutrition estimation of meals from visual data is an important problem for dietary monitoring and computational health, but existing approaches largely rely on single images of the finally completed dish. This setting is fundamentally…
A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry…
Robotic systems in manufacturing applications commonly assume known object geometry and appearance. This simplifies the task for the 3D perception algorithms and allows the manipulation to be more deterministic. However, those approaches…
In this paper, we adopt 3D Convolutional Neural Networks to segment volumetric medical images. Although deep neural networks have been proven to be very effective on many 2D vision tasks, it is still challenging to apply them to 3D tasks…