An intelligent analysis software for volleyball match is developed by using Intel open CV software and MATLAB software. Then, a method of extracting volleyball motion trajectory using image and video frame is proposed...
详细信息
The proceedings contain 54 papers. The topics discussed include: a machine learning based platform for remote management of heart failure patients;optical flow with semantic guidance and uncertainty estimation for rob...
ISBN:
(纸本)9798350370355
The proceedings contain 54 papers. The topics discussed include: a machine learning based platform for remote management of heart failure patients;optical flow with semantic guidance and uncertainty estimation for robust video perception;using artificial intelligence to fight clickbait in Romanian news articles;symbolic analysis based pipeline for EEG data;MITRE tactics inference from Splunk queries;Im2Vide0: a zero-shot approach using diffusion models for natural language conditioned image-to-video;extracting appliance behavior from heterogeneous data;and modeling the deformation behavior of wind turbine blades using artificial neural networks.
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneousl...
ISBN:
(纸本)9798350307184
In this work, we investigate the problem of creating high-fidelity 3D content from only a single image. This is inherently challenging: it essentially involves estimating the underlying 3D geometry while simultaneously hallucinating unseen textures. To address this challenge, we leverage prior knowledge from a well-trained 2D diffusion model to act as 3D-aware supervision for 3D creation. Our approach, Make-It-3D, employs a two-stage optimization pipeline: the first stage optimizes a neural radiance field by incorporating constraints from the reference image at the frontal view and diffusion prior at novel views;the second stage transforms the coarse model into textured point clouds and further elevates the realism with diffusion prior while leveraging the high-quality textures from the reference image. Extensive experiments demonstrate that our method outperforms prior works by a large margin, resulting in faithful reconstructions and impressive visual quality. Our method presents the first attempt to achieve high-quality 3D creation from a single image for general objects and enables various applications such as text-to-3D creation and texture editing.
The accuracy of image recognition algorithm is greatly reduced in practical applications. To solve this problem, an architecture combining semantic segmentation and image classification is proposed. U-Net network is u...
详细信息
The propagation of digital content and its ease of distribution over the internet have raised concerns regarding the protection of intellectual property and its prevention from unauthorized usage. One such effective m...
详细信息
This paper introduces an innovative and efficient multi-scale Vision Transformer (ViT) for the task of image classification. The proposed model leverages the inherent power of transformer architecture and combines it ...
详细信息
Edge detection is an useful tool utilized by various computer Vision applications. A prime application example for locating the spatial edges of an image is to then separate and identify the included objects. Similarl...
详细信息
The research of computer intelligent image recognition algorithm and technology is the research field of the development of computer intelligent image recognition algorithm. The main purpose of this research field is ...
详细信息
Any method that looks for samples that deviate from anticipated patterns is sometimes referred to as anomaly detection. Many outlier finding methods are established on the types of irregularities, various types of dat...
详细信息
Precisely knowing each instance's position and extents is a critical first step in many biological applications. State-of-the-art techniques rely either on deep learning models designed to predict segmentation mas...
详细信息
ISBN:
(纸本)9781728198354
Precisely knowing each instance's position and extents is a critical first step in many biological applications. State-of-the-art techniques rely either on deep learning models designed to predict segmentation masks on each Region of Interest (RoI) or on classic active contour methods. The former struggles to precisely delineating boundaries and tends to output masks at low resolutions when the cells/nuclei are very irregular while the latter often needs good initialization and manual setting of parameters, thus limiting their usefulness. To bridge this gap, we introduce Snake R-CNN, a new level of the learnable active contour model that predict boundary on each RoI in a sequent way. To do so, for each RoI, we reformulate the contour deformation task in terms of a hidden state evolution problem and update the evolution process using energy minimization. We learn snake parameterizations per instance in an end-to-end manner, and demonstrate its effectiveness for contour inferences of various cell/nucleus types where consistently higher performances were obtained for comparison against state-of-the-arts.
暂无评论