Currently, mainstream panoramic depth estimation methods primarily focus on correcting distortion effects. For Convolutional Neural Networks (CNN) based on standard convolution, it is difficult to fully perceive the c...
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imageprocessing technology plays an important role in the field of cultivation. Its applications are rapidly increasing in agriculture fields such as disease identification, prevention, and crop health monitoring. Ge...
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A novel VR intelligent guiding system with imageprocessing and programming methods is designed in this study. The fundamental goal of VR technology is to achieve realistic experiences and also natural technology-base...
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This paper analyzes the mathematical model constructed by the Net VLAD method, and proposes a second-order function based on the Net VLAD method to solve the problem that the obtained image feature encoding is a first...
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Agricultural diseases and pests pose a formidable menace to crop production and the safety of agricultural products. To address the pressing need for rapid and accurate identification and detection of these agricultur...
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In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introdu...
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ISBN:
(纸本)9798350312935
In recent years, the ViT model has been widely used in the field of computer vision, especially for image classification tasks. This paper summarizes the application of ViT in image classification tasks, first introduces the image classification implementation process and the basic architecture of the ViT model, then analyzes and summarizes the image classification methods, including traditional image classification methods, CNN -based image classification methods, and ViT-based image classification methods, and provides a comparative analysis of CNN and ViT. Subsequently, this paper outlines the application prospects of ViT in image classification and its future development and also outlines some shortcomings of ViT and its solutions.
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 recent years, accurate models for image segmentation have become larger and more complex. However, it is hard to apply them into embedded devices which usually have limited space for data, energy and computing. Mea...
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ISBN:
(纸本)9781728198354
In recent years, accurate models for image segmentation have become larger and more complex. However, it is hard to apply them into embedded devices which usually have limited space for data, energy and computing. Meanwhile, since many embedded devices do not support modifications of computing units, simple models with such requirements cannot be adapted to these devices. To achieve high accuracy for image segmentation in embedded devices, we propose a light-weight and efficient neural network, named Fibonet. Fibonet is constructed by cascading two Fiboblocks, with the Fibonacci structure which adjusts the combination of basic computing units through skip connections and feature reusing so that it is less computationally intensive and dataset-demanding. The experiments demonstrate that Fibonet can be embedded to the mobile terminal for real-time segmentation and can effectively balance accuracy and computing resources. Compared with Resnet18, Fibonet achieves 10.8% higher accuracy performance (mIoU: 0.533 vs. 0.481) with 84.4% fewer parameters (0.441M vs. 2.82M) using similar model width and depth.
image edge detection is an important step in imageprocessing. The current edge detection methods have low quality in extracting weak edges, which affects the efficiency and quality of detection. Therefore, this paper...
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Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomica...
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ISBN:
(纸本)9798350319439
Ultrasonography (US) has demonstrated many advantages in the detection, characterization, and monitoring of different diseases. Through high frequency probes, it is possible to visualize and characterize the anatomical layers, such as tissues, which can constitute a very helpful supporting tool in several procedures, e.g., surgeries. However, the visual identification of tissues in this type of image is still a challenge for some professionals. In this work, we evaluate deep learning (DL) segmentation algorithms for the tissue segmentation task in US. Moreover, we briefly assess whether their performance can be improved by including a crowdsourcing step. In order to perform the segmentation task, different segmentation models are trained, as posteriorly, the crowdsourcing step is included. The proposed approach is composed of the following steps: 1- automatic segmentation using a deep learning algorithm;2 - crowd evaluation and correction of the results. In order to perform step 1, different segmentation models are trained. The second step includes a visual interface where users can: a) validate the quality of the automatic segmentation;or b) correct the segmentation whether the DL result is inconsistent. All users are scored, denoting the quality of their annotations, considering the manual annotations provided by an expert for a small group of images. In order to evaluate the method and compare it to a DL algorithms alone, a total of 100 US images were used. Our experiments show that the inclusion of crowdsourcing significantly improved the performance of the tissue segmentation task compared to using the DL models alone. The performance of our method demonstrated the feasibility of applying this type of solution for the considered problem of segmenting tissues in US facial images. Moreover, the results suggest that this tool can be employed as an auxiliary tool in oral procedures.
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