Style transformation on face images has traditionally been a popular research area in the field of computer vision, and its applications are quite extensive. Currently, the more mainstream schemes include Generative A...
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Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional...
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ISBN:
(纸本)9789819785070;9789819785087
Artificial Intelligence Generated Content (AIGC) has experienced significant advancements, particularly in the areas of natural language processing and 2D image generation. However, the generation of three-dimensional (3D) content from a single image still poses challenges, particularly when the input image contains complex backgrounds. This limitation hinders the potential applications of AIGC in areas such as human-machine interaction, virtual reality (VR), and architectural design. Despite the progress made so far, existing methods face difficulties when dealing with single images that have intricate backgrounds. Their reconstructed 3D shapes tend to be incomplete, noisy, or lack of partial geometric structures. In this paper, we introduce a 3D generation framework for indoor scenes from a single image to generate realistic and visually-pleasing 3D geometry shapes, without the requirement of point clouds, multi-view images, depth or masks as input. The main idea of our method is clustering-based 3D shape learning and prediction, followed by a shape deformation. Since more than one objects tend to be existing in indoor scenes, our framework will simultaneously generate multi-objects and predict the layout with a camera pose, as well as 3D object bounding boxes for holistic 3D scene understanding. We have evaluated the proposed framework on benchmark datasets including ShapeNet, SUN RGB-D and Pix3D, and state-of-the-art performance has been achieved. We have also given examples to illustrate immediate applications in virtual reality.
With the advancements of the Internet of Things (IoT) technology, applications of battery powered machinevision based IoT devices is rapidly growing. While numerous research works are being conducted to develop low p...
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With the advancements of the Internet of Things (IoT) technology, applications of battery powered machinevision based IoT devices is rapidly growing. While numerous research works are being conducted to develop low power hardware solutions for IoT devices, image capture and imageprocessing remain high power demanding processes leading to a short battery life. However, the power consumption of the machinevision based IoT devices can be minimized by the careful optimization of the hardware components that are used is these devices. In this article, we present a review of low power machinevision hardware components for the IoT applications. A guide to selecting the optimum processors and image sensors for a given battery powered machinevision based IoT device is presented. Next, the factors that must be considered when selecting processors and image sensors for a given IoT application are discussed, and selection criteria for the processors and image sensors are established. Then, the current commercially available hardware components are reviewed in accordance with the established selection criteria. Finally, the research trends in the field of battery powered machinevision based IoT devices are discussed, and the potential future research directions in the field are presented.
In the realm of deep learning, the traditional approach has been to train specialized models for individual tasks, which, although effective, is resource-intensive. The advent of large, universal models has mitigated ...
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image-to-image translation has long been recognized as a crucial undertaking within the field of computer vision, owing to its broad applications in domain adaption. Existing methods differ in Generative Adversarial N...
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The automatic description of the content of an image is a challenge that combines computer vision with natural language processing in the field of artificial intelligence. Most existing models have limitations in accu...
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This paper introduces the image diagnosis technology of power inspection based on computer vision. image import, database access, text output and other functional modules are designed using VisualStudio2010. ADO techn...
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This work evaluates the use of a programmable logic controller (PLC) from Phoenix Contact's PLCnext ecosystem as an imageprocessing platform. PLCnext controllers provide the functions of "classical" ind...
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This work evaluates the use of a programmable logic controller (PLC) from Phoenix Contact's PLCnext ecosystem as an imageprocessing platform. PLCnext controllers provide the functions of "classical" industrial controllers, but they are based on the Linux operating system, also allowing for the use of software tools usually associated with computers. Visual processingapplications in the Python programming language using the OpenCV library are implemented in the PLC using this feature. This research is focused on evaluating the use of this PLC as an imageprocessing platform, particularly for industrial machinevisionapplications. The methodology is based on comparing the PLC's performance against a computer using standard imageprocessing algorithms. In addition, a demonstration application based on a real-world scenario for quality control by visual inspection is presented. It is concluded that despite significant limitations in processing power, the simultaneous use of the PLC as an industrial controller and imageprocessing platform is feasible for applications of low complexity and undemanding cycle times, providing valuable insights and benchmarks for the scientific community interested in the convergence of industrial automation and computer vision technologies.
The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with...
ISBN:
(纸本)9789819780426
The proceedings contain 128 papers. The special focus in this conference is on Data Science, machine Learning and applications. The topics include: Digitization of Monuments – An Impact on the Tourist Experience with Special Reference to Hampi;resume Parser Using machine Learning;IOT Based Smart Hydroponics System;comparative Study of machine Learning and Deep Learning Techniques for Cancer Disease Detection;High Thruput Modulation Approaches Used in Next Generation WiF’s Under Multi-impairments Environments with MATLAB Codes;skin Disease Detection;root Vegetable Crop Recommendation System Based on Soil Properties and Environmental Factors;deep Learning Model Development for an Automatic Healthcare Edge Computing Application;Empathetic Conversations in Mental Health: Fine-Tuning LLMs for Supportive AI Interactions;exploring Block Chain Technology with applications, and Future Prospects;a Comprehensive Review of Soft Computing Enabled Techniques for IoT Security: State-of-the-Art and Challenges Ahead;Performance Analysis of machine Learning Algorithms on Imbalanced Datasets Using SMOTE Technique;An AI Based Nutrient Tracking and Analysis System;power Saving Mechanism for Street Lights System Using IoT;Automatic Login System Using ATTINY85 IC;forecasting Stock Prices: A Comparative Analysis of machine Learning, Deep Learning, and Statistical Approaches;smart vision Bot;robots in Logistics: Apprehension of Current Status and Future Trends in Indian Warehouses;smart Healthcare: Enhancing Patient Well-Being with IoT;Detection of B-ALL Using CNN Model and Deep Learning;a Comprehensive Analysis for Advancements and Challenges in Deep Learning Models for imageprocessing;a Comprehensive Survey on Enhancing Patient Care Through Deep Learning and IoT-Enabled Healthcare Innovations;attention-Based image Caption Generation.
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