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Technology

Image Processing at IgMin Research | Technology Group

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Illuminating the Language of Visual Data

In an era dominated by visual information, Image Processing stands as the keystone to unlocking the power of images. This segment of IgMin Research delves into the intricate landscape of algorithms that breathe life into raw visual data, enabling us to enhance, analyze, and comprehend the visual world around us. From medical imaging to computer vision, we navigate the realm where pixels reveal stories.

  • Image Enhancement and Restoration
  • Image Filtering and Transformations
  • Digital Image Compression
  • Image Segmentation and Object Recognition
  • Computer Vision and Pattern Recognition
  • Medical Image Analysis
  • Remote Sensing Image Processing
  • Machine Learning in Image Processing
  • Image Fusion and Multi-modal Analysis
  • Biometric Image Processing
  • Geospatial Image Analysis
  • Image-Based Rendering
  • Motion Analysis and Tracking
  • 3D Image Processing
  • Image Quality Assessment
  • Image Encryption and Security
  • Image Processing in Art and Media
  • Image-Based Machine Learning
  • Image Analysis in Agriculture
  • Image Processing in Autonomous Systems
  • Industrial Image Analysis
  • Image Processing in Virtual Reality
  • Case Studies in Innovative Image Processing
  • Emerging Trends in Visual Data Analysis
  • Image Processing for Healthcare

Technology Group (2)

Case Report Article ID: igmin167
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Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

System for Detecting Moving Objects Using 3D Li-DAR Technology
by Md. Milon Rana, Orora Tasnim Nisha, Md. Mahabub Hossain, Md Selim Hossain, Md Mehedi Hasan and Md Abdul Muttalib Moon

The “System for Detecting Moving Objects Using 3D LiDAR Technology” introduces a groundbreaking method for precisely identifying and tracking dynamic entities within a given environment. By harnessing the capabilities of advanced 3D LiDAR (Light Detection and Ranging) technology, the system constructs intricate three-dimensional maps of the surroundings using laser beams. Through continuous analysis of the evolving data, the system adeptly discerns and monitors the movement of objects within its designated area. What sets this syste...m apart is its innovative integration of a multi-sensor approach, combining LiDAR data with inputs from various other sensor modalities. This fusion of data not only enhances accuracy but also significantly boosts the system’s adaptability, ensuring robust performance even in challenging environmental conditions characterized by low visibility or erratic movement patterns. This pioneering approach fundamentally improves the precision and reliability of moving object detection, thereby offering a valuable solution for a diverse array of applications, including autonomous vehicles, surveillance, and robotics.

Robotics Image Processing
Mini Review Article ID: igmin125
Cite

Open Access Policy refers to a set of principles and guidelines aimed at providing unrestricted access to scholarly research and literature. It promotes the free availability and unrestricted use of research outputs, enabling researchers, students, and the general public to access, read, download, and distribute scholarly articles without financial or legal barriers. In this response, I will provide you with an overview of the history and latest resolutions related to Open Access Policy.

Deep Semantic Segmentation New Model of Natural and Medical Images
by Pei-Yu Chen, Chien-Chieh Huang and Yuan-Chen Liu

Semantic segmentation is the most significant deep learning technology. At present, automatic assisted driving (Autopilot) is widely used in real-time driving, but if there is a deviation in object detection in real vehicles, it can easily lead to misjudgment. Turning and even crashing can be quite dangerous. This paper seeks to propose a model for this problem to increase the accuracy of discrimination and improve security. It proposes a Convolutional Neural Network (CNN)+ Holistically-Nested Edge Detection (HED) combined with Spatial Pyr...amid Pooling (SPP). Traditionally, CNN is used to detect the shape of objects, and the edge may be ignored. Therefore, adding HED increases the robustness of the edge, and finally adds SPP to obtain modules of different sizes, and strengthen the detection of undetected objects. The research results are trained in the CityScapes street view data set. The accuracy of Class mIoU for small objects reaches 77.51%, and Category mIoU for large objects reaches 89.95%.

Machine Learning Image ProcessingData Science