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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Estimating the distance traveled by a vehicle using its recorded velocity over time is a common problem in physics and engineering. When velocity data is available at discrete time intervals, rather than as a continuous function, numerical integration methods such as the trapezoidal rule are often employed to approximate the total displacement.The trapezoidal rule works by dividing the total time interval into several equal segments. Within each segment, the recorded velocities at the endpoints...
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Fast Contour-Tracing Algorithm Based on a Pixel-Following Method for Image Sensors.

Jonghoon Seo1, Seungho Chae2, Jinwook Shim3

  • 1Software Platform R&D Lab., LG Electronics Advanced Research Institute, 19 Yangjae-daero 11-gil, Seocho-gu, Seoul, 06772, Korea. jonghoon.seo@lge.com.

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This study introduces a novel contour-tracing algorithm for image sensors. It efficiently extracts and compresses contour data, improving object detection accuracy and enabling accurate image restoration, even for complex corners.

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boundary followingcontour data compressioncontour tracingpixel following

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Area of Science:

  • Computer Vision
  • Image Processing
  • Algorithm Development

Background:

  • Contour pixels are crucial for separating objects from backgrounds in image analysis.
  • Current contour tracing methods are essential for smart/wearable image sensor devices but can be improved for speed and accuracy.

Purpose of the Study:

  • To develop a novel contour-tracing algorithm that enhances speed and accuracy in object detection.
  • To enable efficient data compression and accurate restoration of contour images.

Main Methods:

  • A new algorithm classifies contour pixels based on local patterns (straight line, inner corner, outer corner, inner-outer corner).
  • It traces contours by utilizing the classification type of the previous pixel.
  • Data compression is achieved using representative and inner-outer corner points for accurate restoration.

Main Results:

  • The proposed algorithm demonstrates superior processing time and accuracy compared to conventional techniques.
  • It successfully compresses contour pixel data and accurately restores images, including challenging inner-outer corner features.
  • Conventional algorithms struggle to restore inner-outer corner details accurately.

Conclusions:

  • The novel contour-tracing algorithm offers significant improvements in efficiency and accuracy for image sensor applications.
  • Its ability to handle complex contour types and compress data makes it highly valuable for real-time object detection.
  • This method advances contour extraction capabilities, particularly for wearable and smart imaging devices.