Segmentation Algorithm of the Valid Region in Fisheye Images Using Edge and Region Information

Du, Tongxin; Fang, Bin; Zhou, Mingliang; Zhao, Henjun; Xian, Weizhi; Wu, Xuegang

doi:10.1109/icip40778.2020.9191294

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…Now we need to find a method to minimize this Loss. However, equation (11) does not have a direct solution and can only be solved through iterative methods to find the optimal solution. Therefore, we choose to use the gradient descent method to solve the problem.…”

Section: Improved Circle Fitting Algorithmmentioning

confidence: 99%

“…The accuracy and speed of the segmentation algorithm directly affect the quality of panoramic image synthesis. Due to the influence of lighting noise during the capturing process of fish-eye images, in wellilluminated images, there may be lens flare regions at the image edges, where the grayscale values are similar to those of the valid region [11] . This can cause interference in computer recognition.…”

Section: Introductionmentioning

confidence: 99%

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Fish-eye image effective region segmentation algorithm based on circle fitting

Jiang,

Yu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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When synthesizing panoramic images from fish-eye images, dynamically segmenting the fish-eye image's valid region is crucial. This paper addresses the challenge of segmenting the valid region in fish-eye images caused by lens glare by proposing a segmentation algorithm that combines adaptive threshold edge detection with improved circle fitting. The process begins by performing gradient and direction calculations on the original fish-eye image to extract edge contour information. Subsequently, the edge contours undergo refinement using the non-maximum suppression method. An adaptive dual threshold is generated using the maximum between-class variance method to enable accurate edge detection. The algorithm further initializes and fits the edge points using the least squares method while incorporating a loss function to enhance the circle-fitting algorithm. Finally, an iterative gradient descent method is employed to iteratively solve for the optimal solution and segment the fish-eye image's valid region. Experimental results demonstrate the algorithm's ability to accurately detect contours and effectively segment the valid region, successfully addressing the challenge of extracting the valid region caused by lens glare.

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Section: Improved Circle Fitting Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fish-eye image effective region segmentation algorithm based on circle fitting

Jiang,

Yu,

2023

Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023)

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Vision-based environmental perception for autonomous driving

Liu,

Lu,

Lin

2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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Visual perception plays an important role in autonomous driving. One of the primary tasks is object detection and identification. Since the vision sensor is rich in color and texture information, it can quickly and accurately identify various road information. The commonly used technique is based on extracting and calculating various features of the image. The recent development of deep learning-based method has better reliability and processing speed and has a greater advantage in recognizing complex elements. For depth estimation, vision sensor is also used for ranging due to their small size and low cost. Monocular camera uses image data from a single viewpoint as input to estimate object depth. In contrast, stereo vision is based on parallax and matching feature points of different views, and the application of Deep learning also further improves the accuracy. In addition, Simultaneous Location and Mapping (SLAM) can establish a model of the road environment, thus helping the vehicle perceive the surrounding environment and complete the tasks. In this paper, we introduce and compare various methods of object detection and identification, then explain the development of depth estimation and compare various methods based on monocular, stereo, and RGB-D sensors, next review and compare various methods of SLAM, and finally summarize the current problems and present the future development trends of vision technologies.

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Segmentation Algorithm of the Valid Region in Fisheye Images Using Edge and Region Information

Cited by 2 publications

References 11 publications

Fish-eye image effective region segmentation algorithm based on circle fitting

Fish-eye image effective region segmentation algorithm based on circle fitting

Vision-based environmental perception for autonomous driving

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