Sun Lei
School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
PeerJ Comput Sci. 2024 Jan 18;10:e1812. doi: 10.7717/peerj-cs.1812. eCollection 2024.
Object detection plays an important role in the field of computer vision. The purpose of object detection is to identify the objects of interest in the image and determine their categories and positions. Object detection has many important applications in various fields. This article addresses the problems of unclear foreground contour in moving object detection and excessive noise points in the global vision, proposing an improved Gaussian mixture model for feature fusion. First, the RGB image was converted into the HSV space, and a mixed Gaussian background model was established. Next, the object area was obtained through background subtraction, residual interference in the foreground was removed using the median filtering method, and morphological processing was performed. Then, an improved Canny algorithm using an automatic threshold from the Otsu method was used to extract the overall object contour. Finally, feature fusion of edge contours and the foreground area was performed to obtain the final object contour. The experimental results show that this method improves the accuracy of the object contour and reduces noise in the object.
目标检测在计算机视觉领域中起着重要作用。目标检测的目的是识别图像中感兴趣的物体，并确定它们的类别和位置。目标检测在各个领域都有许多重要应用。本文针对运动目标检测中前景轮廓不清晰以及全局视觉中噪声点过多的问题，提出了一种用于特征融合的改进高斯混合模型。首先，将RGB图像转换到HSV空间，并建立混合高斯背景模型。接着，通过背景减法获得目标区域，使用中值滤波方法去除前景中的残余干扰，并进行形态学处理。然后，使用一种基于大津法自动阈值的改进Canny算法来提取整体目标轮廓。最后，对边缘轮廓和前景区域进行特征融合以获得最终的目标轮廓。实验结果表明，该方法提高了目标轮廓的准确性，并减少了目标中的噪声。
