Tiantian Feng

Geometric active contour based approach for segmentation of high-resolution spaceborne SAR images

Segmentation is the key step in auto-interpretation of high-resolution spaceborne synthetic aperture radar (SAR) images. A novel method is proposed based on integrating the geometric active contour (GAC) and the support vector machine (SVM) models. First, the images are segmented by using SVM and textural statistics. A likelihood measurement for every pixel is derived by using the initial segmentation. The Chan-Vese model then is modified by adding two items: the likelihood and the distance between the initial segmentation and the evolving contour. Experimental results using real SAR images demonstrate the good performance of the proposed method compared to several classic GAC models.

Pedestrian Counting for a Large Scene Using a GigaPan Panorama and Exemplar-SVMs

A novel scheme for counting pedestrians in a large scene is presented in this paper. A panoramic imaging system, GigaPan, is employed to capture a high-resolution panorama that can cover a large area, such as a square, across 360 degrees. An improved object recognition method based on Exemplar-Support Vector Machines (SVMs) is used to detect pedestrians from the panoramic image. A histogram of oriented gradients (HOG) is used to characterize the objects. Due to the huge number of pixels in the high-resolution panorama, the time cost of the pedestrian counting is extremely high. A Graphics Processing Unit (GPU)-based scheme for parallel computation is adopted to reduce the time cost. The experimental results show that the proposed method is effective.