The human visual systems tend to integrate oriented line segments into groups if they follow the Gestalt principles. It is commonly acknowledged that early human visual processing operates bye first performing edge detection followed by perceptual organization to group edges into object-like structures. Edge groups can be used to improve a variety of tasks such as multi-threshold selection, object proposal generation sketch segmentation. In this paper, a perceptual grouping framework that organizes image edges into meaningful structures is proposed. The grouper formulates edge grouping as a spectral clustering problem, where a computation model based on Gestalt principles is developed to encode probabilities of candidate edge pairs. First, a probability model is proposed as grouping constraint inspired by the Gestalt principles, i.e. proximity, continuity and similarity. Then we take the grouping constraint as the input and perform spectral clustering to integrate edge fragments into groups. Experiments have shown that our algorithm can effectively organizes image edges into meaningful structures.
This paper proposes a novel posture estimation method which is composed of two stages. The first stage is reconstructing lines from stereo images and the second stage is estimate posture by reconstructed lines. Accuracy of line detection is better than the point detection. So our method have better accuracy than the methods base on points.
The scene matching based navigation is an important precision navigation technology for unmanned aerial vehicles (UAV). Selection of interest area where reference image is made has an important influence on the precision of matching result besides the performance of match algorithm. In this paper, a method to select interest area based on structured edge detection is proposed. We use a data driven approach that classifies each pixel with a typical structured edge label. We propose a method that combines these labels into a feature measuring suitable to match of a region. Then a SVM classifier is trained to classify the features and get the final result of the selection of interest area. The experimental result shows that the proposed method is valid and effective.