Abstract
Boundary detection is a fundamental computer vision problem that is essential for a variety of tasks, such as contour and region segmentation, symmetry detection and object recognition and categorization. We propose a generalized formulation for boundary detection, with closed-form solution, applicable to the localization of different types of boundaries, such as object edges in natural images and occlusion boundaries from video. Our generalized boundary detection method (Gb) simultaneously combines low-level and mid-level image representations in a single eigenvalue problem and solves for the optimal continuous boundary orientation and strength. The closed-form solution to boundary detection enables our algorithm to achieve state-of-the-art results at a significantly lower computational cost than current methods. We also propose two complementary novel components that can seamlessly be combined with Gb: first, we introduce a soft-segmentation procedure that provides region input layers to our boundary detection algorithm for a significant improvement in accuracy, at negligible computational cost; second, we present an efficient method for contour grouping and reasoning, which when applied as a final post-processing stage, further increases the boundary detection performance.
Highlights
Boundary detection is a fundamental computer vision problem with broad applicability in areas such as feature extraction, contour grouping, symmetry detection, segmentation of image regions, object recognition and categorization
In this paper we propose a general formulation for boundary detection that can be applied, in principle, to the identification of any type of boundaries, such as general edges from low-level static cues (Fig. 11), and occlusion boundaries from optical flow (Figs. 14 and 15)
There is an interesting connection between the filters used in generalized boundary detection method (Gb) (e.g., Hx ∝ g(x − x0, y − y0)2(x − x0)) and Gaussian Derivative (GD) filters (i.e., Gx(x − x0, y − y0) ∝ g(x−x0, y−y0)(x−x0)), which could be used for computing the gradient of multi-images [7]
Summary
Boundary detection is a fundamental computer vision problem with broad applicability in areas such as feature extraction, contour grouping, symmetry detection, segmentation of image regions, object recognition and categorization. When discontinuities in intensity are correlated with discontinuities in optical flow, texture or other cues, the evidence for a relevant boundary is higher, with boundaries that align across multiple layers typically corresponding to the semantic boundaries that interest humans Based on these observations and motivated by the analysis of real world images (see Fig. 2), we develop a compact, integrated boundary model that ca simultaneously consider evidence from different input layers of the image, obtained from both lower and higher levels of visual processing. 2) Our formulation provides an efficient closed-form solution that jointly computes the boundary strength and its normal by combining evidence from different input layers This is in contrast with current approaches [1], [46], [49] that process the low and mid-level layers separately and combine them through multiple complex, computationally demanding stages, in order to detect different types of boundaries. Our method bridges the gap between model fitting methods such as [2], [28], and recent successful, but computationally demanding learning-based boundary detectors [1], [46], [49]. 5) We propose an efficient mid-level softsegmentation method which offers effective input layers for our boundary detector and significantly improves accuracy at small computational expense (Sec. 6). 6) We present an efficient method for contour grouping and reasoning, which further improves the overall performance at minor cost (Sec. 7)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Pattern Analysis and Machine Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
