Traditional methods in Edge, Corner and Boundary detection
- URL: http://arxiv.org/abs/2208.07714v1
- Date: Fri, 12 Aug 2022 22:26:05 GMT
- Title: Traditional methods in Edge, Corner and Boundary detection
- Authors: Sai Pavan Tadem
- Abstract summary: There are many real-world applications of edge, corner, and boundary detection methods.
In modern innovations like autonomous vehicles, edge detection and segmentation are the most crucial things.
Real-world images are used to validate detector performance and limitations.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This is a review paper of traditional approaches for edge, corner, and
boundary detection methods. There are many real-world applications of edge,
corner, and boundary detection methods. For instance, in medical image
analysis, edge detectors are used to extract the features from the given image.
In modern innovations like autonomous vehicles, edge detection and segmentation
are the most crucial things. If we want to detect motion or track video, corner
detectors help. I tried to compare the results of detectors stage-wise wherever
it is possible and also discussed the importance of image prepossessing to
minimise the noise. Real-world images are used to validate detector performance
and limitations.
Related papers
- Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
A fundamental limitation of object detectors is that they suffer from "spatial bias"
We present a new zone evaluation protocol, which measures the detection performance over zones.
For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
arXiv Detail & Related papers (2023-10-20T01:44:49Z) - Linear Object Detection in Document Images using Multiple Object
Tracking [58.720142291102135]
Linear objects convey substantial information about document structure.
Many approaches can recover some vector representation, but only one closed-source technique introduced in 1994.
We propose a framework for accurate instance segmentation of linear objects in document images using Multiple Object Tracking.
arXiv Detail & Related papers (2023-05-26T14:22:03Z) - DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients [105.25109274550607]
Line segments are increasingly used in vision tasks.
Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions.
We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector.
arXiv Detail & Related papers (2022-12-15T12:36:49Z) - End-to-End Instance Edge Detection [29.650295133113183]
Edge detection has long been an important problem in the field of computer vision.
Previous works have explored category-agnostic or category-aware edge detection.
In this paper, we explore edge detection in the context of object instances.
arXiv Detail & Related papers (2022-04-06T15:32:21Z) - Segmentation-Based Bounding Box Generation for Omnidirectional
Pedestrian Detection [8.122270502556374]
We propose a segmentation-based bounding box generation method for omnidirectional pedestrian detection.
Due to the wide angle of view, omnidirectional cameras are more cost-effective than standard cameras.
Standard pedestrian detectors are likely to be substantially degraded because pedestrians' appearance in omnidirectional images may be rotated to any angle.
arXiv Detail & Related papers (2021-04-28T13:53:21Z) - Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
This paper explores a relatively less-studied methodology based on classification.
We propose new techniques to push its frontier in two aspects.
Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
arXiv Detail & Related papers (2020-11-19T05:42:02Z) - Line-Circle-Square (LCS): A Multilayered Geometric Filter for Edge-Based
Detection [2.4054377316708964]
The proposed filter applies detection, tracking and learning to each defined expert to extract higher level information for judging scenes without over-calculation.
The experiment validates the effectiveness of the proposed filter in terms of detection precision and resource usage in both experimental and real-world scenarios.
arXiv Detail & Related papers (2020-08-21T05:28:12Z) - False Detection (Positives and Negatives) in Object Detection [1.0965065178451106]
This study explores ways of reducing false positives and negatives with labelled data.
In the process also discovered insufficient labelling in Openimage 2019 Object Detection dataset.
arXiv Detail & Related papers (2020-08-16T20:09:05Z) - Learning Crisp Edge Detector Using Logical Refinement Network [29.59728791893451]
We propose a novel logical refinement network for crisp edge detection, which is motivated by the logical relationship between segmentation and edge maps.
The network consists of a joint object and edge detection network and a crisp edge refinement network, which predicts more accurate, clearer and thinner high quality binary edge maps.
arXiv Detail & Related papers (2020-07-24T11:12:48Z) - Black-box Explanation of Object Detectors via Saliency Maps [66.745167677293]
We propose D-RISE, a method for generating visual explanations for the predictions of object detectors.
We show that D-RISE can be easily applied to different object detectors including one-stage detectors such as YOLOv3 and two-stage detectors such as Faster-RCNN.
arXiv Detail & Related papers (2020-06-05T02:13:35Z) - Refined Plane Segmentation for Cuboid-Shaped Objects by Leveraging Edge
Detection [63.942632088208505]
We propose a post-processing algorithm to align the segmented plane masks with edges detected in the image.
This allows us to increase the accuracy of state-of-the-art approaches, while limiting ourselves to cuboid-shaped objects.
arXiv Detail & Related papers (2020-03-28T18:51:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.