DSeg: Direct Line Segments Detection
This paper presents a model-driven approach to detect image line segments. The approach incrementally detects segments on the gradient image using a linear Kalman filter that estimates the supporting line parameters and their associated variances. The algorithm is fast and robust with respect to image noise and illumination variations, it allows the detection of longer line segments than data-driven approaches, and does not require any tedious parameters tuning. An extension of the algorithm that exploits a pyramidal approach to enhance the quality of results is proposed. Results with varying scene illumination and comparisons to classic existing approaches are presented.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SNE-RoadSeg+: Rethinking Depth-Normal Translation and Deep Supervision for Freespace Detection
Freespace detection is a fundamental component of autonomous driving perception. Recently, deep convolutional neural networks (DCNNs) have achieved impressive performance for this task. In particular, SNE-RoadSeg, our pr…
Autonomous DrivingSurface Normal EstimationTranslationBeyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation Models
LIME (Local Interpretable Model-agnostic Explanations) is a popular XAI framework for unraveling decision-making processes in vision machine-learning models. The technique utilizes image segmentation methods to identify …
Decision MakingExplainable artificial intelligenceFeature ImportanceImage Segmentation+2Fusion of neural networks, for LIDAR-based evidential road mapping
LIDAR sensors are usually used to provide autonomous vehicles with 3D representations of their environment. In ideal conditions, geometrical models could detect the road in LIDAR scans, at the cost of a manual tuning of …
Autonomous VehiclesAutomatic segmentation of lung findings in CT and application to Long COVID
Automated segmentation of lung abnormalities in computed tomography is an important step for diagnosing and characterizing lung disease. In this work, we improve upon a previous method and propose S-MEDSeg, a deep learni…
SegmentationEmbedding-based Instance Segmentation in Microscopy
Automatic detection and segmentation of objects in 2D and 3D microscopy data is important for countless biomedical applications. In the natural image domain, spatial embedding-based instance segmentation methods are know…
GPUInstance SegmentationSegmentationSemantic Segmentation