This paper proposes an edge based compressively sensed (CS) geodesic active contour (GAC) model, termed CS-GAC, to ensure faithful edge detection and accurate object segmentation. The motivation behind this paper is that edge information driving ...

• Edge information driving the contour evolution is iteratively obtained from CS measurements.
• Updating property of the edge indicator takes advantages of both edge sparsity and edge detection.
• The complex shearlet transform based ...

This paper presents a new method for detecting ellipses in images, which has many applications in pattern recognition and robotic tasks. Previous approaches typically use sophisticated arc grouping strategies or calculate differential such as ...

• A novel ellipse detection method that is accurate and capable of processing complex images.
• A new and robust arc grouping strategy using chord computation without differential.
• An effective verification scheme by the projective ...

• We point out that the performances of self-labeling-based semi-supervised object detection (SSOD) approaches are always limited, and the reason behind such ...

Semi-supervised object detection has recently achieved substantial progress. As a mainstream solution, the self-labeling-based methods train the detector on both labeled data and unlabeled data with pseudo labels predicted by the ...

• An adaptive local variances-based coefficient is proposed to distinguish noise points and the edge of an object, which improves the segmentation accuracy of ...

Medical image segmentation is a very challenging task, not only because the intensity of the medical image itself is not uniform, but also it may be accompanied by the impact of noise. Although mathematics, computer science, medicine, ...

• Region-wise loss can simultaneously account for class imbalance and pixel importance.

We propose Region-wise (RW) loss for biomedical image segmentation. Region-wise loss is versatile, can simultaneously account for class imbalance and pixel importance, and it can be easily implemented as the pixel-wise multiplication ...

• Color is generalizable across plant species for leaf segmentation.
• Leaves in ...

As an essential prerequisite task in image-based plant phenotyping, leaf segmentation has garnered increasing attention in recent years. While self-supervised learning is emerging as an effective alternative to various computer vision ...

• We present a dynamic curvature-penalized geodesic model in the orientation-lifted space to detect weak structures with complicated geometries.

Geodesic models are considered as a fundamental and powerful tool in the applications of curvilinear structure extraction, where the target structures are usually modeled as geodesic paths connecting prescribed points. Despite great ...

• A measure of connectivity between fuzzy superpixels and refined superpixels by their spatial intersection is proposed.

This article presents a multi-phase image segmentation methodology based on fuzzy superpixel decomposition, aggregation and merging. First, a collection of layers of dense fuzzy superpixels is generated by the variational fuzzy ...

• A structure transformation is proposed to transform the heterogeneous images to the same differential domain.

Change detection between heterogeneous images has become an increasingly interesting research topic in remote sensing. The different appearances and statistics of heterogeneous images bring great challenges to this task. In this paper, ...

• A new method is proposed for minimizing general objective functionals for image segmentation.

Variational methods, which have been tremendously successful in image segmentation, work by minimizing a given objective functional. The objective functional usually consists of a fidelity term and a regularization term. Because ...

• Moving objects segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, traffic monitoring, and surveillance.
• MOS becomes challenging due to abrupt ...

Moving Objects Segmentation (MOS) is a fundamental task in many computer vision applications such as human activity analysis, visual object tracking, content based video search, traffic monitoring, surveillance, and security. MOS becomes ...

• Dual-region feature generation is proposed to eliminate false positives among different instances for crowded pedestrian detection.

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Occlusion is a severe problem for pedestrian detection in crowded scenes. Due to the diversity of pedestrian postures and occlusion forms, leading to false detection and missed detection. In this paper, we propose a high quality ...

• We verify the importance of position-sensitive features and the differences among feature levels in FPN and propose AR-FPN module to extract more ...

Increasing object detectors reveal the importance of feature representation in improving detection performance. Currently, feature enhancement mainly focuses on Feature Pyramid Network (FPN) as well as Region-of-Interest (RoI) feature ...

• A foreground and background separation framework is proposed for prohibited item detection in heavily overlapping X-ray image.

X-ray imagery security screening is an essential component of transportation and logistics. In recent years, some researchers have used computer vision algorithms to replace inefficient and tedious manual baggage inspection. However, X-...

• A novel formulation: The formulation tries to cover each circle instance by a pre-determined number of maximally compatible edge ...

In the paper, we present a circle detector that achieves the state-of-art performance in almost every type of image. The detector represents each circle instance by a set of equally distributed arcs and searches for the same number of ...

• The drawbacks of taking weather recognition as a simple image classification problem is analyzed.

Although it is of great importance to recognize weather conditions automatically, this task has not been explored thoroughly in practice. Generally, most approaches in the literature simply treat it as a common image classification ...

• A global saliency-guided energy term is defined using the saliency map to roughly extract the objects, which significantly improves the segmentation efficiency and the robustness to noise and to initialize the SLSM.
• Unlike most ...

The level set model is a popular method for object segmentation. However, most existing level set models perform poorly in color images since they only use grayscale intensity information to defined their energy functions. To address this ...

• We introduce an effective agnostic attribute video object segmentation method.
• ...

The presence of limited spatio-temporal resolution in dynamic scenes renders segmentation of foreground objects problematic, as it brings negative effects on candidate object missing or motion boundary overfilling caused by large ...

• A multi-scale local feature-based level set method is proposed.
• The optimal ...

Images with intensity inhomogeneity pose significant challenges in image segmentation. Local region-based level set models have recently been recognized as promising methods to segment such images. In these models, local intensity ...

• The novel global weighted SPF (GWSPF) is defined by introducing the normalized global minimum absolute differences as the coefficients of global inner and ...

This paper proposes a new global and local weighted signed pressure force (SPF) based active contour model (ACM) to segment various types of images. First, by introducing the normalized global minimum absolute differences as the ...