A survey on deep learning-based panoptic segmentation

Li, Xinye; Chen, Ding

doi:10.1016/j.dsp.2021.103283

Cited by 27 publications

(10 citation statements)

References 80 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The PS is classified into top-down, bottom-up, single-path approaches, and other approaches [39]. But many deep learning methods follow the top-down approach, and this section elaborates the understanding of this approach.…”

Section: Top-down Approach To Panoptic Segmentationmentioning

confidence: 99%

Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Mohammed,

Ralescu

2024

Algorithms

View full text Add to dashboard Cite

Image understanding plays a pivotal role in various computer vision tasks, such as extraction of essential features from images, object detection, and segmentation. At a higher level of granularity, both semantic and instance segmentation are necessary for fully grasping a scene. In recent times, the concept of panoptic segmentation has emerged as a field of study that unifies semantic and instance segmentation. This article sheds light on the pivotal role of panoptic segmentation as a visualization tool for understanding scene components, including object detection, categorization, and precise localization of scene elements. Advancements in achieving panoptic segmentation and suggested improvements to the predicted outputs through a top-down approach are discussed. Furthermore, datasets relevant to both scene recognition and panoptic segmentation are explored to facilitate a comparative analysis. Finally, the article outlines certain promising directions in image recognition and analysis by underlining the ongoing evolution in image understanding methodologies.

show abstract

Section: Top-down Approach To Panoptic Segmentationmentioning

confidence: 99%

Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Mohammed,

Ralescu

2024

Algorithms

View full text Add to dashboard Cite

show abstract

“…Different from basic semantic segmentation, instance segmentation needs to label different individual aspects of the same object [11][12][13]. On the basis of instance segmentation, panoptic segmentation needs to detect and segment all objects in the image, including the background [14][15][16]. It can be found that the instance segmentation and panoptic segmentation tasks are more complicated.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

Cheng

Liu

2023

Remote Sensing

View full text Add to dashboard Cite

The semantic segmentation of remote sensing images is a significant research direction in digital image processing. The complex background environment, irregular size and shape of objects, and similar appearance of different categories of remote sensing images have brought great challenges to remote sensing image segmentation tasks. Traditional convolutional-neural-network-based models often ignore spatial information in the feature extraction stage and pay less attention to global context information. However, spatial context information is important in complex remote sensing images, which means that the segmentation effect of traditional models needs to be improved. In addition, neural networks with a superior segmentation performance often suffer from the problem of high computational resource consumption. To address the above issues, this paper proposes a combination model of a modified multiscale deformable convolutional neural network (mmsDCNN) and dense conditional random field (DenseCRF). Firstly, we designed a lightweight multiscale deformable convolutional network (mmsDCNN) with a large receptive field to generate a preliminary prediction probability map at each pixel. The output of the mmsDCNN model is a coarse segmentation result map, which has the same size as the input image. In addition, the preliminary segmentation result map contains rich multiscale features. Then, the multi-level DenseCRF model based on the superpixel level and the pixel level is proposed, which can make full use of the context information of the image at different levels and further optimize the rough segmentation result of mmsDCNN. To be specific, we converted the pixel-level preliminary probability map into a superpixel-level predicted probability map according to the simple linear iterative clustering (SILC) algorithm and defined the potential function of the DenseCRF model based on this. Furthermore, we added the pixel-level potential function constraint term to the superpixel-based Gaussian potential function to obtain a combined Gaussian potential function, which enabled our model to consider the features of various scales and prevent poor superpixel segmentation results from affecting the final result. To restore the contour of the object more clearly, we utilized the Sketch token edge detection algorithm to extract the edge contour features of the image and fused them into the potential function of the DenseCRF model. Finally, extensive experiments on the Potsdam and Vaihingen datasets demonstrated that the proposed model exhibited significant advantages compared to the current state-of-the-art models.

show abstract

“…Image segmentation is the first step of image analysis which separates an image into several regions with similar properties. Several segmentation approaches have been suggested in the literature, such as edge-based [ 15 , 19 , 26 , 44 , 47 ], region-based [ 12 , 13 , 22 , 41 , 46 ], deep-learning [ 21 , 38 ], and histogram thresholding [ 8 , 35 ].…”

Section: Introductionmentioning

confidence: 99%

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

Ehsaeyan

2023

Multimed Tools Appl

View full text Add to dashboard Cite

Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.

show abstract

A survey on deep learning-based panoptic segmentation

Cited by 27 publications

References 80 publications

Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Insights into Image Understanding: Segmentation Methods for Object Recognition and Scene Classification

Semantic Segmentation of Remote Sensing Imagery Based on Multiscale Deformable CNN and DenseCRF

An efficient image segmentation method based on expectation maximization and Salp swarm algorithm

Contact Info

Product

Resources

About