The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

Wu, Yanyan; Li, Qian

doi:10.3390/s22218202

Cited by 30 publications

(12 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The light types, such as window lights, in urban nighttime UAV images as small targets are prone to over-segmentation using multiresolution segmentation directly [ 20 , 21 , 22 ]. In this paper, we finally chose to combine Canny edge detection to alleviate the over-segmentation phenomenon.…”

Section: Methodsmentioning

confidence: 99%

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Zhang

Zhou

et al. 2023

Sensors

View full text Add to dashboard Cite

Fine classification of urban nighttime lighting is a key prerequisite step for small-scale nighttime urban research. In order to fill the gap of high-resolution urban nighttime light image classification and recognition research, this paper is based on a small rotary-wing UAV platform, taking the nighttime static monocular tilted light images of communities near Meixi Lake in Changsha City as research data. Using an object-oriented classification method to fully extract the spectral, textural and geometric features of urban nighttime lights, we build four types of classification models based on random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN) and decision tree (DT), respectively, to finely extract five types of nighttime lights: window light, neon light, road reflective light, building reflective light and background. The main conclusions are as follows: (i) The equal division of the image into three regions according to the visual direction can alleviate the variable scale problem of monocular tilted images, and the multiresolution segmentation results combined with Canny edge detection are more suitable for urban nighttime lighting images; (ii) RF has the highest classification accuracy among the four classification algorithms, with an overall classification accuracy of 95.36% and a kappa coefficient of 0.9381 in the far view region, followed by SVM, KNN and DT as the worst; (iii) Among the fine classification results of urban light types, window light and background have the highest classification accuracy, with both UA and PA above 93% in the RF classification model, while road reflective light has the lowest accuracy; (iv) Among the selected classification features, the spectral features have the highest contribution rates, which are above 59% in all three regions, followed by the textural features and the geometric features with the smallest contribution rates. This paper demonstrates the feasibility of nighttime UAV static monocular tilt image data for fine classification of urban light types based on an object-oriented classification approach, provides data and technical support for small-scale urban nighttime research such as community building identification and nighttime human activity perception.

show abstract

Section: Methodsmentioning

confidence: 99%

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Zhang

Zhou

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…The image segmentation process remains a difficult and unsolved problem in image processing and computer vision. Many studies are comparing important image segmentation techniques, the most relevant works are: In [9], this article presents an algorithm for watershed color image segmentation based on morphological gradients. An improved watershed color image segmentation algorithm based on morphological gradients is proposed.…”

Section: Related Workmentioning

confidence: 99%

“…Watershed segmentation is a mathematical-morphological approach that draws similarities with real-life flood situations [19]. Watershed Segmentation [20][21][22], is an image segmentation algorithm that combines ideas about topography and regional growth. This algorithm displays the grayscale image as a topographic map, as shown in Fig.…”

Section: Watershed Algorithmmentioning

confidence: 99%

Enhance watershed algorithms using principal component analysis capabilities

F. Mohamed,

Zaki Soliman,

Mahmoud Ashour

2024

QJES

View full text Add to dashboard Cite

Traditional image segmentation algorithms have many drawbacks, such as over-segmentation and image distortion due to reflected light. The Watershed algorithm is one of the most popular image segmentation algorithms. Over-segmentation errors caused by overlapping targets in the image, as well as noise and glare, must be removed. In this article, we apply image processing using the watershed algorithm and propose to improve the algorithm based on principal component analysis. PCA is a popular technique for analyzing large datasets with many advantages per observation. PCA improves data interpretability while maximizing information content, enabling visualization of multidimensional data by finding image component gradients in a new space called the principle component that is unaffected by noise and reflected light. In contrast, the components mainly containing noise will eliminate with negligible information. This paper introduces three primary steps. The process involves applying the watershed algorithm to the image in the first phase, using the proposed approach (applying the watershed algorithm and suggesting an improvement based on principal component analysis) to the image in the second step, and comparing the outcomes of the two previous processes. Test results show that the suggested technique can achieve accurate and durable target shapes.

show abstract

“…Suppose is classes of sample data, each of which is a set of data . Suppose that there are attribute parameters of the sample, For example, the image peak, single peak, grayscale, valley, color, edge, inflection point, tone, multimodal and other indicators 24 . The -dimensional feature space is constructed according to the indicators of these sample points, and all the sample points correspond to the points in the -dimensional feature space.…”

Section: Small Neighborhood Clustering Segmentation Algorithmmentioning

confidence: 99%

A clustering-optimized segmentation algorithm and application on food quality detection

Chen

et al. 2023

Sci Rep

View full text Add to dashboard Cite

For solving the problem of quality detection in the production and processing of stuffed food, this paper suggests a small neighborhood clustering algorithm to segment the frozen dumpling image on the conveyor belt, which can effectively improve the qualified rate of food quality. This method builds feature vectors by obtaining the image's attribute parameters. The image is segmented by a distance function between categories using a small neighborhood clustering algorithm based on sample feature vectors to calculate the cluster centers. Moreover, this paper gives the selection of optimal segmentation points and sampling rate, calculates the optimal sampling rate, suggests a search method for optimal sampling rate, as well as a validity judgment function for segmentation. Optimized small neighborhood clustering (OSNC) algorithm uses the fast frozen dumpling image as a sample for continuous image target segmentation experiments. The experimental results show the accuracy of defect detection of OSNC algorithm is 95.9%. Compared with other existing segmentation algorithms, OSNC algorithm has stronger anti-interference ability, faster segmentation speed as well as more efficiently saves key information ability. It can effectively improve some disadvantages of other segmentation algorithms.

show abstract

The Algorithm of Watershed Color Image Segmentation Based on Morphological Gradient

Cited by 30 publications

References 39 publications

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach

Enhance watershed algorithms using principal component analysis capabilities

A clustering-optimized segmentation algorithm and application on food quality detection

Contact Info

Product

Resources

About