Image Segmentation And Edge Detection Based On Watershed Techniques

Salman, Nassir H.; Liu, C.-Q.

doi:10.1080/1206212x.2003.11441713

Cited by 32 publications

(27 citation statements)

References 10 publications

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“…The use of graphs was taken [Boyk01, Roth04,Felz04] to produce a segmentation based on region growing. By using the analogy between image and topographic relief, Beucher et al [Beuc79], and more recently Salman [Salm06], have proposed approaches based on watershed. Improvements have recently been made by Couprie et al [Coup09] to optimize the performance of watershed and diversify its use.…”

Section: State Of the Artmentioning

confidence: 99%

Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods

Grand-Brochier

Vacavant

Cerutti

et al. 2015

IEEE Trans. on Image Process.

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In this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation--Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, using preprocessing tools, such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally, we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones.

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Section: State Of the Artmentioning

confidence: 99%

Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods

Grand-Brochier

Vacavant

Cerutti

et al. 2015

IEEE Trans. on Image Process.

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“…Edge detection is a process of finding the sharp contrast based on the intensities of an image, by reducing the amount of data in an image, while preserving important structural features of that image [3]. The methods for detecting the edges depend on the computation of image gradients and the type of filter used to calculate gradient estimates in the horizontal and vertical directions.…”

Section: Boundary Detection Using Thresholdingmentioning

confidence: 99%

Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

Krishnaveni¹,

Subashini²,

Dhivyaprabha³

2016

IJECE

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The development of computer based sign language recognition system, for enabling communication with hearing impaired people, is an important research area that faces different challenges in the pre-processing stage of image processing, particularly in boundary detection stage. In edge detection, the possibility of achieving high quality images significantly depends on the fitting threshold values, which are generally selected using canny method, and these threshold values may vary, based on the type of images and the applications chosen. This research work presents a novel idea of establishing a hybrid particle swarm optimization algorithm, which is a combination of PSO with the behavioural pattern of cellular organism in canny method that defines an objective to find optimal threshold values for the implementation of double thresholding hysteresis method, which is viewed as a non-linear complex problem. The attempt to incorporate the model has minimized the problem of quick convergence of PSO algorithm which has improved the detection of broken edges. The efficiency of the proposed algorithm is proved through the experimental observation, done in Tamil sign images to indicate the better performance of canny operator by introducing new variant based PSO.

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“…The Watershed segmentation technique is based on a simple heuristic approach that consists in analyzing the gray level of the image pixels in an ascending order and performing region growing [10]. Because of the regions in the image characterized by small variations in gray levels, in practice, the Watershed segmentation is often applied to the gradient of an image rather than to the image itself [11].The Watershed segmentation technique uses, in addition to the gradient image, a seed image calculated from the gradient image. By gathering into the region, the pixels that are the closest to the corresponding seed, the growing process determines the region associated with each seed.…”

Section: Watershed Transformmentioning

confidence: 99%

A Watershed-Canny Based Approach for Building Footprint Extraction from Very High Resolution Optical Image

2017

The 5th International Conference on Advanced Computer Science Applications and Technologies (ACSAT 2017)

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Abstract. The advanced very high resolution (VHR) sensors are capable of achieving submeter resolution, which offers the opportunity for a fine level of analysis of man-made structures. In this paper, we present a method for the extraction of 2-D building footprints from VHR optical scenes. The data sets include Worldview-2 (0.5m) image and cover urban area of San Francisco. Our main idea was to combine edge based detector, regions based segmentation and non-building masks. In the first step, Canny operator was used to extract edge from the optical image and morphological operations were used to remove small edge. In the second step, the Watershed transform was used for segmentation of optical image and morphological operations were used to remove small regions. In the third step, we computed two non-building masks which are vegetation mask and shadow mask. These two masks were applied first to filter Watershed segmentation result. In the fourth step, we have translated the Canny contour image in both directions and computed the Correlation Coefficient between Canny contour and filtered Watershed contour for each value of translation, and we got the best translation parameters when Correlation Coefficient was maximal. In the last step, the matched Canny edge image was combined separately with: 1) vegetation mask, 2) shadow mask and 3) filtered Watershed segmentation. The three results were then combined (logical "or") to obtain the final building footprint. The obtained results demonstrated that our approach performs well and improves the boundaries extraction based on Watershed segmentation.

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Image Segmentation And Edge Detection Based On Watershed Techniques

Cited by 32 publications

References 10 publications

Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods

Tree Leaves Extraction in Natural Images: Comparative Study of Preprocessing Tools and Segmentation Methods

Improved Canny Edges Using Cellular Based Particle Swarm Optimization Technique for Tamil Sign Digital Images

A Watershed-Canny Based Approach for Building Footprint Extraction from Very High Resolution Optical Image

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