Shadow Detection Using Color and Edge Information

Golchin, Maryam; Khalid, Fatimah; Abdullah, Lili Nurliyana; Davarpanah, Seyed Hashem

doi:10.3844/jcssp.2013.1575.1588

Cited by 18 publications

(12 citation statements)

References 16 publications

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“…Thresholding is specified as a non-contextual technique in which the color or gray-scale image is transformed into binary region map (Efford, 2000). Generally, segmentation is the most pragmatic preprocessing technique that could be applied on manifold filed where the texture of background or foreground should be analyzed (Golchin et al, 2013). As the failure and success rate of every image processing applications is extremely dependent on reliability and scrupulousness of the preprocessing techniques and especially thresholding, therefore, many researchers attempted to introduce thresholding optimization techniques using algorithms such as P-tile (Doyle, 1962), Gray-Level Histogram thresholding "Otsu" (Otsu, 1979), Maximum Entropy with adaptive Genetic algorithm Nie, 2009), Shanbhag algorithm (Shanbhag, 1994), Yen technique (Yen et al, 1995), Entropic thresholding method based on Ant Colony Genetic algorithm (Shen et al, 2009), integration of clustering algorithm and marker controlled watershed segmentation algorithm (Christ et al, 2010) and many other algorithms.…”

Section: Related Workmentioning

confidence: 99%

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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Abstract:In automated pulmonary nodules extraction and lung disease diagnosis by image processing techniques, image segmentation is utilized as a primary and the most essential step of lung tumor analysis. But due to extensive similarity between pulmonary vessels, bronchus and arteries in lung region and the low contrast of the ComputedTomography (CT) image the accuracy of lung tumor diagnosis is highly dependent on the precision of segmentation. Therefore, precise lung CT image segmentation has become a challenging preprocessing task for every lung disease pathological application.In this study, a novel FourDirectional Thresholding (FDT) technique is introduced. This propounded technique segments the pulmonary parenchyma in Computed-Tomography (CT) images using the Similarity-Based Segmentation (SBS). The proposed technique aims to augment the precision of the CT image thresholding by implementing an advanced thresholding approach from four different directions in which the determination of pixels' value as being either on foreground or background is highly dependent on its adjacent pixel's intensity value and the final decision is made based on all four directions' thresholding results. In this study the importance of neighbor pixels in precision of thresholding with FDT technique is demonstrated and the effectiveness of FDT method has been evaluated on different CT images. Eventually the result of segmentation using FDT method is compared by other precursors techniques, which corroborates the high exactitude of proposed technique.

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Section: Related Workmentioning

confidence: 99%

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Heidari¹,

Abdullah²,

Abdullah³

2015

Journal of Computer Science

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“…25 Some representative approaches of this category of shadow detection methods are described next. These methods often estimate the lighting and reflection components based on the intensity of a pixel and of its vicinity.…”

Section: Physical-based Methodsmentioning

confidence: 99%

Shadow detection in camera-based vehicle detection: survey and analysis

Barcellos

Gomes

Scharcanski

2016

J. Electron. Imaging

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Shadow detection in camera-based vehicle detection: survey and analysis,"Abstract. The number of vehicles in circulation in modern urban centers has greatly increased, which motivates the development of automatic traffic monitoring systems. Consequently, camera-based traffic monitoring systems are becoming more widely used, since they offer important technological advantages in comparison with traditional traffic monitoring systems (e.g., simpler maintenance and more flexibility for the design of practical configurations). The segmentation of the foreground (i.e., vehicles) is a fundamental step in the workflow of a camera-based traffic monitoring system. However, foreground segmentation can be negatively affected by vehicle shadows. This paper discusses the types of shadow detection methods available in the literature, their advantages, disadvantages, and in which situations these methods can improve camera-based vehicle detection for traffic monitoring. In order to compare the performance of these different types of shadow detection methods, experiments are conducted with typical methods of each category using publicly available datasets. This work shows that shadow detection definitely can improve the reliability of traffic monitoring systems, but the choice of the type of shadow method depends on the system specifications (e.g., tolerated error), the availability of computational resources, and prior information about the scene and its illumination in regular operation conditions. Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx Barcellos, Gomes, and Scharcanski: Shadow detection in camera-based vehicle detection. . . Downloaded From: http://electronicimaging.spiedigitallibrary.org/ on 06/21/2016 Terms of Use: http://spiedigitallibrary.org/ss/TermsOfUse.aspx

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“…The partial shadow significantly decreases the image quality due to that the shadow area in the image lacks of proper exposure. Some specific algorithm could resolve the problem of the partial shadow (Golchin et al, 2013), however with the consideration of the computation time, using artificial illumination which can provide uniform light density in the field of view of the camera is more feasible. …”

Section: Ajabsmentioning

confidence: 99%

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

Liu

Lee

Saunders

2014

American Journal of Agricultural and Biological Sciences

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More than half of the Australian cropping land is no-tillage and weed control within continuous no-tillage agricultural cropping area is becoming more and more difficult. A major problem is that the heavy herbicide usage causes some of more prolific weeds becoming more resistant to the regular herbicides and therefore more powerful and more expensive options are being pursued. To overcome such problems with aiming at the reduction of herbicide usage, this proposed research focuses on developing a machine vision system which can detect and mapping weeds or do spot spray. The weed detection methods described in this study include three aspects which are image acquisition, a new green plant detection algorithm using hybrid spectral indices and a new inter-row weed detection method taking the advantage of the location of the crop rows. The developed method could detect the weeds both during the non-growing summer period and also within the growing season until the canopy of the crop has closed. The design of the methods focuses on overcoming the challenges of the complex no-tillage background, the faster image acquisition speed and quicker processing time for real-time spot spray. The experiment results show that the proposed method are more suitable for the weed detection in the notillage background than the existing methods and could be used as a powerful tool for the weed control.

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Shadow Detection Using Color and Edge Information

Cited by 18 publications

References 16 publications

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

A Novel Four-Directional Thresholding Approach for Lung Computed-Tomography Images by Using Similarity-Based Segmentation Technique

Shadow detection in camera-based vehicle detection: survey and analysis

Development of a Machine Vision System for Weed Detection During Both of Off-Season and in-Season in Broadacre No-Tillage Cropping Lands

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