&lt;title&gt;Modified binary watershed transform for segmentation of agricultural products&lt;/title&gt;

Talukder, Ashit; Casasent, David P.; Lee, Ha-Woon; Keagy, Pamela M.; Schatzki, Thomas F.

doi:10.1117/12.336912

Cited by 11 publications

(4 citation statements)

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“…Such analysis is highly mandated in agricultural and food industry as worms often contribute to several conditions favoring mold growth or toxin production. Many prior work exist in the literature on the segmentation of agricultural product [4,39,40,41]. Most of them are based on watershed transform [7,39,44], mathematical morphology [17], granulometric methods [43], or their variants.…”

Section: Related Workmentioning

confidence: 99%

A Fast and Automated Granulometric Image Analysis Based on Digital Geometry

Bera

Biswas

Bhattacharya

2015

Fundamenta Informaticae

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Granular object segmentation is an important area of image processing, which has several practical applications in agriculture, food industry, geology, and forensics. In this paper, we present a simple algorithm for the analysis of granulometric images that consist of touching or overlapping convex objects such as coffee bean, food grain, nuts, blood cell, or cookies. The algorithm is based on certain underlying digital-geometric features embedded in their binary snapshots. The concept of an outer isothetic cover and the property of geometric convexity are used to extract the joining points (or concavity points) from the ensemble of objects. Next, a combinatorial technique is employed to determine the separator of two overlapping or neighboring objects. This technique is fully automated and it needs only integer-domain computation. The termination time of the algorithm can be tradedoff with the quality of segmentation by changing the resolution parameter. Experimental results for a variety of objects chosen from different application domains such as cell image, coffee-bean image and others demonstrate the efficiency and robustness of the proposed method compared to earlier watershed-based algorithms.

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Section: Related Workmentioning

confidence: 99%

A Fast and Automated Granulometric Image Analysis Based on Digital Geometry

Bera

Biswas

Bhattacharya

2015

Fundamenta Informaticae

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“…3. Segmenting touching objects in an image is a challenging problem that received a great deal of attention in several classes of image processing applications, such as medical image analysis [5][6][7], food industry and agriculture [8,9]. Several techniques have been proposed: Hough transform, curve fitting, active contour [10], watershed transform [11,12] from which watershed transform emerges as a reference technique due to its stability in a wide range of applications.…”

Section: Introductionmentioning

confidence: 99%

A new approach for pin detection for an electronic system prototyping reconfigurable platform

Nguyen

Guillemot

Savaria

et al. 2012

2012 23rd IEEE International Symposium on Rapid System Prototyping (RSP)

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A new approach for pin detection in a reconfigurable platform for electronic system prototyping is proposed. It makes use of image processing techniques to first, extract pin core regions by a two-pass process: a top-down multi-level erosion process to remove touching parts of pin regions, followed by a bottom-up pin core recovery process to recover core regions removed by the first process. Once all pin cores have been isolated, regions associated to every pin can be determined by a simple segmentation procedure based on the shortest distance principle. The proposed approach has successfully extracted the pin maps from many circuit footprint images, even in cases of touching pin regions. The results produced by the proposed method have also been compared with those obtained from the reference Watershed algorithm and this shows that our approach provides better results in terms of pin recovery and pin positioning accuracy for the type of images produced by our electronic prototyping system.

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“…Prior work on pistachio nut classification [2] did not address the problem of segmentation of touching nuts. The image preprocessing we use to segment touching pistachio nuts is discussed elsewhere [9]. The MRDF method to extract features that are used as inputs to the classifier is discussed in Sect.…”

Section: Introductionmentioning

confidence: 99%

<title>New nonlinear features for inspection, robotics, and face recognition</title>

Casasent

Talukder

1999

SPIE Proceedings

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Classification of real-time X-ray images of randomly oriented touching pistachio nuts is discussed. The ultimate objective is the development of a system for automated non-invasive detection of defective product items on a conveyor belt. We discuss the extraction of new features that allow better discrimination between damaged and clean items (pistachio nuts). This feature extraction and classification stage is the new aspect of this paper; our new maximum representation and discriminating feature (MRDF) extraction method computes nonlinear features that are used as inputs to a new modified k nearest neighbor classifier. In this work, the MRDF is applied to standard features (rather than iconic data). The MRDF is robust to various probability distributions of the input class and is shown to provide good classification and new ROC (receiver operating characteristic) data. Other applications of these new feature spaces in robotics and face recognition are also noted.

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<title>Modified binary watershed transform for segmentation of agricultural products</title>

Cited by 11 publications

References 0 publications

A Fast and Automated Granulometric Image Analysis Based on Digital Geometry

A Fast and Automated Granulometric Image Analysis Based on Digital Geometry

A new approach for pin detection for an electronic system prototyping reconfigurable platform

<title>New nonlinear features for inspection, robotics, and face recognition</title>

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