A novel segmentation algorithm for clustered flexional agricultural products based on image analysis

An algorithm to separate clustered flexional agricultural products has been developed.The proposed algorithm achieved a mean accuracy of 92.7% in clustered shrimp dataset.The algorithm has potential to segment flexional agricultural products. The problem for segmentation of clustered flexional agricultural products becomes complex when we perform the duties of counting and classification. A novel algorithm based on concavities and circle fitting is proposed to solve these difficulties. Initially, a circular mask method was applied into the contour images of clustered shrimp, to acquire a series of concavity points. Furthermore, the candidate segmentation lines can be acquired by connecting each two concavity points, and then the correctness for each candidate segmentation line was evaluated by designing four acceptance criterions. Additionally, one new point was acquired by combining adaptive two concavity points together to construct a training model to fit the circle equation, which can transform the erroneous straight segmentation lines into proper curve segmentation lines. Finally, the straight and curve segmentation lines were integrated in one clustered image to achieve the segmentation results. Experimental results revealed that the proposed algorithm achieved a mean accuracy of 92.7% across the clustered shrimp dataset. Other two application examples of flexional agricultural products, such as clustered green pepper and shrimp meat, were also used to test the effectiveness of the proposed algorithm. Segmentation results demonstrated it can successfully segment the images, which indicates the proposed algorithm has the potential to separate clustered flexional agricultural products.

[1]  Roberto de Alencar Lotufo,et al.  Watershed from propagated markers: An interactive method to morphological object segmentation in image sequences , 2010, Image Vis. Comput..

[2]  Won Suk Lee,et al.  Postharvest citrus mass and size estimation using a logistic classification model and a watershed algorithm , 2012 .

[3]  J Hodgkinson,et al.  Noise analysis for CCD-based ultraviolet and visible spectrophotometry. , 2015, Applied optics.

[4]  Gyeongsik Ok,et al.  High-speed terahertz imaging toward food quality inspection. , 2014, Applied optics.

[5]  Dah-Jye Lee,et al.  Automated apple stem end and calyx detection using evolution-constructed features , 2013 .

[6]  Umapada Pal,et al.  Multi-oriented touching text character segmentation in graphical documents using dynamic programming , 2012, Pattern Recognit..

[7]  Roland T. Chin,et al.  A one-pass thinning algorithm and its parallel implementation , 1987, Comput. Vis. Graph. Image Process..

[8]  Christoph H. Lampert,et al.  Attribute-Based Classification for Zero-Shot Visual Object Categorization , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Robert Ehrlich,et al.  Fourier grain-shape analysis: A new tool for sourcing and tracking abyssal silts , 1980 .

[10]  Mohammad Hammoudeh,et al.  Information extraction from sensor networks using the Watershed transform algorithm , 2015, Inf. Fusion.

[11]  Alexandre X. Falcão,et al.  Segmentation of sandstone thin section images with separation of touching grains using optimum path forest operators , 2013, Comput. Geosci..

[12]  A. Ramachandra Rao,et al.  Regionalization of watersheds by fuzzy cluster analysis , 2006 .

[13]  Joanna Isabelle Olszewska,et al.  Active contour based optical character recognition for automated scene understanding , 2015, Neurocomputing.

[14]  Amit K. Roy-Chowdhury,et al.  Re-Identification in the Function Space of Feature Warps , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Liangrui Peng,et al.  Exploring More Representative States of Hidden Markov Model in Optical Character Recognition: A Clustering-Based Model Pre-Training Approach , 2015, Int. J. Pattern Recognit. Artif. Intell..

[16]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  Jean Louchet,et al.  Using colour, texture, and hierarchial segmentation for high-resolution remote sensing , 2008 .

[18]  Lizhe Wang,et al.  Overview of Ecohydrological Models and Systems at the Watershed Scale , 2015, IEEE Systems Journal.

[19]  Zhen Zhang,et al.  Linking watershed-scale stream health and socioeconomic indicators with spatial clustering and structural equation modeling , 2015, Environ. Model. Softw..

[20]  Fang Cheng,et al.  Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier , 2016, Food Analytical Methods.

[21]  Dah-Jye Lee,et al.  Automatic shrimp shape grading using evolution constructed features , 2014 .

[22]  Ping Zhou,et al.  A novel segmentation algorithm for clustered slender-particles , 2009 .

[23]  Zhuqing Ding,et al.  Quality and Safety Inspection of Food and Agricultural Products by LabVIEW IMAQ Vision , 2015, Food Analytical Methods.

[24]  Mazdak Arabi,et al.  Computational Procedure for Evaluating Sampling Techniques on Watershed Model Calibration , 2015 .