A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow

Teimouri, Nima; Omid, Mahmoud; Mollazade, Kaveh; Rajabipour, Ali

doi:10.1016/j.compag.2014.04.008

Cited by 35 publications

(22 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A variety of methods exist for distinguishing the target from the background, but the possibility of misclassification always exists (Teimouri et al, 2014), and even a small misclassification rate can affect the results. With contact imaging, however, there is no need to remove the background, and the entire image can be used as an input data.…”

Section: Image Acquisition and Spad Determinationmentioning

confidence: 99%

“…Therefore a feature selection method was applied to select the best features to estimate chlorophyll content (SPAD value) of leaves. Numerous statistical methods to find superior features exist, such as LASSO, stepwise regression, wrapper methods, sensitivity analysis, and correlation-based feature selection (Saeys et al, 2007;Mollazade et al, 2012;Lee and Lee, 2013;Teimouri et al, 2014). In this study to develop a linear model we used stepwise regression to choose the most reliable and effective features.…”

Section: Feature Extractionmentioning

confidence: 99%

“…Sensitivity analysis attempts to provide a ranking of the model inputs based on their relative contributions to model output variability and uncertainty. For calculating the sensitivities, each input (feature vector) was shifted slightly and the corresponding change in the output was computed (Teimouri et al, 2014). The number of hidden layers was minimised based on the observation that one hidden layer neural network can approximate any function that contains a continuous mapping from one finite space to another.…”

Section: Neural Network Modellingmentioning

confidence: 99%

See 2 more Smart Citations

Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Vesali

Omid

Kaleita

et al. 2015

Computers and Electronics in Agriculture

Self Cite

View full text Add to dashboard Cite

Section: Image Acquisition and Spad Determinationmentioning

confidence: 99%

Section: Feature Extractionmentioning

confidence: 99%

Section: Neural Network Modellingmentioning

confidence: 99%

See 1 more Smart Citation

Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Vesali

Omid

Kaleita

et al. 2015

Computers and Electronics in Agriculture

Self Cite

View full text Add to dashboard Cite

“…Huang (2012) applied ANN and image processing techniques for detecting and classifying the quality of areca nuts. Teimouri et al (2014) used a novel ANN assisted segmentation algorithm for discriminating almond nut and shell from background and shadow, classifying the images into three categories. Omid et al (2009) developed and tested an intelligent pistachio nut sorting system combining acoustic emissions analysis, principal component analysis and a multilayer feed-forward neural network.…”

Section: Introductionmentioning

confidence: 99%

Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks

Guiné

Almeida²,

Correia

et al. 2015

Food Bioprocess Technol

View full text Add to dashboard Cite

The present work assessed the influence of different factors on some physical and chemical properties of nuts. The factors evaluated were the presence or absence of the inner skin, geographical origin, storage conditions (ambient temperature, in a stove at 30 and 50°C, in a chamber at 30 and 50°C and 90 % RH, refrigerated and freezing) and type of package (none, low density polyethylene and low density polyethylene). The fruits studied were almonds, hazelnuts and walnuts from different countries. The properties measured were moisture content, water activity, colour coordinates (L*, a* and b*) and texture parameters (hardness and friability). Experimental data were modelled using neural networks. The results showed that the almonds from Spain and Romania had a w greater than 0.6, and therefore, its stability was not guaranteed, contrarily to the other samples that presented values of a w lower than 0.6. The colour coordinate lightness varied from 40.60 to 49.30 in the fresh samples but decreased during storage, indicating darkening. In general, an increase in hardness and friability was observed with the different storage conditions. Neuron weight analysis has shown that the origin was a good predictor for moisture content and texture; whereas, the storage condition was a good predictor for a w and colour. In conclusion, it was possible to verify that the properties of nuts are very different depending on origin; they are better preserved at lower temperatures and the type of package used did not impact the properties studied.

show abstract

“…With the development of artificial neural network, fuzzy set theory and graph theory, some novel segmentation algorithms are proposed combined with these theories [7][8]. Image segmentation based on partial differential equations is an important branch in the field of image segmentation [9][10].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Ultrasonic Images Based on Modified Chan-Vese algorithm

Wang¹,

Fan²

2016

Proceedings of the 6th International Conference on Electronic, Mechanical, Information and Management Society

View full text Add to dashboard Cite

Abstract. The primary ultrasonic image segmentation goal is to partition a given ultrasonic image into different regions representing anatomical structures. Ultrasonic image segmentation is very important because the accurate representation of object and background fluid provides a way to identify many disease. In this paper, a modified Chan-Vese model is proposed for image segmentation, which is based on the similarity between each point and center point in the neighborhood. This model can capture the details of local region to realize the image segmentation in gray-level heterogeneous area. Experimental results show that this method can segment the ultrasonic image with high accuracy, adapt ability and more stable performance compared with the traditional Chan-Vese model.

show abstract

A novel artificial neural networks assisted segmentation algorithm for discriminating almond nut and shell from background and shadow

Cited by 35 publications

References 17 publications

Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging

Modelling the Influence of Origin, Packing and Storage on Water Activity, Colour and Texture of Almonds, Hazelnuts and Walnuts Using Artificial Neural Networks

Segmentation of Ultrasonic Images Based on Modified Chan-Vese algorithm

Contact Info

Product

Resources

About