Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 5

Digital Image Processing Related Abstracts

5 Sorting Fish by Hu Moments

Authors: E. Inzunza-González, E. E. García-Guerrero, J. M. Hernández-Ontiveros, O. R. López-Bonilla

Abstract:

This paper presents the implementation of an algorithm that identifies and accounts different fish species: Catfish, Sea bream, Sawfish, Tilapia, and Totoaba. The main contribution of the method is the fusion of the characteristics of invariance to the position, rotation and scale of the Hu moments, with the proper counting of fish. The identification and counting is performed, from an image under different noise conditions. From the experimental results obtained, it is inferred the potentiality of the proposed algorithm to be applied in different scenarios of aquaculture production.

Keywords: Pattern Recognition, Digital Image Processing, counting fish, invariant moments

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4 Study of Aerosol Deposition and Shielding Effects on Fluorescent Imaging Quantitative Evaluation in Protective Equipment Validation

Authors: Shinhao Yang, Hsiao-Chien Huang, Chin-Hsiang Luo

Abstract:

The leakage of protective clothing is an important issue in the occupational health field. There is no quantitative method for measuring the leakage of personal protective equipment. This work aims to measure the quantitative leakage of the personal protective equipment by using the fluorochrome aerosol tracer. The fluorescent aerosols were employed as airborne particulates in a controlled chamber with ultraviolet (UV) light-detectable stickers. After an exposure-and-leakage test, the protective equipment was removed and photographed with UV-scanning to evaluate areas, color depth ratio, and aerosol deposition and shielding effects of the areas where fluorescent aerosols had adhered to the body through the protective equipment. Thus, this work built a calculation software for quantitative leakage ratio of protective clothing based on fluorescent illumination depth/aerosol concentration ratio, illumination/Fa ratio, aerosol deposition and shielding effects, and the leakage area ratio on the segmentation. The results indicated that the two-repetition total leakage rate of the X, Y, and Z type protective clothing for subject T were about 3.05, 4.21, and 3.52 (mg/m2). For five-repetition, the leakage rate of T were about 4.12, 4.52, and 5.11 (mg/m2).

Keywords: Digital Image Processing, Deposition, Personal Protective Equipment, fluorochrome, shielding effects, leakage ratio

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3 A Neural Network Classifier for Estimation of the Degree of Infestation by Late Blight on Tomato Leaves

Authors: Gizelle K. Vianna, Gabriel V. Cunha, Gustavo S. Oliveira

Abstract:

Foliage diseases in plants can cause a reduction in both quality and quantity of agricultural production. Intelligent detection of plant diseases is an essential research topic as it may help monitoring large fields of crops by automatically detecting the symptoms of foliage diseases. This work investigates ways to recognize the late blight disease from the analysis of tomato digital images, collected directly from the field. A pair of multilayer perceptron neural network analyzes the digital images, using data from both RGB and HSL color models, and classifies each image pixel. One neural network is responsible for the identification of healthy regions of the tomato leaf, while the other identifies the injured regions. The outputs of both networks are combined to generate the final classification of each pixel from the image and the pixel classes are used to repaint the original tomato images by using a color representation that highlights the injuries on the plant. The new images will have only green, red or black pixels, if they came from healthy or injured portions of the leaf, or from the background of the image, respectively. The system presented an accuracy of 97% in detection and estimation of the level of damage on the tomato leaves caused by late blight.

Keywords: Artificial Neural Networks, Pattern Recognition, Digital Image Processing, phytosanitary

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2 Comparison of Classical Computer Vision vs. Convolutional Neural Networks Approaches for Weed Mapping in Aerial Images

Authors: Paulo Cesar Pereira Junior, Alexandre Monteiro, Rafael da Luz Ribeiro, Antonio Carlos Sobieranski, Aldo von Wangenheim

Abstract:

In this paper, we present a comparison between convolutional neural networks and classical computer vision approaches, for the specific precision agriculture problem of weed mapping on sugarcane fields aerial images. A systematic literature review was conducted to find which computer vision methods are being used on this specific problem. The most cited methods were implemented, as well as four models of convolutional neural networks. All implemented approaches were tested using the same dataset, and their results were quantitatively and qualitatively analyzed. The obtained results were compared to a human expert made ground truth for validation. The results indicate that the convolutional neural networks present better precision and generalize better than the classical models.

Keywords: Digital Image Processing, Precision Agriculture, Deep learning, Unmanned Aerial Vehicles, Convolutional Neural Networks, semantic segmentation

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1 An Analysis of the Relations between Aggregates’ Shape and Mechanical Properties throughout the Railway Ballast Service Life

Authors: Daianne Fernandes Diogenes

Abstract:

Railway ballast aggregates’ shape properties and size distribution can be directly affected by several factors, such as traffic, fouling, and maintenance processes, which cause breakage and wearing, leading to the fine particles’ accumulation through the ballast layer. This research aims to analyze the influence of traffic, tamping process, and sleepers’ stiffness on aggregates' shape and mechanical properties, by using traditional and digital image processing (DIP) techniques and cyclic tests, like resilient modulus (RM) and permanent deformation (PD). Aggregates were collected in different phases of the railway service life: (i) right after the crushing process; (ii) after construction, for the aggregates positioned below the sleepers and (iii) after 5 years of operation. An increase in the percentage of cubic particles was observed for the materials (ii) and (iii), providing a better interlocking, increasing stiffness and reducing axial deformation after 5 years of service, when compared to the initial conditions.

Keywords: Digital Image Processing, Mechanical Behavior, railway ballast, shape properties

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