Comparison of three methods for classification of pizza topping using different colour space transformations

Du, Cheng-Jin; Sun, Da‐Wen

doi:10.1016/j.jfoodeng.2004.05.044

Cited by 127 publications

(48 citation statements)

References 22 publications

(28 reference statements)

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“…The image is represented in RGB color as a primary color model. The threedimensional color spaces are normally discretised to 256 levels per axis, which gives over 1.67 million distinct colors (Du & Sun, 2005).…”

Section: Rgb Color Spacementioning

confidence: 99%

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Ganganagowdar

Siddaramappa

2016

Acta Sci. Agron.

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ABSTRACT.A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.Keywords: White Wholes (WW) grade cashew kernel images, feature extraction, artificial neural networks, classification.Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial RESUMO. Analisa-se um novo modelo automatizado para detectar e classificar castanhas de caju por uma rede neural artificial. O modelo funciona em duas frases. Fase 1 é construída para detectar as características, as quais incluem 16 fatores morfológicos e 24 características de cor a partir de imagens da castanha do caju. Na Fase 2 emprega-se a rede neural artificial multicamada para detectar e classificar os graus inteiros brancos por algoritmo de propagação reversa. O método classifica com uma exatidão de 88,93%. Essa investigação também revela que a combinação das características morfológicas e de cor vai além de que quando se usa uma série de características separadas para classificar as castanhas de caju.Palavras-chave: imagens de castanhas de caju serie brancos inteiros, características de extração, rede neural artificial, classificação.

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Section: Rgb Color Spacementioning

confidence: 99%

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Ganganagowdar

Siddaramappa

2016

Acta Sci. Agron.

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“…The studies described in [13][14][15] show applications of color images for the assessment of food quality. Recently, HSI systems have been considered as robust tools for processing and evaluating food products, for example, classifying or predicting attributes related to meat quality and safety.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A total number of 75 samples, including lamb (18), beef (13), pork (13), and fat (31), were prepared (i.e., cutting and then drying with normal tissue). Then, the samples were labeled and kept at 2 • C for 16 h. The next day, the samples were taken from the fridge and put into well-designed containers (frames shaped as a matrix of meat and fat species; Figure 3a shows examples of these frames).…”

Section: Dataset and Sample Preparationmentioning

confidence: 99%

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

et al. 2018

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This paper provides a comprehensive analysis of the performance of hyperspectral imaging for detecting adulteration in red-meat products. A dataset of line-scanning images of lamb, beef, or pork muscles was collected taking into account the state of the meat (fresh, frozen, thawed, and packing and unpacking the sample with a transparent bag). For simulating the adulteration problem, meat muscles were defined as either a class of lamb or a class of beef or pork. We investigated handcrafted spectral and spatial features by using the support vector machines (SVM) model and self-extraction spectral and spatial features by using a deep convolution neural networks (CNN) model. Results showed that the CNN model achieves the best performance with a 94.4% overall classification accuracy independent of the state of the products. The CNN model provides a high and balanced F-score for all classes at all stages. The resulting CNN model is considered as being simple and fairly invariant to the condition of the meat. This paper shows that hyperspectral imaging systems can be used as powerful tools for rapid, reliable, and non-destructive detection of adulteration in red-meat products. Also, this study confirms that deep-learning approaches such as CNN networks provide robust features for classifying the hyperspectral data of meat products; this opens the door for more research in the area of practical applications (i.e., in meat processing).

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“…For this work, colour classification is primarily achieved within the Hue-Saturation-Value (HSV) colourspace, which is commonly used for image classification [21]. Unlike Red-Green-Blue (RGB), HueSaturation-Value (HSV)is designed to make human interpretation easier, separating colour data into channels reflecting how human vision functions.…”

Section: Colour Classificationmentioning

confidence: 99%

Machine Vision for UAS Ground Operations

Coombes

Eaton

Chen

2017

J Intell Robot Syst

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This paper discusses the machine vision element of a system designed to allow Unmanned Aerial System (UAS) to perform automated taxiing around civil aerodromes, with only a monocular camera. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detecting potential collision risks. In practice, untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are used to estimate the class. As the competency of each individual estimate can vary dependent on multiple factors (number of pixels, lighting conditions and even surface type). A Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for localisation and obstacle detection.M. Coombes ( ) · W. Eaton

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Comparison of three methods for classification of pizza topping using different colour space transformations

Cited by 127 publications

References 22 publications

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Detection of Red-Meat Adulteration by Deep Spectral–Spatial Features in Hyperspectral Images

Machine Vision for UAS Ground Operations

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