An Algorithm Oriented to the Classification of Quinoa Grains by Color from Digital Images

2019 ◽  
pp. 237-247
Author(s):  
Moisés Quispe ◽  
José Arroyo ◽  
Guillermo Kemper ◽  
Jonell Soto
Keyword(s):  
Digital Images

Detection of Yellow Hawkweed (Hieracium pratense) with High Resolution Multispectral Digital Imagery

1995 ◽  
Vol 9 (3) ◽  
pp. 477-483 ◽  
Author(s):  
Hubert W. Carson ◽  
Lawrence W. Lass ◽  
Robert H. Callihan
Keyword(s):  
Supervised Classification ◽  
Digital Images ◽  
Ground Cover ◽  
Unsupervised Classification ◽  
Digital Imagery ◽  
Charge Coupled Device ◽  
Spectral Filters ◽  
A Charge ◽  
Northern Idaho

Yellow hawkweed infests permanent upland pastures and forest meadows in northern Idaho. Conventional surveys to determine infestations of this weed are not practical. A charge coupled device with spectral filters mounted in an airplane was used to obtain digital images (1 m resolution) of flowering yellow hawkweed. Supervised classification of the digital images predicted more area infested by yellow hawkweed than did unsupervised classification. Where yellow hawkweed was the dominant ground cover species, infestations were detectable with high accuracy from digital images. Moderate yellow hawkweed infestation detection was unreliable, and areas having less than 20% yellow hawkweed cover were not detected.

Adaptive Median Filtering Based on Unsupervised Classification of Pixels

2013 ◽  
pp. 273-296 ◽  
Author(s):  
J. K. Mandal ◽  
Somnath Mukhopadhyay
Keyword(s):  
Clustering Algorithm ◽  
Digital Images ◽  
Unsupervised Classification ◽  
Median Filtering ◽  
Adaptive Window ◽  
Novel Approach ◽  
Weighted Median ◽  
Window Selection ◽  
Filtering Approach

This chapter deals with a novel approach which aims at detection and filtering of impulses in digital images through unsupervised classification of pixels. This approach coagulates directional weighted median filtering with unsupervised pixel classification based adaptive window selection toward detection and filtering of impulses in digital images. K-means based clustering algorithm has been utilized to detect the noisy pixels based adaptive window selection to restore the impulses. Adaptive median filtering approach has been proposed to obtain best possible restoration results. Results demonstrating the effectiveness of the proposed technique are provided for numeric intensity values described in terms of feature vectors. Various benchmark digital images are used to show the restoration results in terms of PSNR (dB) and visual effects which conform better restoration of images through proposed technique.

scholarly journals The affective notes of represented space as motors of emotional and sensorial response

2019 ◽  
Vol 64 ◽  
pp. 01013 ◽  
Author(s):  
Elisa Morselli
Keyword(s):  
Design Research ◽  
Digital Images ◽  
Space Perception ◽  
Specific Kind ◽  
Emotional Aspects ◽  
Contemporary Architecture ◽  
Psychology Of Art

The following essay proposes to investigate the perceptual and emotional aspects related to the visualization of architectural images. The field of research is limited to a well-defined category: figurative representations as the photographic and digital images of contemporary architecture. In particular, two types will be analysed: the un-built architecture produced by Studio MIR and Bloomimages compared with the photographed built architecture. Using figurative images as a tool of reading, the aim of this work is to identify and classify three types of affective spaces capable of generating a specific kind of perception, producing a sensorial classification of atmosphere for architecture. The study of the Psychology of Art, as well as Aesthetics and Neuroaesthetics can be a valuable tool in understanding the phenomena of the present, considering the marked pictoriality of these images. The application of the analytic methodology, developed in these disciplines, can suggest a new way of "looking" at the project, paying attention to the representation of the atmospheres, which characterizes the experience of felt space. Keywords: Affective Space, Perception, Representation, Aesthetics, Atmospheres, Design Research, Generators.

scholarly journals Neural Classification of Compost Maturity by Means of the Self-Organising Feature Map Artificial Neural Network and Learning Vector Quantization Algorithm

2019 ◽  
Vol 16 (18) ◽  
pp. 3294 ◽  
Author(s):  
Piotr Boniecki ◽  
Małgorzata Idzior-Haufa ◽  
Agnieszka Pilarska ◽  
Krzysztof Pilarski ◽  
Alicja Kolasa-Wiecek
Keyword(s):  
Vector Quantization ◽  
Digital Images ◽  
Neural Model ◽  
Learning Vector Quantization ◽  
Neural Models ◽  
Topological Map ◽  
Compost Maturity ◽  
Feature Map ◽  
Graphic Information

Self-Organising Feature Map (SOFM) neural models and the Learning Vector Quantization (LVQ) algorithm were used to produce a classifier identifying the quality classes of compost, according to the degree of its maturation within a period of time recorded in digital images. Digital images of compost at different stages of maturation were taken in a laboratory. They were used to generate an SOFM neural topological map with centres of concentration of the classified cases. The radial neurons on the map were adequately labelled to represent five suggested quality classes describing the degree of maturation of the composted organic matter. This enabled the creation of a neural separator classifying the degree of compost maturation based on easily accessible graphic information encoded in the digital images. The research resulted in the development of original software for quick and easy assessment of compost maturity. The generated SOFM neural model was the kernel of the constructed IT system.

scholarly journals Denoising of digital images through PSO based pixel classification

2013 ◽  
Vol 3 (4) ◽  
Author(s):  
Somnath Mukhopadhyay ◽  
Jyotsna Mandal
Keyword(s):  
Digital Images ◽  
Fitness Function ◽  
Unsupervised Classification ◽  
Noisy Image ◽  
Pixel Classification ◽  
Visual Effects ◽  
Swarm Optimization ◽  
Image Pixels ◽  
Noise Models

AbstractThis paper proposes a de-noising method where the detection and filtering is based on unsupervised classification of pixels. The noisy image is grouped into subsets of pixels with respect to their intensity values and spatial distances. Using a novel fitness function the image pixels are classified using the Particle Swarm Optimization (PSO) technique. The distance function measured similarity/dissimilarity among pixels using not only the intensity values, but also the positions of the pixels. The detection technique enforced PSO based clustering, which is very simple and robust. The filtering operator restored only the noisy pixels keeping noise free pixels intact. Four types of noise models are used to train the digital images and these noisy images are restored using the proposed algorithm. Results demonstrated the effectiveness of the proposed technique. Various benchmark images are used to produce restoration results in terms of PSNR (dB) along with other parametric values. Some visual effects are also presented which conform better restoration of digital images through the proposed technique.

Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

2019 ◽  
pp. 715-722
Author(s):  
Juan V. Lorenzo-Ginori ◽  
Lyanett Chinea-Valdés ◽  
Yanela IzquierdoTorres ◽  
Rubén Orozco-Morales ◽  
Niurka Mollineda-Diogo ◽  
...  
Keyword(s):  
Plasmodium Berghei ◽  
Digital Images ◽  
Features Extraction ◽  
Color Features

scholarly journals Variable Selection Towards Classification of Digital Images: Identification of Altered Glucose Levels in Serum

2019 ◽  
Vol 52 (14) ◽  
pp. 2239-2250 ◽  
Author(s):  
Camilo L. M. Morais ◽  
Kássio M. G. Lima ◽  
Francis L. Martin
Keyword(s):  
Variable Selection ◽  
Digital Images ◽  
Glucose Levels ◽  
Altered Glucose

Classification of extra virgin olive oil and verification of adulteration using digital images and discriminant analysis

2015 ◽  
Vol 7 (20) ◽  
pp. 8839-8846 ◽  
Author(s):  
Karla Danielle Tavares de Melo Milanez ◽  
Márcio José Coelho Pontes
Keyword(s):  
Pattern Recognition ◽  
Discriminant Analysis ◽  
Soybean Oil ◽  
Olive Oil ◽  
Digital Images ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Pattern Recognition Methods

This work proposes a new methodology based on digital images and supervised pattern recognition methods for the classification of extra virgin olive oil (EVOO) samples with respect to brand (A, B and C) and verification of adulteration with soybean oil.

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