Discrimination of sunflower, weed and soil by artificial neural networks

Kavdır, İsmail

doi:10.1016/j.compag.2004.03.006

Cited by 41 publications

(24 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The use of filter banks for feature extraction of textures has been motivated by their ability to be tuned to many diverse applications [22][35] [42]. Their utility has allowed for a wide spread use in computer vision applications with many high-quality results.…”

Section: Filter Banksmentioning

confidence: 99%

A Compression Based Distance Measure for Texture

Campana

Keogh

2010

Proceedings of the 2010 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

The analysis of texture is an important subroutine in application areas as diverse as biology, medicine, robotics, and forensic science. While the last three decades have seen extensive research in algorithms to measure texture similarity, almost all existing methods require the careful setting of many parameters. There are many problems associated with a surfeit of parameters, the most obvious of which is that with many parameters to fit, it is exceptionally difficult to avoid over fitting. In this work we propose to extend recent advances in Kolmogorov complexity-based similarity measures to texture matching problems. These Kolmogorov based methods have been shown to be very useful in intrinsically discrete domains such as DNA, protein sequences, MIDI music and natural languages; however, they are not well defined for realvalued data. Towards this, we introduce the CampanaKeogh (CK) video compression based method for texture measures. These measures utilize state-of-theart video compressors to approximate the Kolmogorov complexity. Using the CK method, we create an efficient and robust parameter-free texture similarity measure, the CK-1 distance measure. We demonstrate the utility of our measure with an extensive empirical evaluation on real-world case studies drawn from nematology, arachnology, entomology, medicine, forensics, ecology, and several well known texture analysis benchmarks.

show abstract

Section: Filter Banksmentioning

confidence: 99%

A Compression Based Distance Measure for Texture

Campana

Keogh

2010

Proceedings of the 2010 SIAM International Conference on Data Mining

View full text Add to dashboard Cite

show abstract

“…Two variables were selected to optimize the classifier's performance on cross-validation data: scaling factor of the kernel function "σ", and "C" to control the soft margin between classes and the hyper plane. We geometrically varied the values for these parameters such that each value for σ is tested in combination with each value of C. Following is the batch represents the options to select the value of these parameters; Batch = {.01 .02 .05 .08 .1 .2 .4 .6 .8 1 1.2 1.5 1.8 2 5 10 15 20 30 40 50 70 80 100} (9) The classifier is optimized by using the cross-validation data, in the following three ways independently: • Achieving the maximum sensitivity regardless of specificity or classification accuracy…”

Section: B Support Vector Machinesmentioning

confidence: 99%

“…Among them, Artificial Neural Network (ANN) has shown potential for resolving problems in estimating a mathematical relationship where some inputs and their corresponding target outputs are known [12][13][14]. Extensive work has been carried out employing this technique 253 | P a g e www.ijacsa.thesai.org by using several kinds of features including statistical, spectral, texture and color features [15][16][17][18][19][20][21][22][23]. Support Vector Machine (SVM) is another state of the art technique, used in binary classification problems [24][25].…”

Section: Introductionmentioning

confidence: 99%

A Machine Vision System for Quality Inspection of Pine Nuts

Khosa¹,

Pasero²

2016

ijacsa

View full text Add to dashboard Cite

Abstract-Computers and artificial intelligence have penetrated in the food industry since last decade, for intellectual automatic processing and packaging in general, and in assisting for quality inspection of the food itself in particular. The food quality assessment task becomes more challenging when it is about harmless internal examination of the ingredient, and even more when its size is also minute. In this article, a method for automatic detection, extraction and classification of raw food item is presented using x-ray image data of pine nuts. Image processing techniques are employed in developing an efficient method for automatic detection and then extraction of individual ingredient, from the source x-ray image which comprises bunch of nuts in a single frame. For data representation, statistical texture analysis is carried out and attributes are calculated from each of the sample image on the global level as features. In addition co-occurrence matrices are computed from images with four different offsets, and hence more features are extracted by using them. To find fewer meaningful characteristics, all the calculated features are organized in several combinations and then tested. Seventy percent of image data is used for training and 15% each for crossvalidation and test purposes. Binary classification is performed using two state-of-the-art non-linear classifiers: Artificial Neural Network (ANN) and Support Vector Machines (SVM). Performance is evaluated in terms of classification accuracy, specificity and sensitivity. ANN classifier showed 87.6% accuracy with correct recognition rate of healthy nuts and unhealthy nuts as 94% and 62% respectively. SVM classifier produced the similar accuracy achieving 86.3% specificity and 89.2% sensitivity rate. The results obtained are unique itself in terms of ingredient and promising relatively. It is also found that feature set size can be reduced up to 57% by compromising 3.5% accuracy, in combination with any of the tested classifiers.

show abstract

“…ANN models are flexible function approximators to describe nonlinear systems (Zhang and Barrion, 2006), make no a priori assumptions on the type or statistical distribution of data, and, thus, can be used for pattern recognition on practically any kind of multivariate data sets (Do et al, 1999;Moore and Miller, 2002;Clark, 2003;Kavdır, 2004;Marini et al, 2004;Aldrich et al, 2007;Vaňhara et al, 2007;Fedor et al, 2008Fedor et al, , 2009Esteban et al, 2009;Muráriková et al, 2011;Bilgili, 2011;Tohidi et al, 2012). Their use now spans all fields of science, including a wide variety of applied branches, such as pest control in agriculture and forestry.…”

Section: Introductionmentioning

confidence: 99%