88 research outputs found
Real-time Detection of Young Spruce Using Color and Texture Features on an Autonomous Forest Machine
Forest machines are manually operated machines that are efficient when operated by a professional. Point cleaning is a silvicultural task in which weeds are removed around a young spruce tree. To automate point cleaning, machine vision methods are used for identifying spruce trees. A texture analysis method based on the Radon and wavelet transforms is implemented for the task. Real-time GPU implementation of algorithms is programmed using CUDA framework. Compared to a single thread CPU implementation, our GPU implementation is between 18 to 80 times faster depending on the size of image blocks used. Color information is used in addition of texture and a location estimate of the tree is extracted from the detection result. The developed spruce detection system is used as a part of an autonomous point cleaning machine. To control the system, an integrated user interface is presented. It allows the operator to control, monitor and train the system online.Peer reviewe
SVM and ANN Based Classification of Plant Diseases Using Feature Reduction Technique
Computers have been used for mechanization and
automation in different applications of agriculture/horticulture.
The critical decision on the agricultural yield and plant protection
is done with the development of expert system (decision support
system) using computer vision techniques. One of the areas
considered in the present work is the processing of images of
plant diseases affecting agriculture/horticulture crops. The first
symptoms of plant disease have to be correctly detected, identified,
and quantified in the initial stages. The color and texture features
have been used in order to work with the sample images of plant
diseases. Algorithms for extraction of color and texture features
have been developed, which are in turn used to train support
vector machine (SVM) and artificial neural network (ANN)
classifiers. The study has presented a reduced feature set based
approach for recognition and classification of images of plant
diseases. The results reveal that SVM classifier is more suitable
for identification and classification of plant diseases affecting
agriculture/horticulture crops
Weed/Plant Classification Using Evolutionary Optimised Ensemble Based On Local Binary Patterns
This thesis presents a novel pixel-level weed classification through rotation-invariant uniform local binary pattern (LBP) features for precision weed control. Based on two-level optimisation structure; First, Genetic Algorithm (GA) optimisation to select the best rotation-invariant uniform LBP configurations; Second, Covariance Matrix Adaptation Evolution Strategy (CMA-ES) in the Neural Network (NN) ensemble to select the best combinations of voting weights of the predicted outcome for each classifier. The model obtained 87.9% accuracy in CWFID public benchmark
On the Use of the Kantorovich-Rubinstein Distance for Dimensionality Reduction
The goal of this thesis is to study the use of the Kantorovich-Rubinstein
distance as to build a descriptor of sample complexity in classification
problems. The idea is to use the fact that the Kantorovich-Rubinstein distance
is a metric in the space of measures that also takes into account the geometry
and topology of the underlying metric space. We associate to each class of
points a measure and thus study the geometrical information that we can obtain
from the Kantorovich-Rubinstein distance between those measures. We show that a
large Kantorovich-Rubinstein distance between those measures allows to conclude
that there exists a 1-Lipschitz classifier that classifies well the classes of
points. We also discuss the limitation of the Kantorovich-Rubinstein distance
as a descriptor.Comment: 214 pages, 0 figures, This is a PhD thesis in mathematics under the
supervision of Dr. Vladimir Pestov and Dr. George Wells submitted on May 1,
2023 at the University of Ottaw
Blur Invariants for Image Recognition
Blur is an image degradation that is difficult to remove. Invariants with
respect to blur offer an alternative way of a~description and recognition of
blurred images without any deblurring. In this paper, we present an original
unified theory of blur invariants. Unlike all previous attempts, the new theory
does not require any prior knowledge of the blur type. The invariants are
constructed in the Fourier domain by means of orthogonal projection operators
and moment expansion is used for efficient and stable computation. It is shown
that all blur invariants published earlier are just particular cases of this
approach. Experimental comparison to concurrent approaches shows the advantages
of the proposed theory.Comment: 15 page
Sustainable Agriculture and Advances of Remote Sensing (Volume 1)
Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others
Gender classification using facial components.
Master’s degree. University of KwaZulu-Natal, Durban.Gender classification is very important in facial analysis as it can be used as input
into a number of systems such as face recognition. Humans are able to classify gender
with great accuracy however passing this ability to machines is a complex task
because of many variables such as lighting to mention a few. For the purpose of this
research we have approached gender classification as a binary problem, involving
the two classes male and female. Two datasets are used in this research which are
the FG-NET dataset and Pilots Parliament datasets. Two appearance based feature
extractors are used which are the LBP and LDP with the Active Shape model being
included by fusing. The classifiers used here are the Support Vector Machine with
Radial Basis Function kernel and an Artificial Neural Network with backpropagation.
On the FG-NET an average detection of 90.6% against that of 87.5% to that of
the PPB. Gender is then detected from the facial components the nose, eyes among
others. The forehead recorded the highest accuracy with 92%, followed by the nose
with 90%, cheeks with 89.2% and the eyes with 87% and the mouth recorded the
lowest accuracy of 75%. As a result feature fusion is then carried out to improve
classification accuracies especially that of the mouth and eyes with lowest accuracies.
The eyes with an accuracy of 87% is fused with the forehead with 92% and the
resulting accuracy is an increase to 93%. The mouth, with the lowest accuracy of 75%
is fused with the nose which has an accuracy of 90% and the resulting accuracy is
87%. These results carried out by fusing through addition showed improved results.
Fusion is then carried out between Appearance based and shape based features. On
the FG-NET dataset using the LBP and LDP an accuracy of 85.33% and 89.53% with
the PPB recording 83.13%, 89.3% for LBP and LDP respectively. As expected and
shown by previous researchers the LDP clearly obtains higher classification accuracies
as it than LBP as it uses gradient rather than pixel intensity. We then fuse the
vectors of the LDP, LBP with that of the ASM and carry out dimensionality reduction,
then fusion by addition. On the PPB dataset fusion of LDP and ASM records
81.56%, and 94.53% with the FG-NET recording 89.53% respectively
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