15 research outputs found
Indian Monuments Classification using Support Vector Machine
Recently, Content-Based Image Retrieval is a widely popular and efficient searching and indexing approach used by knowledge seekers. Use of images by e-commerce sites, by product and by service industries is not new nowadays. Travel and tourism are the largest service industries in India. Every year people visit tourist places and upload pictures of their visit on social networking sites or share via the mobile device with friends and relatives. Classification of the monuments is helpful to hoteliers for the development of a new hotel with state of the art amenities, to travel service providers, to restaurant owners, to government agencies for security, etc.. The proposed system had extracted features and classified the Indian monuments visited by the tourists based on the linear Support Vector Machine (SVM). The proposed system was divided into 3 main phases: preprocessing, feature vector creation and classification. The extracted features are based on Local Binary Pattern, Histogram, Co-occurrence Matrix and Canny Edge Detection methods. Once the feature vector had been constructed, classification was performed using Linear SVM. The Database of 10 popular Indian monuments was generated with 50 images for each class. The proposed system is implemented in MATLAB and achieves very high accuracy. The proposed system was also tested on other popular benchmark databases
Hierarchical age estimation using enhanced facial features.
Doctor of Philosopy in Computer Science, University of KwaZulu-Natal, Westville, 2018.Ageing is a stochastic, inevitable and uncontrollable process that constantly affect
shape, texture and general appearance of the human face. Humans can easily determine
ones’ gender, identity and ethnicity with highest accuracy as compared to
age. This makes development of automatic age estimation techniques that surpass
human performance an attractive yet challenging task. Automatic age estimation
requires extraction of robust and reliable age discriminative features. Local binary
patterns (LBP) sensitivity to noise makes it insufficiently reliable in capturing age
discriminative features. Although local ternary patterns (LTP) is insensitive to noise,
it uses a single static threshold for all images regardless of varied image conditions.
Local directional patterns (LDP) uses k directional responses to encode image gradient
and disregards not only central pixel in the local neighborhood but also 8 k
directional responses. Every pixel in an image carry subtle information. Discarding
8 k directional responses lead to lose of discriminative texture features. This
study proposes two variations of LDP operator for texture extraction. Significantorientation
response LDP (SOR-LDP) encodes image gradient by grouping eight
directional responses into four pairs. Each pair represents orientation of an edge
with respect to central reference pixel. Values in each pair are compared and the
bit corresponding to the maximum value in the pair is set to 1 while the other is
set to 0. The resultant binary code is converted to decimal and assigned to the central
pixel as its’ SOR-LDP code. Texture features are contained in the histogram of
SOR-LDP encoded image. Local ternary directional patterns (LTDP) first gets the
difference between neighboring pixels and central pixel in 3 3 image region. These
differential values are convolved with Kirsch edge detectors to obtain directional
responses. These responses are normalized and used as probability of an edge occurring
towards a respective direction. An adaptive threshold is applied to derive
LTDP code. The LTDP code is split into its positive and negative LTDP codes. Histograms
of negative and positive LTDP encoded images are concatenated to obtain
texture feature. Regardless of there being evidence of spatial frequency processing
in primary visual cortex, biologically inspired features (BIF) that model visual cortex
uses only scale and orientation selectivity in feature extraction. Furthermore,
these BIF are extracted using holistic (global) pooling across scale and orientations
leading to lose of substantive information. This study proposes multi-frequency BIF
(MF-BIF) where frequency selectivity is introduced in BIF modelling. Local statistical
BIF (LS-BIF) uses local pooling within scale, orientation and frequency in n n
region for BIF extraction. Using Leave-one-person-out (LOPO) validation protocol,
this study investigated performance of proposed feature extractors in age estimation
in a hierarchical way by performing age-group classification using Multi-layer
Perceptron (MLP) followed by within age-group exact age regression using support
vector regression (SVR). Mean absolute error (MAE) and cumulative score (CS) were
used to evaluate performance of proposed face descriptors. Experimental results on
FG-NET ageing dataset show that SOR-LDP, LTDP, MF-BIF and LS-BIF outperform
state-of-the-art feature descriptors in age estimation. Experimental results show that
performing gender discrimination before age-group and age estimation further improves
age estimation accuracies. Shape, appearance, wrinkle and texture features
are simultaneously extracted by visual system in primates for the brain to process
and understand an image or a scene. However, age estimation systems in the literature
use a single feature for age estimation. A single feature is not sufficient enough
to capture subtle age discriminative traits due to stochastic and personalized nature
of ageing. This study propose fusion of different facial features to enhance their
discriminative power. Experimental results show that fusing shape, texture, wrinkle
and appearance result into robust age discriminative features that achieve lower
MAE compared to single feature performance
Advancing the technology of sclera recognition
PhD ThesisEmerging biometric traits have been suggested recently to overcome
some challenges and issues related to utilising traditional human
biometric traits such as the face, iris, and fingerprint. In particu-
lar, iris recognition has achieved high accuracy rates under Near-
InfraRed (NIR) spectrum and it is employed in many applications for
security and identification purposes. However, as modern imaging
devices operate in the visible spectrum capturing colour images, iris
recognition has faced challenges when applied to coloured images
especially with eye images which have a dark pigmentation. Other
issues with iris recognition under NIR spectrum are the constraints on
the capturing process resulting in failure-to-enrol, and degradation in
system accuracy and performance. As a result, the research commu-
nity investigated using other traits to support the iris biometric in the
visible spectrum such as the sclera.
The sclera which is commonly known as the white part of the eye
includes a complex network of blood vessels and veins surrounding
the eye. The vascular pattern within the sclera has different formations
and layers providing powerful features for human identification. In
addition, these blood vessels can be acquired in the visible spectrum
and thus can be applied using ubiquitous camera-based devices. As a
consequence, recent research has focused on developing sclera recog-
nition. However, sclera recognition as any biometric system has issues
and challenges which need to be addressed. These issues are mainly
related to sclera segmentation, blood vessel enhancement, feature ex-
traction, template registration, matching and decision methods. In
addition, employing the sclera biometric in the wild where relaxed
imaging constraints are utilised has introduced more challenges such
as illumination variation, specular reflections, non-cooperative user
capturing, sclera blocked region due to glasses and eyelashes, variation
in capturing distance, multiple gaze directions, and eye rotation.
The aim of this thesis is to address such sclera biometric challenges
and highlight the potential of this trait. This also might inspire further
research on tackling sclera recognition system issues. To overcome the
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above-mentioned issues and challenges, three major contributions are
made which can be summarised as 1) designing an efficient sclera
recognition system under constrained imaging conditions which in-
clude new sclera segmentation, blood vessel enhancement, vascular
binary network mapping and feature extraction, and template registra-
tion techniques; 2) introducing a novel sclera recognition system under
relaxed imaging constraints which exploits novel sclera segmentation,
sclera template rotation alignment and distance scaling methods, and
complex sclera features; 3) presenting solutions to tackle issues related
to applying sclera recognition in a real-time application such as eye
localisation, eye corner and gaze detection, together with a novel image
quality metric.
The evaluation of the proposed contributions is achieved using five
databases having different properties representing various challenges
and issues. These databases are the UBIRIS.v1, UBIRIS.v2, UTIRIS,
MICHE, and an in-house database. The results in terms of segmen-
tation accuracy, Equal Error Rate (EER), and processing time show
significant improvement in the proposed systems compared to state-
of-the-art methods.Ministry of Higher Education and
Scientific Research in Iraq and the Iraqi Cultural Attach´e in Londo
Local Binary Pattern based algorithms for the discrimination and detection of crops and weeds with similar morphologies
In cultivated agricultural fields, weeds are unwanted species that compete with the crop plants for nutrients, water, sunlight and soil, thus constraining their growth. Applying new real-time weed detection and spraying technologies to agriculture would enhance current farming practices, leading to higher crop yields and lower production costs. Various weed detection methods have been developed for Site-Specific Weed Management (SSWM) aimed at maximising the crop yield through efficient control of weeds. Blanket application of herbicide chemicals is currently the most popular weed eradication practice in weed management and weed invasion. However, the excessive use of herbicides has a detrimental impact on the human health, economy and environment. Before weeds are resistant to herbicides and respond better to weed control strategies, it is necessary to control them in the fallow, pre-sowing, early post-emergent and in pasture phases. Moreover, the development of herbicide resistance in weeds is the driving force for inventing precision and automation weed treatments. Various weed detection techniques have been developed to identify weed species in crop fields, aimed at improving the crop quality, reducing herbicide and water usage and minimising environmental impacts.
In this thesis, Local Binary Pattern (LBP)-based algorithms are developed and tested experimentally, which are based on extracting dominant plant features from camera images to precisely detecting weeds from crops in real time. Based on the efficient computation and robustness of the first LBP method, an improved LBP-based method is developed based on using three different LBP operators for plant feature extraction in conjunction with a Support Vector Machine (SVM) method for multiclass plant classification. A 24,000-image dataset, collected using a testing facility under simulated field conditions (Testbed system), is used for algorithm training, validation and testing. The dataset, which is published online under the name “bccr-segset”, consists of four subclasses: background, Canola (Brassica napus), Corn (Zea mays), and Wild radish (Raphanus raphanistrum). In addition, the dataset comprises plant images collected at four crop growth stages, for each subclass. The computer-controlled Testbed is designed to rapidly label plant images and generate the “bccr-segset” dataset. Experimental results show that the classification accuracy of the improved LBP-based algorithm is 91.85%, for the four classes.
Due to the similarity of the morphologies of the canola (crop) and wild radish (weed) leaves, the conventional LBP-based method has limited ability to discriminate broadleaf crops from weeds. To overcome this limitation and complex field conditions (illumination variation, poses, viewpoints, and occlusions), a novel LBP-based method (denoted k-FLBPCM) is developed to enhance the classification accuracy of crops and weeds with similar morphologies. Our contributions include (i) the use of opening and closing morphological operators in pre-processing of plant images, (ii) the development of the k-FLBPCM method by combining two methods, namely, the filtered local binary pattern (LBP) method and the contour-based masking method with a coefficient k, and (iii) the optimal use of SVM with the radial basis function (RBF) kernel to precisely identify broadleaf plants based on their distinctive features. The high performance of this k-FLBPCM method is demonstrated by experimentally attaining up to 98.63% classification accuracy at four different growth stages for all classes of the “bccr-segset” dataset.
To evaluate performance of the k-FLBPCM algorithm in real-time, a comparison analysis between our novel method (k-FLBPCM) and deep convolutional neural networks (DCNNs) is conducted on morphologically similar crops and weeds. Various DCNN models, namely VGG-16, VGG-19, ResNet50 and InceptionV3, are optimised, by fine-tuning their hyper-parameters, and tested. Based on the experimental results on the “bccr-segset” dataset collected from the laboratory and the “fieldtrip_can_weeds” dataset collected from the field under practical environments, the classification accuracies of the DCNN models and the k-FLBPCM method are almost similar. Another experiment is conducted by training the algorithms with plant images obtained at mature stages and testing them at early stages. In this case, the new k-FLBPCM method outperformed the state-of-the-art CNN models in identifying small leaf shapes of canola-radish (crop-weed) at early growth stages, with an order of magnitude lower error rates in comparison with DCNN models. Furthermore, the execution time of the k-FLBPCM method during the training and test phases was faster than the DCNN counterparts, with an identification time difference of approximately 0.224ms per image for the laboratory dataset and 0.346ms per image for the field dataset. These results demonstrate the ability of the k-FLBPCM method to rapidly detect weeds from crops of similar appearance in real time with less data, and generalize to different size plants better than the CNN-based methods
Technologies of information transmission and processing
Сборник содержит статьи, тематика которых посвящена научно-теоретическим разработкам в области сетей телекоммуникаций, информационной безопасности, технологий передачи и обработки информации. Предназначен для научных сотрудников в области инфокоммуникаций, преподавателей, аспирантов, магистрантов и студентов технических вузов