220 research outputs found
The Optimisation of Elementary and Integrative Content-Based Image Retrieval Techniques
Image retrieval plays a major role in many image processing applications. However, a number of factors (e.g. rotation, non-uniform illumination, noise and lack of spatial information) can disrupt the outputs of image retrieval systems such that they cannot produce the desired results. In recent years, many researchers have introduced different approaches to overcome this problem. Colour-based CBIR (content-based image retrieval) and shape-based CBIR were the most commonly used techniques for obtaining image signatures. Although the colour histogram and shape descriptor have produced satisfactory results for certain applications, they still suffer many theoretical and practical problems. A prominent one among them is the well-known “curse of dimensionality “.
In this research, a new Fuzzy Fusion-based Colour and Shape Signature (FFCSS) approach for integrating colour-only and shape-only features has been investigated to produce an effective image feature vector for database retrieval. The proposed technique is based on an optimised fuzzy colour scheme and robust shape descriptors.
Experimental tests were carried out to check the behaviour of the FFCSS-based system, including sensitivity and robustness of the proposed signature of the sampled images, especially under varied conditions of, rotation, scaling, noise and light intensity. To further improve retrieval efficiency of the devised signature model, the target image repositories were clustered into several groups using the k-means clustering algorithm at system runtime, where the search begins at the centres of each cluster. The FFCSS-based approach has proven superior to other benchmarked classic CBIR methods, hence this research makes a substantial contribution towards corresponding theoretical and practical fronts
A neuro-genetic hybrid approach to automatic identification of plant leaves
Plants are essential for the existence of most living things on this planet. Plants are used for providing food, shelter, and medicine. The ability to identify plants is very important for several applications, including conservation of endangered plant species, rehabilitation of lands after mining activities and differentiating crop plants from weeds.
In recent times, many researchers have made attempts to develop automated plant species recognition systems. However, the current computer-based plants recognition systems have limitations as some plants are naturally complex, thus it is difficult to extract and represent their features. Further, natural differences of features within the same plant and similarities between plants of different species cause problems in classification.
This thesis developed a novel hybrid intelligent system based on a neuro-genetic model for automatic recognition of plants using leaf image analysis based on novel approach of combining several image descriptors with Cellular Neural Networks (CNN), Genetic Algorithm (GA), and Probabilistic Neural Networks (PNN) to address classification challenges in plant computer-based plant species identification using the images of plant leaves.
A GA-based feature selection module was developed to select the best of these leaf features. Particle Swam Optimization (PSO) and Principal Component Analysis (PCA) were also used sideways for comparison and to provide rigorous feature selection and analysis. Statistical analysis using ANOVA and correlation techniques confirmed the effectiveness of the GA-based and PSO-based techniques as there were no redundant features, since the subset of features selected by both techniques correlated well. The number of principal components (PC) from the past were selected by conventional method associated with PCA. However, in this study, GA was used to select a minimum number of PC from the original PC space. This reduced computational cost with respect to time and increased the accuracy of the classifier used. The algebraic nature of the GA’s fitness function ensures good performance of the GA. Furthermore, GA was also used to optimize the parameters of a CNN (CNN for image segmentation) and then uniquely combined with PNN to improve and stabilize the performance of the classification system. The CNN (being an ordinary differential equation (ODE)) was solved using Runge-Kutta 4th order algorithm in order to minimize descritisation errors associated with edge detection.
This study involved the extraction of 112 features from the images of plant species found in the Flavia dataset (publically available) using MATLAB programming environment. These features include Zernike Moments (20 ZMs), Fourier Descriptors (21 FDs), Legendre Moments (20 LMs), Hu 7 Moments (7 Hu7Ms), Texture Properties (22 TP) , Geometrical Properties (10 GP), and Colour features (12 CF). With the use of GA, only 14 features were finally selected for optimal accuracy. The PNN was genetically optimized to ensure optimal accuracy since it is not the best practise to fix the tunning parameters for the PNN arbitrarily. Two separate GA algorithms were implemented to optimize the PNN, that is, the GA provided by MATLAB Optimization Toolbox (GA1) and a separately implemented GA (GA2). The best chromosome (PNN spread) for GA1 was 0.035 with associated classification accuracy of 91.3740% while a spread value of 0.06 was obtained from GA2 giving rise to improved classification accuracy of 92.62%. The PNN-based classifier used in this study was benchmarked against other classifiers such as Multi-layer perceptron (MLP), K Nearest Neigbhour (kNN), Naive Bayes Classifier (NBC), Radial Basis Function (RBF), Ensemble classifiers (Adaboost). The best candidate among these classifiers was the genetically optimized PNN. Some computational theoretic properties on PNN are also presented
Restoration of Defaced Serial Numbers Using Lock-In Infrared Thermography (Part I)
Infrared thermal imaging is an evolving approach useful in non-destructive evaluation of materials for industrial and research purposes. This study investigates the use of this method in combination with multivariate data analysis as an alternative to chemical etching; a destructive method currently used to recover defaced serial numbers stamped in metal. This process involves several unique aspects, each of which works to overcome some pertinent challenges associated with the recovery of defaced serial numbers. Infrared thermal imaging of metal surfaces provides thermal images sensitive to local differences in thermal conductivity of regions of plastic strain existing below a stamped number. These strains are created from stamping pressures distorting the atomic crystalline structure of the metal and extend to depths beneath the stamped number. These thermal differences are quite small and thus not readily visible from the raw thermal images of an irregular surface created by removing the stamped numbers. As such, further enhancement is usually needed to identify the subtle variations. The multivariate data analysis method, principal component analysis, is used to enhance these subtle variations and aid the recovery of the serial numbers. Multiple similarity measures are utilised to match recovered numbers to several numerical libraries, followed by application of various fusion rules to achieve consensus identification
An Overview of Advances of Pattern Recognition Systems in Computer Vision
26 pagesFirst of all, let's give a tentative answer to the following question: what is pattern recognition (PR)? Among all the possible existing answers, that which we consider being the best adapted to the situation and to the concern of this chapter is: "pattern recognition is the scientific discipline of machine learning (or artificial intelligence) that aims at classifying data (patterns) into a number of categories or classes". But what is a pattern? A pattern recognition system (PRS) is an automatic system that aims at classifying the input pattern into a specific class. It proceeds into two successive tasks: (1) the analysis (or description) that extracts the characteristics from the pattern being studied and (2) the classification (or recognition) that enables us to recognise an object (or a pattern) by using some characteristics derived from the first task
Spurious Shear in Weak Lensing with LSST
The complete 10-year survey from the Large Synoptic Survey Telescope (LSST)
will image 20,000 square degrees of sky in six filter bands every few
nights, bringing the final survey depth to , with over 4 billion
well measured galaxies. To take full advantage of this unprecedented
statistical power, the systematic errors associated with weak lensing
measurements need to be controlled to a level similar to the statistical
errors.
This work is the first attempt to quantitatively estimate the absolute level
and statistical properties of the systematic errors on weak lensing shear
measurements due to the most important physical effects in the LSST system via
high fidelity ray-tracing simulations. We identify and isolate the different
sources of algorithm-independent, \textit{additive} systematic errors on shear
measurements for LSST and predict their impact on the final cosmic shear
measurements using conventional weak lensing analysis techniques. We find that
the main source of the errors comes from an inability to adequately
characterise the atmospheric point spread function (PSF) due to its high
frequency spatial variation on angular scales smaller than in the
single short exposures, which propagates into a spurious shear correlation
function at the -- level on these scales. With the large
multi-epoch dataset that will be acquired by LSST, the stochastic errors
average out, bringing the final spurious shear correlation function to a level
very close to the statistical errors. Our results imply that the cosmological
constraints from LSST will not be severely limited by these
algorithm-independent, additive systematic effects.Comment: 22 pages, 12 figures, accepted by MNRA
Content-based indexing of low resolution documents
In any multimedia presentation, the trend for attendees taking pictures of slides that
interest them during the presentation using capturing devices is gaining popularity.
To enhance the image usefulness, the images captured could be linked to image or
video database. The database can be used for the purpose of file archiving, teaching
and learning, research and knowledge management, which concern image search.
However, the above-mentioned devices include cameras or mobiles phones have low
resolution resulted from poor lighting and noise. Content-Based Image Retrieval
(CBIR) is considered among the most interesting and promising fields as far as
image search is concerned. Image search is related with finding images that are
similar for the known query image found in a given image database. This thesis
concerns with the methods used for the purpose of identifying documents that are
captured using image capturing devices. In addition, the thesis also concerns with a
technique that can be used to retrieve images from an indexed image database. Both
concerns above apply digital image processing technique. To build an indexed
structure for fast and high quality content-based retrieval of an image, some existing
representative signatures and the key indexes used have been revised. The retrieval
performance is very much relying on how the indexing is done. The retrieval
approaches that are currently in existence including making use of shape, colour and
texture features. Putting into consideration these features relative to individual
databases, the majority of retrievals approaches have poor results on low resolution
documents, consuming a lot of time and in the some cases, for the given query image,
irrelevant images are obtained. The proposed identification and indexing method in
the thesis uses a Visual Signature (VS). VS consists of the captures slides textual
layout’s graphical information, shape’s moment and spatial distribution of colour.
This approach, which is signature-based are considered for fast and efficient
matching to fulfil the needs of real-time applications. The approach also has the
capability to overcome the problem low resolution document such as noisy image,
the environment’s varying lighting conditions and complex backgrounds. We present
hierarchy indexing techniques, whose foundation are tree and clustering. K-means
clustering are used for visual features like colour since their spatial distribution give a good image’s global information. Tree indexing for extracted layout and shape
features are structured hierarchically and Euclidean distance is used to get similarity
image for CBIR. The assessment of the proposed indexing scheme is conducted
based on recall and precision, a standard CBIR retrieval performance evaluation. We
develop CBIR system and conduct various retrieval experiments with the
fundamental aim of comparing the accuracy during image retrieval. A new algorithm
that can be used with integrated visual signatures, especially in late fusion query was
introduced. The algorithm has the capability of reducing any shortcoming associated
with normalisation in initial fusion technique. Slides from conferences, lectures and
meetings presentation are used for comparing the proposed technique’s performances
with that of the existing approaches with the help of real data. This finding of the
thesis presents exciting possibilities as the CBIR systems is able to produce high
quality result even for a query, which uses low resolution documents. In the future,
the utilization of multimodal signatures, relevance feedback and artificial intelligence
technique are recommended to be used in CBIR system to further enhance the
performance
Biological image analysis
In biological research images are extensively used to monitor growth, dynamics and changes in biological specimen, such as cells or plants. Many of these images are used solely for observation or are manually annotated by an expert. In this dissertation we discuss several methods to automate the annotating and analysis of bio-images. Two large clusters of methods have been investigated and developed. A first set of methods focuses on the automatic delineation of relevant objects in bio-images, such as individual cells in microscopic images. Since these methods should be useful for many different applications, e.g. to detect and delineate different objects (cells, plants, leafs, ...) in different types of images (different types of microscopes, regular colour photographs, ...), the methods should be easy to adjust. Therefore we developed a methodology relying on probability theory, where all required parameters can easily be estimated by a biologist, without requiring any knowledge on the techniques used in the actual software.
A second cluster of investigated techniques focuses on the analysis of shapes. By defining new features that describe shapes, we are able to automatically classify shapes, retrieve similar shapes from a database and even analyse how an object deforms through time
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