Face Detection and Facial Feature Localization for multi-pose faces and complex backgroundimages

The objective of this thesis work, is to propose an algorithm to detect the faces in a digital image with complex background. A lot of work has already been done in the area of face detection, but drawback of some face detection algorithms is the lack of ability to detect faces with closed eyes and open mouth. Thus facial features form an important basis for detection. The current thesis work focuses on detection of faces based on facial objects. The procedure is composed of three different phases: segmentation phase, filtering phase and localization phase. In segmentation phase, the algorithm utilizes color segmentation to isolate human skin color based on its chrominance properties. In filtering phase, Minkowski addition based object removal (Morphological operations) has been used to remove the non-skin regions. In the last phase, Image Processing and Computer Vision methods have been used to find the existence of facial components in the skin regions.This method is effective on detecting a face region with closed eyes, open mouth and a half profile face. The experiment’s results demonstrated that the detection accuracy is around 85.4% and the detection speed is faster when compared to neural network method and other techniques.

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Habitat map of Danajon Bank, Philippines, derived from a high-spatial-resolution multi-spectral satellite image and georeferenced point intercept transect and spot-check survey field data, using an object-based image classification method

A mosaic of two WorldView-2 high resolution multispectral images (Acquisition dates: October 2010 and April 2012), in conjunction with field survey data, was used to create a habitat map of the Danajon Bank, Philippines (10°15'0'' N, 124°08'0'' E) using an object-based approach. To create the habitat map, we conducted benthic cover (seafloor) field surveys using two methods. Firstly, we undertook georeferenced point intercept transects (English et al., 1997). For ten sites we recorded habitat cover types at 1 m intervals on 10 m long transects (n= 2,070 points). Second, we conducted geo-referenced spot check surveys, by placing a viewing bucket in the water to estimate the percent cover benthic cover types (n = 2,357 points). Survey locations were chosen to cover a diverse and representative subset of habitats found in the Danajon Bank. The combination of methods was a compromise between the higher accuracy of point intercept transects and the larger sample area achievable through spot check surveys (Roelfsema and Phinn, 2008, doi:10.1117/12.804806). Object-based image analysis, using the field data as calibration data, was used to classify the image mosaic at each of the reef, geomorphic and benthic community levels. The benthic community level segregated the image into a total of 17 pure and mixed benthic classes.

Color and texture feature fusion using kernel PCA with application to object-based vegetation species classification

A good object representation or object descriptor is one of the key issues in object based image analysis. To effectively fuse color and texture as a unified descriptor at object level, this paper presents a novel method for feature fusion. Color histogram and the uniform local binary patterns are extracted from arbitrary-shaped image-objects, and kernel principal component analysis (kernel PCA) is employed to find nonlinear relationships of the extracted color and texture features. The maximum likelihood approach is used to estimate the intrinsic dimensionality, which is then used as a criterion for automatic selection of optimal feature set from the fused feature. The proposed method is evaluated using SVM as the benchmark classifier and is applied to object-based vegetation species classification using high spatial resolution aerial imagery. Experimental results demonstrate that great improvement can be achieved by using proposed feature fusion method.

Spectral–texture feature extraction using statistical moments with application to object-based vegetation species classification

The use of appropriate features to characterise an output class or object is critical for all classification problems. In order to find optimal feature descriptors for vegetation species classification in a power line corridor monitoring application, this article evaluates the capability of several spectral and texture features. A new idea of spectral–texture feature descriptor is proposed by incorporating spectral vegetation indices in statistical moment features. The proposed method is evaluated against several classic texture feature descriptors. Object-based classification method is used and a support vector machine is employed as the benchmark classifier. Individual tree crowns are first detected and segmented from aerial images and different feature vectors are extracted to represent each tree crown. The experimental results showed that the proposed spectral moment features outperform or can at least compare with the state-of-the-art texture descriptors in terms of classification accuracy. A comprehensive quantitative evaluation using receiver operating characteristic space analysis further demonstrates the strength of the proposed feature descriptors.

Grid-based method for ranking images with multiple objects

One of the content-based image retrieval techniques is the shape-based technique, which allows users to ask for objects similar in shape to a query object. Sajjanhar and Lu proposed a method for shape representation and similarity measure called the grid-based method [1]. They have shown that the method is effective for the retrieval of segmented objects based on shape. In this paper, we describe a system which uses the grid-based method for retrieval of images with multiple objects. We perform experiments on the prototype system to compare the performance of the grid-based method with the Fourier descriptors method [2]. Preliminary results have been presented.

Health information security evaluation: continued development of an object-oriented method

With the convergence of paper to electronic, the health industry is relying more on technology to maintain and update the well-being of patients. This reliance on technology requires an acute level of protection from
unwanted technological disasters and/or human threats. Research shows insufficiencies with the implementation and use of security controls; as well as current analysis methods lacking the techniques to analyse technical and social aspects of security. The aim of this paper is to introduce an information security evaluation methodology for health information systems based on UML.

Incorporating design explanation within formal object-oriented method (FOOM)

Requirements engineering is a commencing phase in the development of either software applications or information systems. It is concerned with understanding and specifying the customer's requirements of the system to be delivered. Throughout the literature, this is agreed to be one of the most crucial and, unfortunately, problematic phases in development. Despite the diversity of research directions, approaches and methods, the question of process understanding and management is still limited. Among contemporary approaches to the improvement of the current practice of Requirements Engineering, Formal Object-Oriented Method (FOOM) has been introduced as a new promising solution. The FOOM approach to requirements engineering is based on a synthesis of socio-organisational theory, the object-oriented approach, and mathematical formal specification. The entire FOOM specification process is evolutionary and involves a large volume of changes in requirements. During this process, requirements evolve through various forms of informal, semi-formal, and formal while maintaining a semantic link between these forms and, most importantly, conforming to the customer's requirements. A deep understanding of the complexity of the requirements model and its dynamics is critical in improving requirements engineering process management. This thesis investigates the benefits of documenting both the evolution of the requirements model and the rationale for that evolution. Design explanation explains and justifies the deliberations of, and decisions made during, the design activity. In this thesis, design explanation is used to describe the requirements engineering process in order to improve understandability of, and traceability within, the evolving requirements specification. The design explanation recorded during this research project is also useful in assisting the researcher in gaining insights into the creativity and opportunistic characteristics of the requirements engineering process. This thesis offers an interpretive investigation into incorporating design explanation within FOOM in order to extend and advantage the method. The researcher's interpretation and analysis of collected data highlight an insight-driven and opportunistic process rather than a strictly and systematically predefined one. In fact, the process was not smoothly evolutionary, but involved occasional 'crisis' points at which the model was reconceptualised, simplified and restructured. Therefore, contributions of the thesis lie not only in an effective incorporation of design explanation within FOOM, but also a deep understanding of the dynamic process of requirements engineering. The new understanding of the complexity of the requirements model and its dynamics suggests new directions for future research and forms a basis for a new approach to process management.

A comparison of pixel and object-based land cover classification. A case study of the Asmara region, Eritrea

In this paper we compare the performance of two image classification paradigms (object- and pixel-based) for creating a land cover map of Asmara, the capital of Eritrea and its surrounding areas using a Landsat ETM+ imagery acquired in January 2000. The image classification methods used were maximum likelihood for the pixel-based approach and Bhattacharyya distance for the object-oriented approach available in, respectively, ArcGIS and SPRING software packages. Advantages and limitations of both approaches are presented and discussed. Classifications outputs were assessed using overall accuracy and Kappa indices. Pixel- and object-based classification methods result in an overall accuracy of 78% and 85%, respectively. The Kappa coefficient for pixel- and object-based approaches was 0.74 and 0.82, respectively. Although pixel-based approach is the most commonly used method, assessment and visual interpretation of the results clearly reveal that the object-oriented approach has advantages for this specific case-study.

RESSIST: a research object-based recommender system

The scientific method is a methodological approach to the process of inquiry { in which empirically grounded theory of nature is constructed and verified [14]. It is a hard, exhaustive and dedicated multi-stage procedure that a researcher must perform to achieve valuable knowledge. Trying to help researchers during this process, a recommender system, intended as a researcher assistant, is designed to provide them useful tools and information for each stage of the procedure. A new similarity measure between research objects and a representational model, based on domain spaces, to handle them in dif ferent levels are created as well as a system to build them from OAI-PMH (and RSS) resources. It tries to represents a sound balance between scientific insight into individual scientific creative processes and technical implementation using innovative technologies in information extraction, document summarization and semantic analysis at a large scale.

Topological SLAM using a graph-matching based method on omnidirectional images

Comunicación presentada en el XI Workshop of Physical Agents, Valencia, 9-10 septiembre 2010.

Modeling geometric rules in object based models:an XML / GML approach

Most object-based approaches to Geographical Information Systems (GIS) have concentrated on the representation of geometric properties of objects in terms of fixed geometry. In our road traffic marking application domain we have a requirement to represent the static locations of the road markings but also enforce the associated regulations, which are typically geometric in nature. For example a give way line of a pedestrian crossing in the UK must be within 1100-3000 mm of the edge of the crossing pattern. In previous studies of the application of spatial rules (often called 'business logic') in GIS emphasis has been placed on the representation of topological constraints and data integrity checks. There is very little GIS literature that describes models for geometric rules, although there are some examples in the Computer Aided Design (CAD) literature. This paper introduces some of the ideas from so called variational CAD models to the GIS application domain, and extends these using a Geography Markup Language (GML) based representation. In our application we have an additional requirement; the geometric rules are often changed and vary from country to country so should be represented in a flexible manner. In this paper we describe an elegant solution to the representation of geometric rules, such as requiring lines to be offset from other objects. The method uses a feature-property model embraced in GML 3.1 and extends the possible relationships in feature collections to permit the application of parameterized geometric constraints to sub features. We show the parametric rule model we have developed and discuss the advantage of using simple parametric expressions in the rule base. We discuss the possibilities and limitations of our approach and relate our data model to GML 3.1. © 2006 Springer-Verlag Berlin Heidelberg.

A vibration based method to update axial shortening of load bearing elements

Axial shortening in vertical load bearing elements of reinforced concrete high-rise buildings is caused by the time dependent effects of shrinkage, creep and elastic shortening of concrete under loads. Such phenomenon has to be predicted at design stage and then updated during and after construction of the buildings in order to provide mitigation against the adverse effects of differential axial shortening among the elements. Existing measuring methods for updating previous predictions of axial shortening pose problems. With this in mind, a innovative procedure with a vibration based parameter called axial shortening index is proposed to update axial shortening of vertical elements based on variations in vibration characteristics of the buildings. This paper presents the development of the procedure and illustrates it through a numerical example of an unsymmetrical high-rise building with two outrigger and belt systems. Results indicate that the method has the capability to capture influence of different tributary areas, shear walls of outrigger and belt systems as well as the geometric complexity of the building.

Evaluation of spectral and texture features for object-based vegetation species classification using Support Vector Machines

The use of appropriate features to characterize an output class or object is critical for all classification problems. This paper evaluates the capability of several spectral and texture features for object-based vegetation classification at the species level using airborne high resolution multispectral imagery. Image-objects as the basic classification unit were generated through image segmentation. Statistical moments extracted from original spectral bands and vegetation index image are used as feature descriptors for image objects (i.e. tree crowns). Several state-of-art texture descriptors such as Gray-Level Co-Occurrence Matrix (GLCM), Local Binary Patterns (LBP) and its extensions are also extracted for comparison purpose. Support Vector Machine (SVM) is employed for classification in the object-feature space. The experimental results showed that incorporating spectral vegetation indices can improve the classification accuracy and obtained better results than in original spectral bands, and using moments of Ratio Vegetation Index obtained the highest average classification accuracy in our experiment. The experiments also indicate that the spectral moment features also outperform or can at least compare with the state-of-art texture descriptors in terms of classification accuracy.

Look before you leap : a confidence-based method for selecting species criticality while avoiding negative populations in τ-leaping

The stochastic simulation algorithm was introduced by Gillespie and in a different form by Kurtz. There have been many attempts at accelerating the algorithm without deviating from the behavior of the simulated system. The crux of the explicit τ-leaping procedure is the use of Poisson random variables to approximate the number of occurrences of each type of reaction event during a carefully selected time period, τ. This method is acceptable providing the leap condition, that no propensity function changes “significantly” during any time-step, is met. Using this method there is a possibility that species numbers can, artificially, become negative. Several recent papers have demonstrated methods that avoid this situation. One such method classifies, as critical, those reactions in danger of sending species populations negative. At most, one of these critical reactions is allowed to occur in the next time-step. We argue that the criticality of a reactant species and its dependent reaction channels should be related to the probability of the species number becoming negative. This way only reactions that, if fired, produce a high probability of driving a reactant population negative are labeled critical. The number of firings of more reaction channels can be approximated using Poisson random variables thus speeding up the simulation while maintaining the accuracy. In implementing this revised method of criticality selection we make use of the probability distribution from which the random variable describing the change in species number is drawn. We give several numerical examples to demonstrate the effectiveness of our new method.