Rajeswari Mandava

• Professor at University of Science Malaysia

In image segmentation, image is divided into regions of similar pixels that satisfy a defined notion of similarity. The complexity of image segmentation is further increased when the separation between neighboring regions is ambiguous. In this paper, we propose an approach that uses the information theoretic rough sets concept (ITRS) to model the a...

In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the ima...

Segmentation of hippocampus in the brain is one of a major challenge in medical image segmentation due to its’ imaging characteristics, with almost similar intensity between another adjacent gray matter structure, such as amygdala. The intensity similarity has causes the hippocampus to have weak or fuzzy boundaries. With this main challenge being d...

Ontology-based label extraction is extensively used to interpret the semantics found in image and video data. Particularly, ontology-based label extraction is one of the main steps in object class recognition, image annotation, and image disambiguation. These applications have important roles in the field of image analysis, and as such, a number of...

Invention of diffusion imaging has empowered the neuro-scientists with maps of microscopic structural information that could be taken in vivo. Different diffusion models have been proposed since the inception of the diffusion tensor imaging. Diffusion models have been mainly used for visualizing the brain tissues as precise as possible. However, in...

To segment an image using the random walks algorithm; users are often required to initialize the approximate locations of the objects and background in the image. Due to its segmenting model that is mainly reflected by the relationship among the neighborhood pixels and its boundary conditions, random walks algorithm has made itself sensitive to the...

The Local Binary Pattern (LBP) descriptor encodes the complementary information of the spatial patterns and intensity variations in a local image neighborhood. The richness of this multidimensional information offers many possible variations to the encoding process. Taking advantage of this, several variants of the LBP have been proposed. This work...

This paper presents a new approach for hippocampus localization using pairwise non-rigid Coherent Point Drift registration method. The concept of assembled point set is introduced, which is a combination of the available training point sets into a single data space that represents its distribution. Non-rigid Coherent Point Drift is then adapted to...

This paper presents a framework for soccer event detection through collaborative analysis of the textual, visual and aural modalities. The basic notion is to decompose a match video into smaller segments until ultimately the desired eventful segment is identified. Simple features are considered namely the minute-by-minute reports from sports websit...

Accelerated multi-armed bandit (MAB) model in Reinforcement-Learning for on-line sequential selection problems is presented. This iterative model utilizes an automatic step size calculation that improves the performance of MAB algorithm under different conditions such as, variable variance of reward and larger set of usable actions. As result of th...

Detecting semantic events in sports video is crucial for video indexing and retrieval. Most existing works have exclusively relied on video content features, namely, directly available and extractable data from the visual and/or aural channels. Sole reliance on such data however, can be problematic due to the high-level semantic nature of video and...

This paper presents a framework for soccer event detection through joint textual, aural and visual feature analysis. Firstly, textual cues from online sporting resources are used to significantly reduce and localize the event search space. Then, analysis is performed based on generic rule-sets imposed on specific audiovisual feature properties to i...

In recent years, the machine learning community has witnessed a tremendous growth in the development of kernel-based learning algorithms. However, the performance of this class of algorithms greatly depends on the choice of the kernel function. Kernel function implicitly represents the inner product between a pair of points of a dataset in a higher...

This study reviews the state-of-the-art multiobjective optimisation (MOO) techniques with metaheuristic through clustering approaches developed specifically for image segmentation problems. The authors treat image segmentation as a real-life problem with multiple objectives; thus, focusing on MOO methods that allow a trade-off among multiple object...

The collaborative teleradiology system detailed here would enable multiple medical experts to view, analyze, and discuss regions of interest in medical images remotely. Current research into similar systems have mostly focused on creating effective mechanisms to securely access and store expert opinions on the medical images, with additional work i...

Low contrast between tumor and healthy liver tissue is one of the significant and challenging features among others in the automated tumor delineation process. In this paper we propose kernel based clustering algorithms that incorporate Tsallis entropy to resolve long range interactions between tumor and healthy tissue intensities. This paper repor...

Problem statement: Extraction of features in object class recognition researches previously gives attention to local features as discriminative features. This is because local features have invariant properties that are robust to viewpoints, translation and rotation. However this feature still has a limitation to represent high-level representation...

Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at address...

Image annotation is an important task in computer vision. The annotated images can be very useful for indexing and retrieval applications. In this paper, we propose a generative model for image annotation based on mixtures of the exponential family of distributions. The distributions considered are the Multivariate Gaussian, Rayleigh, Poisson, Bern...

This paper investigates the effects of feature selection via dimensionality reduction techniques for the task of object class recognition. Two filter-based algorithms are considered namely Correlation-based Feature Selection (CFS) and Principal Components Analysis (PCA). A Support Vector Machine is used to compare these two techniques against class...

Automating the detection of lesions in liver CT scans requires a high performance and robust solution. With CT-scan start to become the norm in emergency department, the need for a fast and efficient liver lesions detection method is arising. In this paper, we propose a fast and evolvable method to profile the features of pre-segmented healthy live...

Recently there has been a steep growth in the development of kernel-based learning algorithms. The intrinsic problem in such algorithms is the selection of the optimal kernel for the learning task of interest. In this paper, we propose an unsupervised approach to learn a linear combination of kernel functions, such that the resulting kernel best se...

This paper aims to provide a comprehensive review of nature-inspired techniques used in image segmentation problems. We focus particularly on multi-objective clustering and classification approaches. The approaches are classified based on the various aspects of optimization, various possible problem formulations, and types of datasets modeled. In t...

Semantics extraction based on local context detection, for the instance's entities, out of a global context in what so called contextualization with reference to WordNet is presented. The entities of single visual content intend to express a single or few interrelated topics in a constrained domain. Thus, the context of the entities is detected and...

The harmony search (HS) algorithm is a relatively new population-based metaheuristic optimization algorithm. It imitates the music improvisation process where musicians improvise their instruments’ pitch by searching for a perfect state of harmony. Since the emergence of this algorithm in 2001, it attracted many researchers from various fields espe...

This paper proposes a cyclic load balancing strategy to parallel Fuzzy C-Means cluster analysis algorithm. The problem is to minimize the total time cost and maximize the parallel processing efficiency when a subset of clusters is distributed over a set of processors cores on shared memory architecture. The parallel Fuzzy C - Means (FCM) cluster an...

Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an inte...

This paper presents a comparative study of Bayesian belief network structure learning algorithms with a view to identify a suitable algorithm for modeling the contextual relations among objects typically found in natural imagery. Four popular structure learning algorithms are compared: two constraint-based algorithms (PC proposed by Spirtes and Gly...

In this paper, the automatic segmentation of Osteosar-coma in MRI images is formed as a clustering problem. Subsequently, a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means (FCM) called DCHS is proposed to automatically segment the Osteosarcoma MRI images in an intelligent manner. The concept of variab...

In this paper, we propose a spatial fuzzy clustering method based on multiple criteria optimization method. Our fuzzy clustering method is an enhanced version of fuzzy c-means (FCM) with consideration of multiple criteria. We initiate our multiple criteria optimization approach with two criteria in term of the features of an image: spatial informat...

Breast cancer continues to be a significant public health problem in the world. Early detection is the key for improving breast cancer prognosis. The CAD systems can provide such help and they are important and necessary for breast cancer control. Microcalcifications and masses are the two most important indicators of malignancy, and their automate...

Automatic brain MRI image segmentation is a challenging problem and received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the Harmony Search (HS) hybridized with Fuzzy C-means called DCHS to automatically segment the brain MRI image in an intelligent manner. In...

This paper introduces an approach to perform segmentation of regions in computed tomography (CT) images that exhibit intra-region intensity variations and at the same time have similar intensity distributions with surrounding/adjacent regions. In this work, we adapt a feature computed from wavelet transform called wavelet energy to represent the re...

Image segmentation aims to partition an image into several disjointed regions that are homogeneous with regards to some measures so that subsequent higher level computer vision processing, such as object recognition, image understanding and scene description can be performed. Multi-objective formulations are realistic models for image segmentation...

A new trend of problem formulation for image segmentation is touse multiobjective optimization approach in its decision makingprocess. Multiobjective formulations are realistic models for manycomplex optimization problems. In many real-life problems,objectives under consideration conflict with each other, andoptimizing a particular solution with re...

In this paper, a new dynamic clustering approach based on the harmony search algorithm (HS) called DCHS is proposed. In this algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony memo...

Image segmentation is considered as one of the crucial steps in image analysis process and it is the most challenging task. Image segmentation can be modeled as a clustering problem. Therefore, clustering algorithms have been applied successfully in image segmentation problems. Fuzzy c-mean (FCM) algorithm is considered as one of the most popular c...

We report a new automated method for White Matter Lesions (WMLs) Segmentation in cranial MR Imaging. WMLs are diffuse white matter abnormalities which are often presented as hyperintense regions. In our approach, the presence of these abnormalities are detected as outliers in the intensity distribution of the FLAIR sequence using the histogram tail...

In this paper we show how to resolve the ambiguity of concepts that are extracted from visual stream with the help of identified concepts from associated textual stream. The disambiguation is performed at the concept-level based on semantic closeness over the domain ontology. The semantic closeness is a function of the distance between the concept...

There has been a lot of research targeting text classification. Many of them focus on a particular characteristic of text data - multi-labelity. This arises due to the fact that a document may be associated with multiple classes at the same time. The consequence of such a characteristic is the low performance of traditional binary or multi-class cl...

In this paper, we propose an approach to learn the kernel which uses transferred knowledge from unlabeled data to cope with situations where training examples are scarce. In our approach, unlabeled data has been used to construct an optimized kernel that better generalizes on the target dataset. For the proposed kernel learning algorithm, Fisher Di...

Domain-specific ontologies encode reusable domain vocabulary and represent established domain semantics. The alignment of such ontologies requires an approach based on a semantic analysis of its components. This paper presents SLADO, Semantic Lexical Alignment for Domain-specific Ontologies. The proposed approach aims to use the available dictionar...

The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic algorithm path planning approach in the previous...

Problem statement: This study proposed multimodal integration method at the concept level to investigate information from multimodalities. The multimodal data was represented as two separate lists of concepts which were extracted from images and its related text. The concepts extracted from image analysis are often ambiguous, while the concepts ext...

In object class recognition, lots of past researches focused on the local descriptors such as SIFT to categorize the variation of objects belonging to the same category in different poses, sizes, and appearance. However, SIFT descriptors may produce poor result especially if the object does not have enough information of its texture features. Due t...

We propose a new approach to tackle the well known fuzzy c-means (FCM) initialization problem. Our approach uses a metaheuristic search method called Harmony Search (HS) algorithm to produce near-optimal initial cluster centers for the FCM algorithm. We then demonstrate the effectiveness of our approach in a MRI segmentation problem. In order to dr...

Context is a vital element in both biological as well as synthetic vision systems. It is essential for deriving meaningful explanation of an image. Unfortunately, there is a lack of consensus in the computer vision community on what context is and how it should be represented. In this paper context is defined generally as "any and all information t...

Image processing plays an important role in computer science, making complex image manipulation more feasible. It can be used either in a general manner or in specific domains such as for medical purposes. With the availability of the internet, image processing applications can be distributed to be available for different people, regardless of thei...

In this paper, we propose a technique for classifying shots of playfield-based sports video into their respective view classes. Based on common broadcasting style, a shot can be classified as a far-view or a closeup-view. The technique considers the frame-wise color values of each pixel in the HSV color space, while at the same time calculating the...

One major challenge faced by segmentation techniques in analyzing and visualizing individual slices of a 3D anatomical structure, is the degree of manual interaction required. To alleviate this problem, researchers have proposed the automatic incorporation of anatomical knowledge, via medical atlases to assist with the segmentation process. Some so...

This paper introduces two-way dictionary based words/strings matching technique for ontology alignment. The proposed technique represents a step ahead into semantic ontology alignment. This technique uses a combination of approximate, exact, NLP method and error correction routines for ontology alignment. The overall design is based on the assumpti...

There is a lot of commercial software for working with images, from very simple editing and enhancement up to very complicated and sophisticated jobs, but we cannot find a share area that is accessible from all around the world and can respond to our needs in image processing and data visualizing. For working with the images in a distributed manner...

This paper presents a new segmentation method that integrates a wavelet based feature, which is able to enhance the dissimilarity between regions with low variations in intensity. This feature is integrated to formulate a new level set based active contour model that addresses the segmentation of regions with highly similar intensities in medical i...

Overlaid text appears frequently in broadcast sports video. They provide supplementary information regarding the happenings of a particular game. Examples include important events of interest such as bookings and substitutions in a soccer match. Furthermore, overlaid-text is displayed when a particular concept of interest is happening or has happen...

This paper introduces Textured Renyi Entropy for image thresholding based on a novel combination mechanism. The Renyi Entropy is extended by modifying its priori, while still preserving overall functionality. An optional priori is introduced to improve accuracy. The priori modification allows adding of texture information in an efficient way, which...

This paper presents a hybrid algorithm for object based clustering. The algorithm is designed based on hybrid of hierarchical and k-means clustering algorithm. For this work, we used dataset consist of natural imagery collected from PASCAL database 2006 collection and Google images. A collection of low level features image is used to validate the p...

Object class recognition is a highly challenging area in computer vision and machine learning. In this paper, we introduce a novel approach to object class recognition using Neuro Evolution of Augmenting Topologies (NEAT) to evolve artificial neural networks (ANN) capable of taking advantage of the robust SIFT feature based descriptor histograms. W...

Overlaid-text appears frequently in broadcast sports video. They provide a plethora of information regarding the goings-on of a particular game. Examples include important events and video segments of interest such as bookings and half-time analysis, respectively. Furthermore, it is common that overlaid text is displayed when a particular concept i...

We present a simple technique to isolate and detect Cupriavidus sp. bacterium in microscopy images as a non-destructive means to monitor the growth and evolution of the bacteria. The approach uses OTSU binarization on the saturation channel of the bacteria sample images followed by morphological processing. This method of analysis has significantly...

Image segmentation is the most essential and crucial process in order to facilitate the delineation, characterization and visualization of structures of interests in medical images. In recent years, various methods of medical image segmentation have been employed depending on the type of tissues, anatomy of object of interest and imaging modality b...

In this paper, a novel approach to automatically segment the optic disc contour using the center point of an optic disc candidate is proposed. The optic disc segmentation algorithm consists of 2 stages. The first stage involves the removal of blood vessels that obscure the optic disc. The blood vessel structures are detected using morphological ope...

Most supervised neural networks are trained by minimizing the mean square error (MSE) of the training set. In the presence of outliers, the resulting neural network model can differ significantly from the underlying model that generates the data. This paper outlines two robust learning methods for a dynamic structure neural network called increment...

This paper presents two novel approaches to determine optimum growing multi-experts network (GMN) structure. The first method called direct method deals with expertise domain and levels in connection with local experts. The growing neural gas (GNG) algorithm is used to cluster the local experts. The concept of error distribution is used to apportio...

The key feature of this paper is the application of a robotic control concept – Active Force Control (AFC). In this type of control, the unknown friction effect of the robotic arm may be compensated by the AFC method. AFC involves the direct measurement of the acceleration and force quantities and therefore, the process of estimating the system ‘di...

A fundamental task in robotic assembly is the pick and place operation. Generally, this operation consists of three subtasks; guiding the robot to the target and positioning the manipulator in an appropriate pose, picking up the object and moving the object to a new location. In situations where the pose of the target may vary in the workspace, sen...

This paper describes the development of a force-guided robot that uses the information of contact force to overcome component misalignment in the automated assembly of a mobile phone. Several possibilities of misalignment in the assembly of the back chassis and front housing of the mobile phone are studied. An assembly approach using force-guided m...

Large image databases are used in many multimedia applications such as entertainment, business, art, engineering and science. Searching information in these databases is a crucial problem to be solved for the development of visual information system. Therefore efficient retrieval methods are required for the purpose of finding a desired image from...

A simple and novel method is proposed to estimate the confidence interval of any neural network. A recently introduced Growing Multi-Experts Network (GMN) is embedded with confidence interval estimator whose output directly indicates the defined measure. One-step hybrid learning is employed in which the unsupervised learning method of Growing Neura...

This paper deals with a novel idea of identification of nonlinear dynamic systems via a constructivism inspired neural network. The proposed network is known as growing multi-experts network (GMN). In GMN, the problem space is decomposed into overlapping regions by expertise domain and local expert models are graded according to their expertise lev...

Artificial neural networks (ANNs) have been used to construct empirical nonlinear models of process data. Because networks are not based on physical theory and contain nonlinearities, their predictions are suspect when extrapolating beyond the range of original training data. Standard networks give no indication of possible errors due to extrapolat...

This paper describes how force-guided robot can be implemented in the automated assembly of mobile phone. A case study was carried out to investigate the assembly operations and strategies involved. Force-guided robot was developed and implemented in the real environment. Proportional-based external force control with hybrid framework was developed...

In this paper, we propose a new similarity measure determination based on the NURBS-Warping method. In this method, the query image represented by the Non-Uniform Rational B-Spline (NURBS) descriptor is warped to best fit the shape of the database image. The similarity between two images is determined by measuring the effort being spent in the warp...

An endeavor is made in this paper to describe a self-regulating constructive multi-model neural network called Self-regulating Growing Multi-Experts Network (SGMN) that can approximate an unknown nonlinear function from observed input-output training data. The proposed network is devised to overcome the redundancy problems of Gaussian neural networ...

Active Force Control (AFC) is known to offer a feasible solution to control efficiently a robot operating at highspeed operation. This method is proven to be stable and robust against internal and external disturbance and has been successfully in various experimental works. In this regards, the main issue of AFC method is the estimation of the iner...