M. Rajeswari
Universiti Sains Malaysia, School of Computer Sciences, Faculty Member
Research Interests:
Research Interests:
Research Interests: Computer Science, Information Retrieval, Image Processing, Natural Language Processing, Timing Analysis, and 10 moreVideo Processing, Video Analysis, Video segmentation, Image and Video Processing, Semantic gap, Feature Extraction, Video Retrieval, Optical Character Recognition, Text Extraction, and Matching statistics
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Research Interests:
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Research Interests: Packet Switching, Fractals, Quality Control, Broadcasting, Region growing, and 15 moreLocal Area Networks, Fractal Analysis, Degradation, Structure learning, Dimensional, Iterated Function System, Broadcast traffic, Network structure, Network Traffic, Polynomials, Switches, Application Software, Divide and Conquer, Growing Neural Gas, and Fuzzy Model
AbstractWith the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of... more
AbstractWith the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of automated text classification ...
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Abstract 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... more
Abstract 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 ...
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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... more
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 solutions include constructing specialized simple 2D, as well as complex 3D atlases. In this paper we propose a simple method that automatically transfers a prior anatomical knowledge from a simple teaching atlas of a single 2D slice to the most similar slice in the 3D volume dataset. Segmentation of the selected anatomy is then able to be propagated automatically on the remaining slices in the dataset without further manual interaction. Our experiments are conducted on abdomen CT images and we successful delineation and visualization have been demonstrated for spleen, and kidney images.
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... Parallel Fuzzy C – Means Cluster Analysis Mogana Vadiveloo, Rosni Abdullah, Mandava Rajeswari, Ahmad Adel Abu-Shareha School of Computer Sciences Universiti Sains Malaysia 11800 Penang, Malaysia mv90991@student.usm.my, {rosni,... more
... Parallel Fuzzy C – Means Cluster Analysis Mogana Vadiveloo, Rosni Abdullah, Mandava Rajeswari, Ahmad Adel Abu-Shareha School of Computer Sciences Universiti Sains Malaysia 11800 Penang, Malaysia mv90991@student.usm.my, {rosni, mandava, adel}@cs.usm.my ...
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... Page 4. Segmentation And Quantification OfThe Cupriavidus Sp. Bacterium Using Microscopy Images [3] Al-Ashraf Amirul, Bee-Yong Tay, Choy-Wan Chang, M. [6] Ortiz de Solorzano, C., Garcia Rodriguez, E., Jones, A., NM Azizan, MIA Majid,... more
... Page 4. Segmentation And Quantification OfThe Cupriavidus Sp. Bacterium Using Microscopy Images [3] Al-Ashraf Amirul, Bee-Yong Tay, Choy-Wan Chang, M. [6] Ortiz de Solorzano, C., Garcia Rodriguez, E., Jones, A., NM Azizan, MIA Majid, K. Sudesh. 2004. Biosynthesis and ...
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Research Interests: Computer Vision, Image Processing, Methodology, Control Engineering, Modeling, and 15 moreDigital Signal Processing, Machine Vision, Motion estimation, Geometric model, System Analysis, Motion Tracking, Real Time Systems, Feature Extraction, Real Time, Chip, Real Time system, Extended Kalman Filter, Interconnection, Degree of Freedom, and Region of Interest
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Research Interests: Algorithms, Artificial Intelligence, Expert Systems, Neural Network, Multidisciplinary, and 15 moreLinear Model, Computer Simulation, Feedback, Adaptive Resonance Theory, Industrial Application, Clustering Method, Network structure, Gradient Descent, Nonlinear Model, Logistic Models, Direct Method, Growing Neural Gas, Decision Support Techniques, Neural nets, and Adaptive learning rate
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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... more
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 the multi-objective clustering methods, the definition of the types of representation methods, encoding techniques, and number of clusters defined (fixed/variable) are presented. In the ...
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ABSTRACf This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller that exhibits self-constructing and self-learning capabilities. In this method, the conventional linear PD controller is augmented with a... more
ABSTRACf This paper proposes a nonlinear variable gain Proportional-Derivative (PD) controller that exhibits self-constructing and self-learning capabilities. In this method, the conventional linear PD controller is augmented with a nonlinear variable PD gain control signal using a ...
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... more
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 liver and use it to detect the presence of liver lesions in emergency scenario. Our preliminary experiment with the MICCAI 2007 grand challenge datasets shows promising results of a fast training time, ability to evolve the produced healthy liver profiles, and accurate detection of the liver lesions. Lastly, the future work directions are also presented.
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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... more
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 inputs of the seeds. Instead of considering the relationship between the neighborhood pixels solely, an attempt has been made to modify the weighting function that accounts for the intensity changes between the neighborhood nodes. Local affiliation within the defined neighborhood region of the two nodes is taken into consideration by incorporating an extra penalty term into the weighting function. Besides that, to better segment images, particularly medical images with texture features, GLCM variance is incorporated into the weighting function through kernel density estimation (KDE). The probability density of each pixel belonging to the ...
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Research Interests:
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... more
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 classical feature concatenation, based on the Graz02 dataset. Experimental results show that the feature selection algorithms are able to retain the most relevant and discriminant features, while maintaining recognition accuracy and improving model building time.
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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... more
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 happened. This paper presents a technique to automatically detect only video frames that contain valid overlaid text. Experiments have shown reliable detection, extraction and recognition, whose results have been successfully used for domain concept understanding via matching with a soccer term database.
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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... more
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 assumed object size within the segmented playfield region. Based on our experiments, it is shown that this technique can greatly reduce the number of misclassified shots, while at the same time maintain a good level of accuracy. At the moment, we have tested our approach on soccer videos but believe that it can be applied to other playfield-based sports as well.
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 assumed object size within the segmented playfield region. Based on our experiments, it is shown that this technique can greatly reduce the number of misclassified shots, while at the same time maintain a good level of accuracy. At the moment, we have tested our approach on soccer videos but believe that it can be applied to other playfield-based sports as well.
Research Interests:
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... more
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 overlaidtext
is displayed when a particular concept is
happening or has happened. This paper presents a
concept identification framework, based on matched
keywords from overlaid-text extraction and
recognition. Possible occurrences of overlaid-text in
soccer programs are extracted and recognized, and
then matched against a soccer-term database.
Preliminary experiments show reliable character
extraction, whose recognition has been successfully
matched with keywords within the database.
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 overlaidtext
is displayed when a particular concept is
happening or has happened. This paper presents a
concept identification framework, based on matched
keywords from overlaid-text extraction and
recognition. Possible occurrences of overlaid-text in
soccer programs are extracted and recognized, and
then matched against a soccer-term database.
Preliminary experiments show reliable character
extraction, whose recognition has been successfully
matched with keywords within the database.
Research Interests:
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... more
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, Bernoulli and Centered Laplacian. The model leads to a generic algorithm that is able to perform learning in a more efficient and flexible manner. Subsequently, it can be used to evaluate the performance of each distribution in the task of image annotation. The approach is also compared to a discriminative approach (i.e. Support Vector Machine) using the LabelMe dataset for the concepts Buildings, Street, Mountains and Coasts, where promising classification results were reported for some of the concepts.
Research Interests:
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... more
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 the difficulty to properly align detected events with their exact time of occurrences. This paper proposes a framework for soccer goal event detection through collaborative analysis of multimodal features. Unlike previous approaches, the visual and aural contents are not directly scrutinized. Instead, an external textual source (i.e., minute-by-minute reports from sports websites) is used to initially localize the event search space. This step is vital as the event search space can significantly be reduced. This also makes further visual and aural analysis more efficient since excessive and unnecessary non-eventful segments are discarded, culminating in the accurate identification of the actual goal event segment. Experiments conducted on thirteen soccer matches are very promising with high accuracy rates being reported.
Research Interests:
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... more
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 websites (i.e., text), the semantic shot classes of far and closeup-views (i.e., visual), and the low-level features of pitch and log-energy (i.e., audio). The framework demonstrates that despite considering simple features, and by averting the use of labeled training examples,
event detection can be achieved at very high accuracy. Experiments conducted on ~30-hours of soccer video show very promising results for the detection of goals, penalties, yellow cards and red cards.
event detection can be achieved at very high accuracy. Experiments conducted on ~30-hours of soccer video show very promising results for the detection of goals, penalties, yellow cards and red cards.
Research Interests:
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... more
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 isolate the most compressed view of the events. Experiments conducted on 30-hours of soccer videos from various broadcasters show encouraging results for the detection of goals, penalties, yellow cards, red cards and substitutions.