A new scars removal technique of fingerprint images
International Conference on Instrumentation, Communication, Information Technology, and Biomedical Engineering 2009, 2009
Abstract-Automatic fingerprint recognition has received considerable attention over the past deca... more Abstract-Automatic fingerprint recognition has received considerable attention over the past decades. Progress has been made on models of the structures of fingerprints, techniques for acquisition of prints, and the developments of commercial automated fingerprint ...
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Papers by Ghazali Sulong
after applying image transform. Result coefficients from discrete cosine transform are
statistically inspected to evaluate their performance over different stages of age and
over different image samples within each age stage. Coefficients efficiency was
measured in term of their changes over different ages and changes over image samples
within each specific age; the coefficient with high interclass changes over age and low
intraclass changes over sample will be chosen to be an age estimation feature. A set of
experiments were conducted on standard FG.net dataset of face images, in addition to
private own collected dataset. Encouraging results were yielded in age estimation
depending on classification accuracy and mean absolute errors.
games
, virtual agents and movie animations. It is also considered important for applications which require interaction between human and computer. However, for this purpose, it is compulsory that the machine should have sufficient intelligence for recognizing and synthesizing human voices. As one of the most vital interaction method between human and machine, speech has recently received significant attention, especially in avatar research innovation. One of the challenges is to create precise lip movements of the avatar and synchronize it with a recorded audio. This paper specifically introduces the innovative concept of multimodal dialog systems of the virtual character and focuses the output part of such systems. More specifically, its focus is on behavior planning and developing the data control languages (DCL).
researches focus on this area with significant attention. Creating animated speech requires a facial model capable
of representing the myriad shapes the human face expressions during speech. Moreover, a method to produce the
correct shape at the correct time is also in order. One of the main challenges is to create precise lip movements of
the avatar and synchronize it with a recorded audio. This paper proposes a new lip synchronization algorithm for
realistic applications, which can be employed to generate synchronized facial movements among the audio
generated from natural speech or through a text-to-speech engine. This method requires an animator to construct
animations using a canonical set of visemes for all pair wise combination of a reduced phoneme set. These
animations are then stitched together smoothly to construct the final animation.
Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and
distance span vectors, acquirement of four features for each portion and classification to ascertain the
abnormality location. The threshold value and region of interest are discerned using operator input and Otsu
algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24° with
15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into
normal and abnormal brain tissues is performed using Support Vector Machine (SVM). Standard Deviation,
Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are
found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with
60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as
normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60%
images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02%
(training) and 98.19% (testing) are achieved.
post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.
Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal of the current status of semi-automated and automated methods for the segmentation of MR images with important issues and terminologies. Advantages and disadvantages of various segmentation methods with salient features and their relevancies are also cited.
after applying image transform. Result coefficients from discrete cosine transform are
statistically inspected to evaluate their performance over different stages of age and
over different image samples within each age stage. Coefficients efficiency was
measured in term of their changes over different ages and changes over image samples
within each specific age; the coefficient with high interclass changes over age and low
intraclass changes over sample will be chosen to be an age estimation feature. A set of
experiments were conducted on standard FG.net dataset of face images, in addition to
private own collected dataset. Encouraging results were yielded in age estimation
depending on classification accuracy and mean absolute errors.
games
, virtual agents and movie animations. It is also considered important for applications which require interaction between human and computer. However, for this purpose, it is compulsory that the machine should have sufficient intelligence for recognizing and synthesizing human voices. As one of the most vital interaction method between human and machine, speech has recently received significant attention, especially in avatar research innovation. One of the challenges is to create precise lip movements of the avatar and synchronize it with a recorded audio. This paper specifically introduces the innovative concept of multimodal dialog systems of the virtual character and focuses the output part of such systems. More specifically, its focus is on behavior planning and developing the data control languages (DCL).
researches focus on this area with significant attention. Creating animated speech requires a facial model capable
of representing the myriad shapes the human face expressions during speech. Moreover, a method to produce the
correct shape at the correct time is also in order. One of the main challenges is to create precise lip movements of
the avatar and synchronize it with a recorded audio. This paper proposes a new lip synchronization algorithm for
realistic applications, which can be employed to generate synchronized facial movements among the audio
generated from natural speech or through a text-to-speech engine. This method requires an animator to construct
animations using a canonical set of visemes for all pair wise combination of a reduced phoneme set. These
animations are then stitched together smoothly to construct the final animation.
Images (MRI). Present classifier is comprised of cerebral tissue extraction, image division into angular and
distance span vectors, acquirement of four features for each portion and classification to ascertain the
abnormality location. The threshold value and region of interest are discerned using operator input and Otsu
algorithm. Novel brain slices image division is introduced via angular and distance span vectors of sizes 24° with
15 pixels. Rotation invariance of the angular span vector is determined. An automatic image categorization into
normal and abnormal brain tissues is performed using Support Vector Machine (SVM). Standard Deviation,
Mean, Energy and Entropy are extorted using the histogram approach for each merger space. These features are
found to be higher in occurrence in the tumor region than the non-tumor one. MRI scans of the five brains with
60 slices from each are utilized for testing the proposed method’s authenticity. These brain images (230 slices as
normal and 70 abnormal) are accessed from the Internet Brain Segmentation Repository (IBSR) dataset. 60%
images for training and 40% for testing phase are used. Average classification accuracy as much as 98.02%
(training) and 98.19% (testing) are achieved.
post-operative Magnetic Resonance Images (MRI) requires accurate estimation and comparison. The active contour segmentation technique is used to segment brain tumors on pre-operative MR images using self-developed software. Tumor volume is acquired from its contours via alpha shape theory. The graphical user interface is developed for rendering, visualizing and estimating the volume of a brain tumor. Internet Brain Segmentation Repository dataset (IBSR) is employed to analyze and determine the repeatability and reproducibility of tumor volume. Accuracy of the method is validated by comparing the estimated volume using the proposed method with that of gold-standard. Segmentation by active contour technique is found to be capable of detecting the brain tumor boundaries. Furthermore, the volume description and visualization enable an interactive examination of tumor tissue and its surrounding. Admirable features of our results demonstrate that alpha shape theory in comparison to other existing standard methods is superior for precise volumetric measurement of tumor.
Developing an efficient algorithm for automated Magnetic Resonance Imaging (MRI) segmentation to characterize tumor abnormalities in an accurate and reproducible manner is ever demanding. This paper presents an overview of the recent development and challenges of the energy minimizing active contour segmentation model called snake for the MRI. This model is successfully used in contour detection for object recognition, computer vision and graphics as well as biomedical image processing including X-ray, MRI and Ultrasound images. Snakes being deformable well-defined curves in the image domain can move under the influence of internal forces and external forces are subsequently derived from the image data. We underscore a critical appraisal of the current status of semi-automated and automated methods for the segmentation of MR images with important issues and terminologies. Advantages and disadvantages of various segmentation methods with salient features and their relevancies are also cited.