Other affiliations: University of Malaya
Bio: Haniza Yazid is an academic researcher from Universiti Malaysia Perlis. The author has contributed to research in topics: Thresholding & Image segmentation. The author has an hindex of 14, co-authored 53 publications receiving 612 citations. Previous affiliations of Haniza Yazid include University of Malaya.
Papers published on a yearly basis
TL;DR: Analysis based on Monte Carlo statistical method shows that the success of image segmentation depends on object-background intensity difference, object size and noise measurement, however is unaffected by location of the object on that image.
TL;DR: In this article, moment invariant is used to identify the object from the captured image using the first invariant (O1) and the recognition rate for this technique is 90% after the image undergoes suitable processing and segmentation process.
Abstract: Geometric moment invariant produces a set of feature vectors that are invariant under shifting, scaling and rotation. The technique is widely used to extract the global features for pattern recognition due to its discrimination power and robustness. In this paper, moment invariant is used to identify the object from the captured image using the first invariant (O1). The recognition rate for this technique is 90% after the image undergoes suitable processing and segmentation process.
TL;DR: The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.
TL;DR: This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding, which outperforms a method based on watershed segmentation.
Abstract: This paper presents a new approach to detect exudates and optic disc from color fundus images based on inverse surface thresholding The strategy involves the applications of fuzzy c-means clustering, edge detection, otsu thresholding and inverse surface thresholding The main advantage of the proposed approach is that it does not depend on manually selected parameters that are normally chosen to suit the tested databases When applied to two sets of databases the proposed method outperforms a method based on watershed segmentation
TL;DR: A new approach to create an adaptive threshold surface is proposed to segment an image and is inspired by the Yanowitz's method and is improved upon by the introduction of a simpler and more accurate threshold surface.
TL;DR: This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease.
Abstract: This paper addresses the automatic detection of microaneurysms in color fundus images, which plays a key role in computer assisted diagnosis of diabetic retinopathy, a serious and frequent eye disease. The algorithm can be divided into four steps. The first step consists in image enhancement, shade correction and image normalization of the green channel. The second step aims at detecting candidates, i.e. all patterns possibly corresponding to MA, which is achieved by diameter closing and an automatic threshold scheme. Then, features are extracted, which are used in the last step to automatically classify candidates into real MA and other objects; the classification relies on kernel density estimation with variable bandwidth. A database of 21 annotated images has been used to train the algorithm. The algorithm was compared to manually obtained gradings of 94 images; sensitivity was 88.5% at an average number of 2.13 false positives per image.
TL;DR: This study combined support vector machine and improved dragonfly algorithm to forecast short-term wind power for a hybrid prediction model and has shown better prediction performance compared with the other models such as back propagation neural network and Gaussian process regression.
TL;DR: An improved Otsu method, named the weighted object variance (WOV), is proposed in this research to detect defects on product surfaces and provides better segmentation results.
TL;DR: The proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling Feature Selection (FS) problems.
Abstract: Dragonfly Algorithm (DA) is a recent swarm-based optimization method that imitates the hunting and migration mechanisms of idealized dragonflies. Recently, a binary DA (BDA) has been proposed. During the algorithm iterative process, the BDA updates its five main coefficients using random values. This updating mechanism can be improved to utilize the survival-of-the-fittest principle by adopting different functions such as linear, quadratic, and sinusoidal. In this paper, a novel BDA is proposed. The algorithm uses different strategies to update the values of its five main coefficients to tackle Feature Selection (FS) problems. Three versions of BDA have been proposed and compared against the original DA. The proposed algorithms are Linear-BDA, Quadratic-BDA, and Sinusoidal-BDA. The algorithms are evaluated using 18 well-known datasets. Thereafter, they are compared in terms of classification accuracy, the number of selected features, and fitness value. The results show that Sinusoidal-BDA outperforms other proposed methods in almost all datasets. Furthermore, Sinusoidal-BDA exceeds three swarm-based methods in all the datasets in terms of classification accuracy and it excels in most datasets when compared in terms of the fitness function value. In a nutshell, the proposed Sinusoidal-BDA outperforms the comparable feature selection algorithms and the proposed updating mechanism has a high impact on the algorithm performance when tackling FS problems.
TL;DR: The success rates obtained on a previously constructed benchmark dataset are quite promising, implying that the cellular automaton image can help to reveal some inherent and subtle features deeply hidden in a pile of long and complicated amino acid sequences.