5 research outputs found
Corner point detection for the map of kariah Kg. Bukit Kapar / Siti Sarah Raseli, Afina Amirhussain and Norpah Mahat
Corner point detection are the important technique for many image processing applications including image enhancement, object detection and pattern recognition. The purpose of this study is to detect the corner points of a map of Kariah Kampung Bukit Kapar image by using Harris Corner Detector. Corner points in an image represents a lot of important information of the image. Detection of corner points accurately is significant to image processing, which can reduce much of the calculations. In this study, the initial technique is smoothing the image and extract the boundary of the image. Then, Harris Corner Detector is used to detect the corner points by considering the amount of corner point detection and run time processing. This study proposed the Harris Corner Detector which can detect 154 points with 12.9552 second
A novel shape descriptor based on salient keypoints detection for binary image matching and retrieval
We introduce a shape descriptor that extracts keypoints from binary images and
automatically detects the salient ones among them. The proposed descriptor operates as
follows: First, the contours of the image are detected and an image transformation is used to
generate background information. Next, pixels of the transformed image that have specific
characteristics in their local areas are used to extract keypoints. Afterwards, the most salient
keypoints are automatically detected by filtering out redundant and sensitive ones. Finally,
a feature vector is calculated for each keypoint by using the distribution of contour points
in its local area. The proposed descriptor is evaluated using public datasets of silhouette
images, handwritten math expressions, hand-drawn diagram sketches, and noisy scanned
logos. Experimental results show that the proposed descriptor compares strongly against
state of the art methods, and that it is reliable when applied on challenging images such as
fluctuated handwriting and noisy scanned images. Furthermore, we integrate our descripto
A Measure of Similarity Between Trajectories of Vessels
The measurement of similarity between trajectories of vessels is one of the kernel problems that must be addressed to promote the development of maritime intelligent traffic system (ITS). In this study, a new model of trajectory similarity measurement was established to improve the data processing efficiency in dynamic application and to reflect actual sailing behaviors of vessels. In this model, a feature point detection algorithm was proposed to extract feature points, reduce data storage space and save computational resources. A new synthesized distance algorithm was also created to measure the similarity between trajectories by using the extracted feature points. An experiment was conducted to measure the similarity between the real trajectories of vessels. The growth of these trajectories required measurements to be conducted under different voyages. The results show that the similarity measurement between the vessel trajectories is efficient and correct. Comparison of the synthesized distance with the sailing behaviors of vessels proves that results are consistent with actual situations. The experiment results demonstrate the promising application of the proposed model in studying vessel traffic and in supplying reliable data for the development of maritime ITS
Image Feature Information Extraction for Interest Point Detection: A Comprehensive Review
Interest point detection is one of the most fundamental and critical problems
in computer vision and image processing. In this paper, we carry out a
comprehensive review on image feature information (IFI) extraction techniques
for interest point detection. To systematically introduce how the existing
interest point detection methods extract IFI from an input image, we propose a
taxonomy of the IFI extraction techniques for interest point detection.
According to this taxonomy, we discuss different types of IFI extraction
techniques for interest point detection. Furthermore, we identify the main
unresolved issues related to the existing IFI extraction techniques for
interest point detection and any interest point detection methods that have not
been discussed before. The existing popular datasets and evaluation standards
are provided and the performances for eighteen state-of-the-art approaches are
evaluated and discussed. Moreover, future research directions on IFI extraction
techniques for interest point detection are elaborated