In Vision Simultaneous Localization and Mapping (VSLAM), feature detection is used in landmark extraction and data association. It examines each pixel to find interesting part of an image that would differentiate the landmark and the less important image details. There are numerous studies in this field but they are scattered in many journals and proceedings which would require many hours just to find related material. Therefore, this research has grouped important studies done in this field to be analyzed by future researcher. Feature detection techniques such as Harris, Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Features from Accelerated Segment Test (FAST) and etc. is discussed in this study. A background history of each technique, their evolution and performance comparison is presented.

Cartoon pirate uploading is a very serious problem for the image and video-sharing website. In this study, we propose a new method to detect the characters in 2D-cartoon images, aiming at rejecting pirate uploading automatically. We extract the curve in the cartoon image as the main content and then design a local shape feature named Scalable-Shape Context (SSC) to present the local shape of cartoon. Firstly, we use the Harris-Laplace corner detector to find the key points at multi-scale in the cartoon image, most of which are localized at the junctions of curves. Secondly, the scale of each key point is used as a reference scale for Shape Context (SC) to describe the curvilinear structure around the key points. Then, the matching problem between the key points extracted from the input model and testing image is solved as an optimal assignment problem. Finally, a Hough-voting scheme is employed to find the …

This study presented a new classification method for single person’s motion, which is represented by Haar wavelet transform and classified by Hidden Markov Model. We tackle the challenge of detecting the feature points by Haar wavelet transform to improve classification accuracy. We extract binary silhouette and segment them by cycle after creating the background model. Then the low-level features are detected by Haar wavelet transform and principal vectors are determined by Principal Component Analysis. We utilize Hidden Markov Models to train and classify cycle sequences and demonstrate their usability. Compared with others, our approach is simple and effective in feature point detection, strength in scale-invariant and generalized in different motions. Therefore, the video surveillance based on our method is practicable in (but not limited to) many scenarios where the background is known.

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