## 2 Research Background
A decision tree algorithm, specifically iterative dichotomiser3, selects the three pixels. Similar to other methods, the FAST was used for facial recognition, specifically in videos. In [56], the FAST was used to detect local features for omnidirectional vision. However, matching was only performed between the features of the panoramic images instead of matching the features of the original objects to those in the panoramic images. To identify the most important region in an image, the region of interest (ROI), [59] used the FAST to distinguish between important and unimportant content in the image. In [58], the FAST was also used to approximate ROI detection of fetal genital organs in B-mode ultrasound images. In [57], the FAST was used to extract keypoints of pine floorboard images to guide a wood patching robot. The maximally stable extremal regions (MSER) detection algorithm detects the invariant stable extremal regions. The extremal region is closed under continuous and monotonic transformations of image coordinates and intensities. The MSER is defined as the property of the intensity function on the outer boundary. Local minima of the changing rate for the area function are the intensity levels that are the thresholds for producing MSER. This is represented by the position of the local intensity maximum or minimum and a threshold. Because of the MSER's invariant and stable properties, it has been used in many applications, such as text and image segmentation [47; 48; 49; 50], video fingerprinting [51], human tracking [52] and object recognition [53]. The histograms of oriented gradient (HOG) evaluate normalized local histograms of gradient orientations on a dense grid. It uses overlapping local contrast normalizations for improving performance. The HOG feature has significant effects for detecting humans and vehicles in conjunction with machine learning algorithms [41; 42; 43; 44; 45]. One interesting application used HOG features to detect landmines in ground-penetrating radar [40]. The HOG was also used for facial expression recognition [39] and other object recognition, such as plant leaves [37], handwritten digits [38], and off-line signatures [36]. The BRISK descriptor is generated from a configurable circular sampling pattern. It is obtained by computing brightness comparisons. To improve the accuracy of facial detection against pose variation, in [26], the feature descriptors of binary robust invariant scalable keypoints (BRISK) were used to register facial images, which are processed prior to facial recognition. The BRISK also has adequate performance for recognizing gender [31] and people [30] using facial and ocular images. In [29], the BRISK was used to recognize appearance-based places for collaborative robot localization. Similar to the BRISK, the sampling pattern that is used for the fast retina keypoint (FREAK) is based on Gaussians and is derived from the retina in the human eye. Because its low computation cost is an advantage, the FREAK was used in multimedia for image and video classification [63; 64]. In target-matching recognition for satellite images, the recognition had poor accuracy and robustness because of its unfit feature detector and large amount of data. Accordingly, FREAK was used to improve target-matching recognition for satellite remote sensing images [65]. To prevent the distribution of pornography, the FREAK descriptor was used to detect pornographic images in conjunction with the bag-of-words model [61; 62]. In [71], the authors explored the use of the query-by-picture technology for artwork recognition. The SURF and eigenpaintings are combined to recognize the artwork images on mobile phones. However, the approach required a preprocessing step to extract the foreground from the image. The artwork recognition was only performed on the foreground. The artwork segmentation relied on the fact that the artwork was presented on a uniform white background. However, the artworks in 360-degree images are surrounded by various objects.