# HSV Color-Space-Based Automated Object Localization for Robot Grasping without Prior Knowledge
## 2 Related Work
### Feature Extraction and Region-Proposal-Based Algorithms  
The features of the object extracted from the image mainly utilize color and structural feature points (keypoints). A typical method that utilizes color characteristics is the color histogram method [21]. It has the advantage that color information can be grouped and mapped in object similarity discrimination and is not affected by rotation or changes in the position [22]. However, if the color information does not include spatial information, it is discriminated from another image. The structural features of an object can become its local invariance features, mainly using corners (i.e., keypoints, interest points, salient points, and feature points) [23]. To be a good feature point, it must be resilient to changes in the shape, size, and position of the object, and it must be detectable even if the camera's viewpoint and lighting change. The Harris corner [24] detection has robust performance against changes in translation, rotation, intensity shift, and illumination. However, because it is sensitive to image scaling,it has the disadvantage of the necessity to extract feature points from images of various scales. The histogram of oriented gradients (HOG) [25] is an algorithm that divides the target area into cells of a certain size and extracts and classifies objects with a histogram of the direction components of the cell gradation. HOG has some robust characteristics against local changes. The histogram is normalized by its neighboring cells to increase the robustness to texture and illumination variations [26]. The scale-invariant feature transform (SIFT) is an algorithm that extracts features that do not change in image size and rotation. This algorithm extracts SIFT features from two different images, matches the features closest to each other, finds the corresponding part, and maps them. It is robust against distortion, noise, and lighting changes, but it has the disadvantages of complicated computation and slow speed [27; 28]. [PERSON] [29] found the optimal boundary value that divides the image into two classes based on the observed distribution of pixel values. The threshold is set by maximizing the dispersion between the background and object. This algorithm is very effective when a single object has a different brightness distribution than the background, but when the internal brightness distribution of the object is not constant, only a part of the object is detected. GrabCut [30] is a graph-cut-based partitioning algorithm used to separate the foreground and background of an image. This algorithm undertakes image segmentation based on the Grab cut [31; 32] algorithm, which uses the distribution of user-specified regions to minimize the energy of each pixel and find zones. However, GrabCut is not an automatic object extraction algorithm but one that requires the user to intervene and extract the required object. The convolutional neural network (CNN) [33] technique is a widely used deepening algorithm used to detect objects such as images, videos, texts, and voices. Face recognition algorithms can be applied to discover changes in lighting angles, brightness, facial expressions, poses, etc. based on CNN [34]. However, large visual variations of faces, such as occlusions, pose variations, and illumination changes, pose great challenges for this task in real applications [35]. To minimize external environmental changes, deep learning algorithms such as R-CNN [36], Fast R-CNN [37], and YOLO [38] have attracted attention in recent years [39; 40; 41]. However, deep-learning-based object detection has many constraints, such as a large amount of training and test data, a long learning time, an annotation task, and a large amount of calculation. Object detection determines object classification and localization in an image. Owing to large variations in viewpoints, poses, occlusions, and lighting conditions, it is difficult to perfectly accomplish object detection with an additional object localization task [39].