Abstract—Detecting and segmenting salient objects in natural scenes, also known as salient object detection, has attracted a lot of focused research in computer vision and has resulted in many applications. However, while many such models exist, a deep understanding of achievements and issues is lacking. We aim to provide a comprehensive review of the recent progress in this field. We situate salient object detection among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 256 publications we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics in salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance and suggest future research directions. Index Terms—Salient object detection, salient region detection, saliency, explicit saliency, visual attention, regions of interest,

Abstract—This paper introduces a novel weighted unsuper-vised learning for object detection using an RGB-D camera. This technique is feasible for detecting the moving objects in the noisy environments that are captured by an RGB-D camera. The main contribution of this paper is a real-time algorithm for detecting each object using weighted clustering as a separate cluster. In a preprocessing step, the algorithm calculates the pose 3D position X, Y, Z and RGB color of each data point and then it calculates each data point’s normal vector using the point’s neighbor. After preprocessing, our algorithm calculates k-weights for each data point; each weight indicates membership. Resulting in clustered objects of the scene.