3D Dental Mesh Segmentation Using Semantics-Based Feature Learning with Graph-Transformer
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Acquiring accurate silhouettes has many applications in computer vision. This is usually done through motion detection, or a simple background subtraction under highly controlled environments (i.e. chroma-key backgrounds). Lighting and contrast issues in typical outdoor or office environments make accurate segmentation very difficult in these scenes. In this paper, gradients are used in conjunction with intensity and colour to provide a robust segmentation of motion, after which graph cuts are utilised to refine the segmentation. The results presented using the ETISEO database demonstrate that an improved segmentation is achieved through the combined use of motion detection and graph cuts, particularly in complex scenes.
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We present an approach for object segmentation in videos that combines frame-level object detection with concepts from object tracking and motion segmentation. The approach extracts temporally consistent object tubes based on an off-the-shelf detector. Besides the class label for each tube, this provides a location prior that is independent of motion. For the final video segmentation, we combine this information with motion cues. The method overcomes the typical problems of weakly supervised/unsupervised video segmentation, such as scenes with no motion, dominant camera motion, and objects that move as a unit. In contrast to most tracking methods, it provides an accurate, temporally consistent segmentation of each object. We report results on four video segmentation datasets: YouTube Objects, SegTrackv2, egoMotion, and FBMS.
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Now-a-days for an intelligent surveillance system, identification of an object from a video has attracted a great deal of interest. Some segmentation techniques are needed to perform to detect an object from a video. Two essential building block of smart surveillance system in real time application are object segmentation and object detection. This method is proposed on a multi object moving background based on morphological technique and cellular automata based segmentation. The video is preprocessed before segmentation. Motion segmentation is done to segment an object from a video. In this, a Morphological operation is proposed to remove unwanted object motion and enhance the segmentation result. This result is then used for object identification. Cellular automata based segmentation is performed to detect particular object from a video. This method can detect any object at any drastic change in illumination.
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Object segmentation is one of the vital tasks in various three-dimensional applications. The paper presents a hybrid object segmentation algorithm to combine intensity segmentation and disparity segmentation in a stereoscopic vision system. First, the disparity maps of the stereo images are estimated using a foreground-based disparity estimation method. Then, the intensity stereo images and their corresponding disparity maps are separately segmented using a region-growing technique. The real segmentation mask can be obtained and the semantic object be extracted by a fusion processing on the intensity and disparity segments. Computer simulations indicate the reliable performance of the proposed algorithm for stereoscopic segmentation.
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Video object segmentation is a task to distinguish the foreground from the background in videos. Most previous research on automatic video object segmentation based on graphcut segmentation uses the motion cue and the color cue to separate the background from the foreground. Consequently, the segmentation result deteriorates when the motion and/or the color becomes disordered, which typically occurs when a moving object stops and when a light is switched on/off. This paper proposes a new automatic video segmentation method robust to unstable motion and color. To achieve robustness, the graphcut segmentation is supported by the SURF feature, which is highly invariant to the change of scale, rotation, and luminance. In particular, our method matches the SURF features between two consecutive frames and modifies the segmentation result when the matched SURF features are assigned different labels.
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We present an approach for autonomous interactive object segmentation by a humanoid robot. The visual segmentation of unknown objects in a complex scene is an important prerequisite for e.g. object learning or grasping, but extremely difficult to achieve through passive observation only. Our approach uses the manipulative capabilities of humanoid robots to induce motion on the object and thus integrates the robots manipulation and sensing capabilities to segment previously unknown objects. We show that this is possible without any human guidance or pre-programmed knowledge, and that the resulting motion allows for reliable and complete segmentation of new objects in an unknown and cluttered environment. We extend our previous work, which was restricted to textured objects, by devising new methods for the generation of object hypotheses and the estimation of their motion after being pushed by the robot. These methods are mainly based on the analysis of motion of color annotated 3D points obtained from stereo vision, and allow the segmentation of textured as well as non-textured rigid objects. In order to evaluate the quality of the obtained segmentations, they are used to train a simple object recognizer. The approach has been implemented and tested on the humanoid robot ARMAR-III, and the experimental results confirm its applicability on a wide variety of objects even in highly cluttered scenes.
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This paper presents a new algorithm for video-object segmentation, which combines motion-based segmentation, high-level object-model detection, and spatial segmentation into a single framework. This joint approach overcomes the disadvantages of these algorithms when applied independently. These disadvantages include the low semantic accuracy of spatial segmentation and the inexact object boundaries obtained from object-model matching and motion segmentation. The now proposed algorithm alleviates three problems common to all motion-based segmentation algorithms. First, it completes object areas that cannot be clearly distinguished from the background because their color is near the background color. Second, parts of the object that are not considered to belong to the object since they are not moving, are still added to the object mask. Finally, when several objects are moving, of which only one is of interest, it is detected that the remaining regions do not belong to any object-model and these regions are removed from the foreground. This suppresses regions erroneously considered as moving or objects that are moving but that are completely irrelevant to the user.
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The task of correctly tracking the body parts is one of the crucial problems in the human body pose modelling. Various factors need to be investigated as the variety of body poses is unlimited and the visual appearance varies according to the environment. The human body can be composed into several parts such as the head, torso, arms and legs. The arms can be considered as the most challenging body part to be tracked since it tends to move fast and usually occluded within other body parts. This paper addresses the problem of extracting the arms which are occluded in the torso part. A wavelet-based skin segmentation method is applied to detect the skin region. The segmentation procedure is performed using six different colour spaces namely the RGB, rgb, HSI, TSL, SCT and CIELAB. The segmentation performances are evaluated on colour component basis. The aim of this paper is to determine the best colour components that are suitable for this segmentation procedure.
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