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Objective
Tracking seizures is crucial for epilepsy monitoring and treatment evaluation. Current epilepsy care relies on caretaker seizure diaries, but clinical seizure monitoring may miss seizures. Wearable devices may be better tolerated and more suitable for long‐term ambulatory monitoring. This study evaluates the seizure detection performance of custom‐developed machine learning (ML) algorithms...
This paper presents an efficient framework to perform recognition and grasp detection of objects from RGB-D images of real scenes. The framework uses a novel architecture of hierarchical cascaded forests, in which object-class and grasp-pose probabilities are computed at different levels of an image hierarchy (e.g., patch and object levels) and fused to infer the class and the grasp of unseen objects...
This paper presents an efficient system for simultaneous dense scene reconstruction and object labeling in real-world environments (captured with an RGB-D sensor). The proposed system starts with the generation of object proposals in the scene. It then tracks spatio-temporally consistent object proposals across multiple frames and produces a dense reconstruction of the scene. In parallel, the proposed...
This paper presents a novel object segmentation approach for highly complex indoor scenes. Our approach starts with a novel algorithm which partitions the scene into distinct regions whose boundaries accurately conform to the physical object boundaries in the scene. Next, we propose a novel perceptual grouping algorithm based on local cues (e.g., 3D proximity, co-planarity, and shape convexity) to...
This paper presents an efficient approach to recognize objects captured with an RGB-D sensor. The proposed approach uses a Bag-of-Words (BOW) model to learn feature representations from raw RGB-D point clouds in a weakly supervised manner. To this end, we introduce a novel method based on randomized clustering trees to learn visual vocabularies which are fast to compute and more discriminative compared...
This paper presents an efficient framework for the categorization of objects in real-world scenes (captured with an RGB-D sensor). The proposed framework uses ensembles of randomized decision trees in a hierarchical cascaded architecture to compute consistent object-class inferences of unseen objects. Specifically, the proposed framework computes object-class probabilities at three levels of an image...
We address the problem of object segmentation from depth images of highly complex indoor scenes. We propose a model-free segmentation approach, which robustly separates unknown stacked objects in real-world scenes. Our approach constructs geometrically constrained 3D clusters known as salient-regions, which are subsequently merged into high-level object hypotheses by analyzing the local geometrical...
Using full scale (480×640) RGB-D imagery, we here present an approach for tracking 6d pose of rigid objects at runtime frequency of up to 15fps. This approach is useful for robotic perception systems to efficiently track object's pose during camera movements in tabletop manipulation tasks with high detection rate and real-time performance. Specifically, appearance-based feature correspondences are...
This paper delivers a study on the motion analysis of a walking robot through dynamic simulation of biologically inspired walking gaits. We described a simple method of gait generation which enabled the robot to configure its pose at different orientations with adaptive leg stroke and stride lengths in our earlier work [12]. After having described a suitable gait generation method in [12], we now...
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