Super pixel and objectness algorithms are broadly used as a pre-processing step to generate support regions and to speed-up further computations. Recently, many algorithms have been extended to video in order to exploit the temporal consistency between frames. However, most methods are computationally too expensive for real-time applications. We introduce an online, real-time video super pixel algorithm based on the recently proposed SEEDS super pixels. A new capability is incorporated which delivers multiple diverse samples (hypotheses) of super pixels in the same image or video sequence. The multiple samples are shown to provide a strong cue to efficiently measure the objectness of image windows, and we introduce the novel concept of objectness in temporal windows. Experiments show that the video super pixels achieve comparable performance to state-of-the-art offline methods while running at 30 fps on a single 2.8 GHz i7 CPU. State-of-the-art performance on objectness is also demonstrated, yet orders of magnitude faster and extended to temporal windows in video.

Depth sensors have become increasingly popular in interactive computer vision applications. Currently, most of these applications are limited to indoor use. Popular IR-based depth sensors cannot provide depth data when exposed to sunlight. In these cases, one can still obtain depth information using a stereo camera set up or a special outdoor Time-of-Flight camera, at the cost of a reduced quality of the depth image. The resulting depth images are often incomplete and suffer from low resolution, noise and missing information. The aim of this paper is to recover the missing depth information based on an extension of SEEDS superpixels [11]. The superpixel segmentation algorithm is extended to take depth information into account where available. The approach takes advantage of the boundary-updating property of SEEDS. The result is a clean segmentation that recovers the missing depth information in a low-quality depth image. We test the approach outdoors on an interactive urban robot. The system is used to segment a person in front of the robot, and to detect body parts for interaction with the robot using pointing gestures.

The use of depth is becoming increasingly popular in real-time computer vision applications. However, when using real-time stereo for depth, the quality of the disparity image is usually insufficient for reliable segmentation. The aim of this paper is to obtain a more accurate and at the same time faster segmentation by incorporating color, depth and optical flow. A novel real-time superpixel segmentation algorithm is presented which uses real-time stereo and realtime optical flow. The presented system provides superpixels which represent suggested object boundaries based on color, depth and motion. Each outputted superpixel has a 3D location and a motion vector, and thus allows for straightforward segmentation of objects by 3D position and by motion direction. In particular, it enables reliable segmentation of persons, and of moving hands or arms. We show that our method is competitive with the state of the art while approaching real-time performance.

This paper implements a real-time hand gesture recognition algorithm based on the inexpensive Kinect sensor. The use of a depth sensor allows for complex 3D gestures where the system is robust to disturbing objects or persons in the background. A Haarlet-based hand gesture recognition system is implemented to detect hand gestures in any orientation, and more in particular pointing gestures while extracting the 3D pointing direction. The system is integrated on an interactive robot (based on ROS), allowing for real-time hand gesture interaction with the robot. Pointing gestures are translated into goals for the robot, telling him where to go. A demo scenario is presented where the robot looks for persons to interact with, asks for directions, and then detects a 3D pointing direction. The robot then explores his vicinity in the given direction and looks for a new person to interact with.

Time-of-Flight (ToF) and other IR-based cameras that register depth are becoming more and more affordable in consumer electronics. This paper aims to improve a realtime hand gesture interaction system by augmenting it with a ToF camera. First, the ToF camera and the RGB camera are calibrated, and a mapping is made from the depth data to the RGB image. Then, a novel hand detection algorithm is introduced based on depth and color. This not only improves detection rates, but also allows for the hand to overlap with the face, or with hands from other persons in the background. The hand detection algorithm is evaluated in these settings, and compared to previous algorithms. Furthermore, the depth information allows us to track the position of the hand in 3D, allowing for more interesting modes of interaction. Finally, the hand gesture recognition algorithm is applied to the depth data as well, and compared to the recognition based on the RGB images. The result is a real-time hand gesture interaction system that allows for complex 3D gestures and is not disturbed by objects or persons in the background.

This paper presents a system to manipulate 3D objects or navigate through 3D models by detecting the gestures and the movements of the hands of a user in front of a camera mounted on top of a screen. This paper more particularly introduces an improved skin color segmentation algorithm which combines an online and an offline model; and a Haarlet-based hand gesture recognition system, where the Haarlets are trained based on Average Neighborhood Margin Maximization (ANMM). The result is a real-time marker-less interaction system which is applied to two applications, one for manipulating 3D objects, and the other for navigating through a 3D model.