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Estimating people visual focus of attention (VFOA) plays a crucial role in various practical systems such as human–robot interaction. It is challenging to extract the cue of the VFOA of a person due to the difficulty of recognizing gaze directionality. In this paper, we propose an improved integrodifferential approach to represent gaze via efficiently and accurately localizing the eye center in lower...
AICNN architecture is presented in this work to map the state-of-the-art machine-learning algorithms of CNN to power-constrained embedded hardware. As the combination of analog-to-information conversion and typical CNN algorithms, AICNN can realize ultra-highly efficient computation by using massive parallel analog signal processing circuits, which could also significantly reduce ADC devices cost...
Robust scene understanding of outdoor environments using passive optical sensors is a onerous and essential task for autonomous navigation. The problem is heavily characterized by changing environmental conditions throughout the day and across seasons. Robots should be equipped with models that are impervious to these factors in order to be operable and more importantly to ensure safety in the real-world...
Convolutional neural networks (CNN) are a deep learning technique that has achieved state-of-the-art prediction performance in computer vision and robotics, but assume the input data can be formatted as an image or video (e.g. predicting a robot grasping location given RGB-D image input). This paper considers the problem of augmenting a traditional CNN for handling image-like input (called main-channel...
Pouring a specific amount of liquid is a challenging task. In this paper we develop methods for robots to use visual feedback to perform closed-loop control for pouring liquids. We propose both a model-based and a model-free method utilizing deep learning for estimating the volume of liquid in a container. Our results show that the model-free method is better able to estimate the volume. We combine...
Emotion recognition is critical for everyday living and is essential for meaningful interaction. If we are to progress towards human and machine interaction that is engaging the human user, the machine should be able to recognize the emotional state of the user. Deep Convolutional Neural Networks (CNN) have proven to be efficient in emotion recognition problems. The good degree of performance achieved...
No matter the end-effector control of joint robots or the pose control of mobile robots, the image feature detection is the critical component of these robot technologies. Moreover, the interest point is the most widely used feedback signal in visual servo control, which performances are directly effected by the actual execution time of the feature detection methods. However, in the robot visual servo...
Keypoint detection and matching is of fundamental importance for many applications in computer and robot vision. The association of points across different views is problematic because image features can undergo significant changes in appearance. Unfortunately, state-of-the-art methods, like the scale-invariant feature transform (SIFT), are not resilient to the radial distortion that often arises...
A conceptual medical robotic system applicable for establishing surgical platform in the process of minimally invasive spine surgery (MISS) is proposed in this paper. According to the requirement of MISS operation, the surgical navigation system is established. The navigation system proposed a set of image intensification algorithm, which can enhance the images visual effect and surgery precision...
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