Titlebook: Computer Vision Systems; 12th International C Dimitrios Tzovaras,Dimitrios Giakoumis,Antonis Arg Conference proceedings 2019 Springer Natur

Modicum

6D Gripper Pose Estimation from RGB-D Imagek frame is given subsequently and the training with unique loss function adjusts the model to desired accuracy. Testing on both known and unknown objects verifies our system when it comes to grasping precision.

cipher

Estimation of Wildfire Size and Location Using a Monocular Camera on a Semi-autonomous Quadcopters, with respect to the edges of the fire, will result in better performance and higher accuracy. Placing the monocular camera horizontally in relation to the drone will provide an accuracy of 68.20%, while mounting the camera with an angle, will deliver an accuracy of 60.76%.

龍卷風(fēng)

善變

Real-Time Binocular Vision Implementation on an SoC TMS320C6678 DSPicient binocular vision system implemented on an SoC TMS320C6678 DSP for real-time depth information extrapolation, where the search range propagates from the bottom of an image to its top. To further improve the stereo matching efficiency, the cost function is factorized into five independent parts

主講人

Real-Time Lightweight CNN in Robots with Very Limited Computational Resources: Detecting Ball in NAOning balls in various scenarios to address the most basic and key issue in robot soccer games: detecting the ball. In contrast to the existing ball detection methods base on traditional machine learning and image processing, this paper presents a lightweight CNN object detection approach for CPU. In

INCH

critic

Feature-Agnostic Low-Cost Place Recognition for Appearance-Based Mapping methods are widely adopted. A common trend among most of these methods is that they are either tailored to work in specific environments or need prior training overhead [.]. Whereas, others demand extreme computational resources, such as CNN [.]. In this paper, we study the existing GSOM-based plac

Lipoma

Semi-semantic Line-Cluster Assisted Monocular SLAM for Indoor Environmentsknowledge that in the indoor environment, the line segments form tight clusters, e.g. many door frames in a straight corridor are of the same shape, size and orientation, so the same edges of these door frames form a tight line segment cluster. We implement our method in the popular ORB-SLAM2, which

Aerophagia

Appearance-Based Loop Closure Detection with Scale-Restrictive Visual Featureses for image representation with a view to reduce the computational cost. In order to achieve this, a training process is performed, where a feature matching technique indicates the features’ repeatability with respect to scale. Votes are distributed into the database through a nearest neighbor meth

連系