Identifying Top Topics in Text Stream Data – This paper presents a novel dataset of Top Topic Images based on the deep neural network (DNN) architecture. We first tackle the problem of image parsing which is the task of extracting relevant information from text. In this paper, we provide an efficient algorithm for image parsing by leveraging deep neural network. We also develop two novel algorithms: A first one which applies a deep learning technique (with no training data) and a second algorithm that combines a deep learning technique (with no training data) and a novel deep learning technique (with no training data). We compare our algorithm with state-of-the-art CNNs and show that our algorithm is much faster, and provides better results for decoding large-scale images. The results reveal that our method outperforms the state-of-the-art CNN parsing method.
A new computer vision tool called 3D-D Foreground Search (3D) has been developed to assist users in managing complex cluttered and clutter-laden objects. The key to this tool is to discover the 3D feature representation of clutter based on 2D point estimates of the surrounding objects and a 3D point model of the objects. Based on the 3D feature representation, 2D model of clutter is identified in a grid of various sizes, and a 3D model of clutter is considered by the user. The user can then create clutter objects and perform the search to locate those objects. The 3D feature representation and the clutter object knowledge are retrieved using a hierarchical system.
Polar Quantization Path Computations
Fast Riemannian k-means, with application to attribute reduction and clustering
Identifying Top Topics in Text Stream Data
Improving Bayesian Compression by Feature Selection
Inventory of 3D Point Cloud Segments and 3D Point Modeling using RGB-D CameraA new computer vision tool called 3D-D Foreground Search (3D) has been developed to assist users in managing complex cluttered and clutter-laden objects. The key to this tool is to discover the 3D feature representation of clutter based on 2D point estimates of the surrounding objects and a 3D point model of the objects. Based on the 3D feature representation, 2D model of clutter is identified in a grid of various sizes, and a 3D model of clutter is considered by the user. The user can then create clutter objects and perform the search to locate those objects. The 3D feature representation and the clutter object knowledge are retrieved using a hierarchical system.