This paper describes a new representation for people and animals, called a body plan. The representation is an organized collection of grouping hints obtained from constraints on color, texture, shape, and geometrical relations. Body plans can be learned from image data, using established statistical learning techniques. Body plans are well adapted to segmentation and recognition in complex env...

The present study tries to investigate the effect of mental imagery on learning and improvement of forehand and backhand skills of the better hand and then transmitting them to the weaker hand among table tennis players. In the present cross-sectional research, out of120 students, 45 students were selected in a randomized way. Then, they were randomly divided into three mental imagery, physical...

Can you easily form mental pictures from the descriptions of scenery that are so frequently met with in novels and books of travel? More than 100 years ago Francis Galton [1] sent out a list of questions, including this one, to gather statistics on the character of mental imagery. Many people reported vivid mental imagery, but not the scientists on his list. “To my astonishment”, Galton reporte...

The advancement of sensor technology enables fast and cost-effective acquisition of spatial data. This paper provides a review of the state-of-the-art development of mobile mapping technology in North America. Following a background introduction, multi-sensor integrated mapping technology is described. The key concept of direct georeferencing is given. The sensors as well as their accuracy aspe...

3D spatial information is known to be beneficial the semantic segmentation task. Most existing methods take data as an additional input, leading a two-stream network that processes RGB and separately. This solution greatly increases inference time severely limits its scope for real-time applications. To solve this problem, we propose Spatial guided Convolution (S-Conv), which allows efficient f...

Generalized zero-shot Learning (GZSL) aims to recognize images from either seen or unseen domain, mainly by learning a joint embedding space associate image features with the corresponding category descriptions. Recent methods have proved that localizing important object regions can effectively bridge semantic-visual gap. However, these are all based on one-off visual localizers, lacking of int...

This paper summarizes our recent research works on semantic video search. Our solution to this exciting topic is grounded on concept-based video search, which attempts to narrow the semantic gap by first indexing the video content with a large number of pre-trained semantic concepts, and then employing these concepts to respond user’s queries (query-by-concept). We developed novel and effective...

Every information system has its own domain model for its environment and efficient task operation, which results in diverse heterogeneities, especially the semantic Heterogeneity. In this paper, order to adapt to the data request model of railway system, and semantic conflict type existing in the process of data integration, Ontology technology is employed in heterogeneous information integrat...