35 research outputs found
Tencent AVS: A Holistic Ads Video Dataset for Multi-modal Scene Segmentation
Temporal video segmentation and classification have been advanced greatly by
public benchmarks in recent years. However, such research still mainly focuses
on human actions, failing to describe videos in a holistic view. In addition,
previous research tends to pay much attention to visual information yet ignores
the multi-modal nature of videos. To fill this gap, we construct the Tencent
`Ads Video Segmentation'~(TAVS) dataset in the ads domain to escalate
multi-modal video analysis to a new level. TAVS describes videos from three
independent perspectives as `presentation form', `place', and `style', and
contains rich multi-modal information such as video, audio, and text. TAVS is
organized hierarchically in semantic aspects for comprehensive temporal video
segmentation with three levels of categories for multi-label classification,
e.g., `place' - `working place' - `office'. Therefore, TAVS is distinguished
from previous temporal segmentation datasets due to its multi-modal
information, holistic view of categories, and hierarchical granularities. It
includes 12,000 videos, 82 classes, 33,900 segments, 121,100 shots, and 168,500
labels. Accompanied with TAVS, we also present a strong multi-modal video
segmentation baseline coupled with multi-label class prediction. Extensive
experiments are conducted to evaluate our proposed method as well as existing
representative methods to reveal key challenges of our dataset TAVS
A digital image analysis of gravel aggregate using CT scanning technique
Particle shape was one of the most important factors which affects the gravel aggregate’s properties. It was also one of the important factors that directly affects the performance of asphalt pavements. In this paper, the gravel aggregate of quartzite was studied by using the industrial CT instrument. MATLAB was used to capture the aggregate slice properties including reverse color, median filtering, noise reduction, binarization and so on. The 3D aggregate model was reconstructed by using the software of MIMICS. The three-dimensional model of the aggregate was further optimized. The best fitting cuboid, cylinder, cone and sphere information of the aggregate were obtained by using the characteristics analysis function. Keywords: CT scanning, 3D reconstruction, Watershed transform, Particle shape, Gravel aggregat