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Cascade Deformable Part Models (DPMs) are cascade frameworks to speed up Deformable Part Models (DPMs), which are one of the state-of-the-art solutions for object detection. Its idea is to reject most non-object hypotheses from the early stages of detection process. By investigating the dependency between hypotheses over scales, we introduce a novel pruning method to accelerate Cascade DPM frameworks.

In this paper, an image-based walkthrough system is presented for navigating real-world outdoor scenes based on only three uncalibrated images without reconstructing 3D model. An image-based rendering operation is performed on these sample images and generates photorealistic in-between views in real time. We extend the traditional two-step view morphing to real-time tri-view morphing based on epipolar constraint.

In this paper, we deal with geometric model fitting problems on graphs, where each vertex represents a model hypothesis,and each edge represents the similarity between two model hypotheses. Conventional median-shift methods are very efficient and they can automatically estimate the number of clusters. However, they assign the same weighting scores to all vertices of a graph, which can not show the discriminability on different vertices. Therefore, we propose a novel weighted median-shift on graphs method (WMSG) to fit and segment multiple-structure data.

The problem of human activity recognition can be approached using spatio-temporal variations in successive video frames. In this paper, a new human action recognition technique is proposed using multi-view videos. Initially, a naive background subtraction using frame differencing between adjacent frames of a video is performed. Then, the motion information of each pixel is recorded in binary indicating existence/nonexistence of motion in the frame. A pixel wise sum over all the difference images in a view gives the frequency of motion in each pixel throughout the clip.