Automatic, full body motion generation for humanoid robots presents a formidable computational challenge. The kicking motion is one of the most important motions in a soccer game. However, at the current state the most common approaches of implementing this motion are based on key frame technique. Such solutions are inflexible and cost a lot of time to adjust robot’s position. In this paper we ...

This paper focus on the different kinds of symmetric key encryption techniques those exist in present world for securing data communication. It also frames all the techniques related to variety of encryption like image encryption, information encryption, double encryption. It aims to explain the performance parameters that are used in encryption processes and analyzing on their security issues....

In this paper, we present an efficient content-based video copy detection method based on vocabulary tree and inverted files. The copy detection system exploits complementary local features and video sequence matching. Using two different local features, vocabulary trees and inverted files are built respectively to get keyframes matching result. Histogram-based and diagonal-based sequence match...

We describe a method to control a motion of particles during physically based simulation. Specifically we design force fields which drive particles so as to shape a human figure. We define attraction, steering and repulsion force fields to achieve a target shape and avoid obstacles. We use key frames of a skeleton of a human figure as a target shape and distribute attraction points over the tar...

In this paper a video coding scheme with Layered Block Matching Super-resolution (LBM-SR) is presented. At the encoder side, it divides the video frames into key and non-key frames, which are encoded at original resolution and reduced resolution respectively. During the resolution reduction process, most of the high frequency information in non-key frames is dropped to save the bit-rate. At the...

We build a fast video shot retrieval system in the context of the NIST TREC Video 2009 evaluation campaign. We compare our efficient Profile Entropy Features (PEF) to usual features, using various classifiers. These PEF are derived using the projection in the horizontal and vertical orientations. These features are then fed to SVM or KNNG classifiers to produce the keyframe ranks, from which we...

This paper presents a novel approach for extracting discriminative descriptions of 3-D objects using spatio-temporal information. In particular, local features are tracked in image sequences leading to local trajectories containing dynamic information. These trajectories are judged with respect to their quality and robustness and finally each of them is assigned a single local descriptor from a...

In MediaEval 2016, we focus on the image interestingness subtask which involves predicting interesting key frames of a video in the form of a movie trailer. We specifically propose three different deep models for this subtask. The first two models are based on fine-tuning two pretrained models, namely AlexNet and MemNet, where we cast the interestingness prediction as a regression problem. Our ...

This paper addresses the problem of how to learn an appropriate representation from video to benefit video-based face recognition. We pose it as learning spatio-temporal embedding (STE) from raw video. STE of a video sequence is defined as its condensed version capturing the essence of space-time characteristics of the video. Relying on co-occurrence statistics of training videos, Bayesian keyf...

To provide users with an overview of medical video content at various levels of abstraction which can be used for more efficient database browsing and access, a hierarchical video summarization strategy has been developed and is presented in this paper. To generate an overview, the key frames of a video are preprocessed to extract special frames (black frames, slides, clip art, sketch drawings)...