In many applications, in industry and laboratory, there is a robot with a human, in close motion, to satisfy the work requirement. Therefore, for safety and monitoring the quality, the classification between them is important. The robot is considered as a semi-rigid object because it has many parts in the half upper, while the lower half is rigid, and a human is a non-rigid object. This paper presents a practical result which investigates the classification between these two objects based on the micro-Doppler signatures. The object's data were acquired using an S-band 2.4 GHz radar. The improved Stockwell transform was used to analyze the radar received signal in the timefrequency domain and feature extraction. The singular value decomposition is used for data filtering based on the Hankel matrix (HSVD) while using the Temporal Radial Basis Function to satisfy the classification. The classification accuracies gained are; 96.4%-100% for semi-rigid, 92%-95% for non-rigid closed objects.
Video summarization used for a different application like video ob ject recognition and classification. In video processing, numerous frames containing similar information, this leads to time Keywords: Key frame, Video summarization, WaveletCopyright © 2018 Universitas Ahmad Dahlan. All rights reserved. IntroductionThe videos of the dynamic signs consist of large number of frames are not essential in order to determine the meaning of the performed sign, rather, only a few important frames from the video are sufficient. These most important and thus distinguishing frames are known as key frames [1]. Through the key frame sequence detection and identification, the sign language can be rapidly recognized. At the same time, sign language is a way for deaf people to communicate and exchange ideas through finger alphabet and gestures instead of language [2]. Extraction of key frames from the video and to analyze only these frames instead of all the frames present in the video can greatly improve the performance of the system. Analysis of these key frames can help in forming the annotations for the video.Different techniques for key frame extraction were reported, a key frame extraction method for video copyright protection was presented in [3]. In this technique, a two-stage method is used to extract accurate key frames to cover the content for the whole video sequence. key frame extraction method based on unsupervised clustering and mutual comparison were developed in [4]. A method of key frame extraction using thresholding of absolute difference of histogram of consecutive frames of video data is proposed [5]. The brief representation and comparison of effective key frame extraction methods like cluster-base analysis, generalized Gaussian density method(GGD), General-Purpose Graphical Processing Unit (GPGPU), Histogram difference was presented in [6]. A square histogram based model using frame segmentation and automatic threshold calculation was developed in [7]. Key frame extraction method using wavelet statistics was proposed in [8] and the edge change ratio algorithm for detecting shots of the video and key frames are extracted from these shots [9].Video summarization has been proposed to improve faster browsing of large video collections and more efficient content indexing and access. As the name implies, video summarization is a mechanism to produce a short summary of a video to give the user a synthetic and useful visual abstract of a video sequence, it can either be key frames or video skims [10]. By using the key-frame it is able to express the main content of video data clearly and reduce the amount of memory needed for video data processing and complexity greatly.so that could make the storage organization, retrieval and recognition of video information more convenient and efficient, thus key frame extraction is an efficient method for video summarization [5]. In this study, an improved method for key frame detection using discrete wavelet transform (DWT) with modified threshold factor is proposed...
<span lang="EN-US">Video summarization used in many applications such as video object recognition and classification. Different methods used for this purpose: key frame extraction using edge detection and key frame extraction using discrete wavelet transform (DWT). In video processing, numerous frames containing similar information, this leads to time consumption and slow processing speed and complexity. New method Hybrid DWT-Edge Key Frame Detection (HDEKFD) of dynamic sign language presented in this paper. This method combined two algorithms edge detection and DWT to increase processing speed by reduce the numbers of redundant frames. Simulation results illustrate the efficient of the proposed algorithm compare to other methods. </span>
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