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In nowadays, as the development of digital photographic technology, video files grow rapidly, there is a great demand for automatic video semantic analysis in many scenes, such as video semantic understanding, content-based analysis, video retrieval. Shot boundary detection is a key basic technology and first step for video analysis. However, recent methods are time consuming and performs bad in the...
Feature ranking from video-wide temporal evolution brings reliable information for complex action recognition. However, a video may contain similar features in the sequence of frames which deliver unnecessary information to the ranking function. This paper proposes a method to improve the rank-pooling strategy which captures the optimized latent structure of the video sequence data. The optimization...
Video scene detection, the task of temporally dividing a video into its semantic sections, is an important process for effective analysis of heterogeneous video content. With the increased amount of video available for consumption, video scene detection becomes more and more important by providing means for effective video summarization, search and retrieval, browsing, and video understanding. We...
Online Social Networks (OSNs) are simple, unweighted graphs used to store information in the context of social media and emails. Accurately representing the connectivity and features of these graphs is important in applications of graph utility and differential privacy. Current methods of describing these network graphs use graph metrics such as the shortest-path betweenness centrality, clustering...
Together with the technology advancement, Computer Vision plays an important role in enhancing smart computing systems to help people overcome obstacles in their daily lives. One of the common troublesome problems is human memorization ability, especially memorizing things such as personal items. It is annoying for people to waste their time finding lost items manually by recall or notes. This motivates...
Learning similarity functions between image pairs with deep neural networks yields highly correlated activations of large embeddings. In this work, we show how to improve the robustness of embeddings by exploiting independence in ensembles. We divide the last embedding layer of a deep network into an embedding ensemble and formulate training this ensemble as an online gradient boosting problem. Each...
The problem of image enhancement for low-contrast images is considered. The histogram-based method of automatic contrast enhancement on the basis of the analyzing of contrast distribution at the boundaries of low-contrast image elements (objects and background) using the various definitions of contrast kernels is proposed. The research of the effectiveness of the proposed and the well-known histogram-based...
In modern remote sensing procedures, one of the most important issues is to distinguish specific types of land coverage. Discrimination between different land coverages especially in metropolitan surveying is so important that the in front civilization projects are basically dependent to them. In this paper, an innovative image processing strategy is employed for distinguishing green lands from other...
The widespread penetration of counterfeit integrated circuits (ICs) is not only a major threat to the electronic goods supply chain, but also constitute a great threat to national security. Image processing based counterfeit IC design techniques are promising, but currently often suffer from high computational complexity and requirement of expensive image acquisition infrastructure. We describe two...
We propose a direct monocular SLAM algorithm based on the Normalised Information Distance (NID) metric. In contrast to current state-of-the-art direct methods based on photometric error minimisation, our information-theoretic NID metric provides robustness to appearance variation due to lighting, weather and structural changes in the scene. We demonstrate successful localisation and mapping across...
This paper describes a joint intensity metric learning method to improve the robustness of gait recognition with silhouette-based descriptors such as gait energy images. Because existing methods often use the difference of image intensities between a matching pair (e.g., the absolute difference of gait energies for the l1-norm) to measure a dissimilarity, large intrasubject differences derived from...
the task of full-focused digital images construction relates to computational photography and is a process of increasing the information capacity of images obtained via photo- and video-fixation devices with limited optical depth of field. Additional to this task is the question of automatic evaluation of the quality of the images. The most popular non-reference metrics for comparing the quality of...
The mobile video playback involves many subsystems of the devices such as computing, rendering and displaying subsystems. Among all subsystems, the displaying subsystem accounts for at least 38% of all consumed power, and it can be up to 68% with the maximum backlight brightness. What is more, lots of people watch videos via mobile devices in various situations, where the ambient luminance condition...
Person re-identification (re-id) aims to match a specific person across non-overlapping views of different cameras, which is currently one of the hot topics in computer vision. Compared with image-based person re-id, video-based techniques could achieve better performance by fully utilizing the space-time information. This paper presents a novel video-based person re-id method named Deep Feature Guided...
We propose a novel method for developing static storyboard for video clips included with biomedical research literature. The technique uses both visual and audio content in the video to select candidate key frames for the storyboard. From the visual channel, the Intra-frames are extracted using FFmpeg tool. IBM Watson speech-to-text service is used to extract words from the audio channel, from which...
The outsourcing of elaboration of data streams requires that a service provider collects and stores data on behalf of a company that does not have enough resources to sustain the efforts related to the management of such data streams. If a company does not trust the service provider, then it has to check the validity of the answers when querying the data store, since the results may be not reliable...
Location data collection at a societal scale is increasingly becoming common - examples of this are call and data detail records in telecommunication companies, GPS samples collected by car companies, and GPS samples from mobile devices in mapping companies (e.g., Google, Microsoft). Such large scale mobility datasets have applications in urban planning, network planning, surveillance, and real-time...
Person re-identification is an important task of matching pedestrians across non-overlapping camera views. In this paper, we exploit a weighted feature descriptor for person re-identification. We firstly compute the weights on the superpixel level via graph-based manifold ranking algorithm, then integrate the computed weights into a patch-based feature descriptor, named local maximal occurrence. Finally,...
The problem of image enhancement for low-contrast images with the small-size objects is considered. The histogram-based method for contrast enhancement of low-contrast images with the small-size objects on the basis of the estimation of parameters of contrast distribution at boundaries of image elements for the various definitions of contrast kernels is proposed. The proposed method is intended for...
dynamic cloud workloads necessitate forecasting methodologies for accurate resource provisioning affecting both cloud providers and clients. This paper focuses on forecasting in the cloud in order to understand its underlying workload dynamics. It analyzes recent workload traces and discovers characteristics that are not adequately captured by traditional linear & nonlinear models employed for...
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