Underwater Video Survey: Planning and Data Processing

The importance of underwater video surveys as an exploration tool has been steadily increasing over recent years [1]. Better photographic equipment, more effective sources of illumination, and improved processing techniques - all make video surveying a reliable tool for seafloor habitat mapping, sediment boundary delineation and groundtruthing, mapping and documentation of forensic and archaeological sites. There is a change in attitude towards video surveying that affects the way the data is collected, and hence its quality. Earlier video data processing algorithms had to cope with whatever was recorded (often simultaneously with acquisition of other data, considered to be more important). Now we have a chance to plan ahead and organize a survey in a way most suitable for the processing. The goal of this paper is to review available processing techniques and to discuss preferable survey patterns, associated errors and processing stability

Lucid Data Dreaming for Video Object Segmentation

Convolutional networks reach top quality in pixel-level video object segmentation but require a large amount of training data (1k~100k) to deliver such results. We propose a new training strategy which achieves state-of-the-art results across three evaluation datasets while using 20x~1000x less annotated data than competing methods. Our approach is suitable for both single and multiple object segmentation. Instead of using large training sets hoping to generalize across domains, we generate in-domain training data using the provided annotation on the first frame of each video to synthesize ("lucid dream") plausible future video frames. In-domain per-video training data allows us to train high quality appearance- and motion-based models, as well as tune the post-processing stage. This approach allows to reach competitive results even when training from only a single annotated frame, without ImageNet pre-training. Our results indicate that using a larger training set is not automatically better, and that for the video object segmentation task a smaller training set that is closer to the target domain is more effective. This changes the mindset regarding how many training samples and general "objectness" knowledge are required for the video object segmentation task.Comment: Accepted in International Journal of Computer Vision (IJCV

Content-based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events

As amounts of publicly available video data grow the need to query this data efficiently becomes significant. Consequently content-based retrieval of video data turns out to be a challenging and important problem. We address the specific aspect of inferring semantics automatically from raw video data. In particular, we introduce a new video data model that supports the integrated use of two different approaches for mapping low-level features to high-level concepts. Firstly, the model is extended with a rule-based approach that supports spatio-temporal formalization of high-level concepts, and then with a stochastic approach. Furthermore, results on real tennis video data are presented, demonstrating the validity of both approaches, as well us advantages of their integrated us

VIDEO STREAMING WITH GIGABIT PASSIVE OPTICAL NETWORK TECHNOLOGY

Perkembangan teknologi berpengaruh kepada kebutuhan user untuk mengakses data lebih cepat, perlahan sistem dengan menggunakan Fiber Optic mulai menggeser posisi kabel tembaga dalam transmisi data karena lebih cepat. Tren user saat ini adalah membutuhkan transmisi data yang lebih cepat dan lebih besar, seperti Video Streaming. Penelitian ini bertujuan untuk membuat system Video Streaming dengan infrastruktur GPON (Gigabit Passive Optical Network). Dalam penelitian dilakukan konfigurasi Video Streaming dengan GPON kemudian juga dilakukan dengan system Ethernet. Keduanya dibandingkan dengan parameter kecepatan dan frame rate untuk Video Streaming. Hasil yang dapat diperoleh dari penelitian dengan menggunakan system GPON adalah kestabilan untuk melakukan Streaming Video dibandingkan Ethernet

Implementation and evaluation of simultaneous video-electroencephalography and functional magnetic resonance imaging

The objective of this study was to demonstrate that the addition of simultaneous and synchronised video to electroencephalography (EEG)-correlated functional magnetic resonance imaging (fMRI) could increase recorded information without data quality reduction. We investigated the effect of placing EEG, video equipment and their required power supplies inside the scanner room, on EEG, video and MRI data quality, and evaluated video-EEG-fMRI by modelling a hand motor task. Gradient-echo, echo-planner images (EPI) were acquired on a 3-T MRI scanner at variable camera positions in a test object [with and without radiofrequency (RF) excitation], and human subjects. EEG was recorded using a commercial MR-compatible 64-channel cap and amplifiers. Video recording was performed using a two-camera custom-made system with EEG synchronization. An in-house script was used to calculate signal to fluctuation noise ratio (SFNR) from EPI in test object with variable camera positions and in human subjects with and without concurrent video recording. Five subjects were investigated with video-EEG-fMRI while performing hand motor task. The fMRI time series data was analysed using statistical parametric mapping, by building block design general linear models which were paradigm prescribed and video based. Introduction of the cameras did not alter the SFNR significantly, nor did it show any signs of spike noise during RF off conditions. Video and EEG quality also did not show any significant artefact. The Statistical Parametric Mapping{T} maps from video based design revealed additional blood oxygen level-dependent responses in the expected locations for non-compliant subjects compared to the paradigm prescribed design. We conclude that video-EEG-fMRI set up can be implemented without affecting the data quality significantly and may provide valuable information on behaviour to enhance the analysis of fMRI data

Real-time data compression of broadcast video signals

A non-adaptive predictor, a nonuniform quantizer, and a multi-level Huffman coder are incorporated into a differential pulse code modulation system for coding and decoding broadcast video signals in real time