VisioForge Video Edit SDK major features include: Input formats video formats - AVI, MPEG-1/2, WMV, 3GP, QuickTime MOV, MPEG-4/H264 (AVC), any other if you have corresponding decoder audio formats - WAV, MP3, OGG, WMA, AAC, any other if you have corresponding decoder images - BMP, PNG, GIF, JPEG, TIFF Add a lot of audio/video files and images Adding various parts of video and audio files to the timeline Video processing and effects image and graphic overlays text overlay video transparency brightness, contrast, saturation, hue, etc. deinterlace denoise pan / zoom resize to any resolution chroma-key 3-rd party DirectShow filters support Transition effects between tracks (above 80, same as in Windows Movie Maker) Motion detection Video encryption Encoding parameters video size frame rate video and audio codecs video and audio bit rate other Audio processing and effects volume booster equalizer 3D-bass system other effects Output video formats: AVI WMV (using built-in or external profiles or specifying all parameters directly) MKV (Matroska) WebM MPEG 1/2 (VCD/SVCD/DVD), MPEG-4 (iPod/iPhone), FLV, using FFMPEG any other formats via third-party filters (e.g. MPEG1, MPEG2, MPEG4/H264, 3GP) MP4 H264 / AAC Output audio formats WAV (PCM or ACM codecs) OGG Vorbis MP3 (LAME) Windows Media Audio Development platforms: Visual Studio 2005 and later: Visual C#, Visual C++, Visual Basic .NET Delphi 6 / 7 / 2005 / 2006 / 2007 / 2009 / 2010 / XE / XE2 / XE3 / XE4 / XE5 / XE6 / XE7 / XE8 / 10 / 10.1 / 10.2 Visual Studio 6: Visual C++, Visual Basic 6 Borland C++ Builder 5 and later may be used with other ActiveX compatible applications like Microsoft Access, Word, Excel, FrontPage, Powerbuilder, etc. x86 and x64 versions System requirements Windows 10, Windows 8/8.1, Windows 7, Windows Vista, Windows XP, Windows Server 2003 and later .Net Framework 2.0 or later (for some demo applications) DirectX 9 or later Distribution rights Royalty-free distribution. Trial limitations Trial version overlay a nag-screen over the video window.
2022-11-17 09:10:46 34.04MB VisioForge
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RNN和Temporal-ConvNet进行活动识别 ,(等额缴纳) 论文代码: (在杂志上接受,2019年) 项目: 抽象的 在这项工作中,我们使用ResNet-101演示了一个强大的基线两流ConvNet。 我们使用此基线来彻底检查RNN和Temporal-ConvNets的使用,以提取时空信息。 基于我们的实验结果,然后我们提出并研究了两个不同的网络,以进一步整合时空信息:1)时域RNN和2)初始样式的Temporal-ConvNet。 我们的分析确定了每种方法的特定局限性,这些局限性可能构成未来工作的基础。 我们在UCF101和HMDB51数据集上的实验结果分别达到了94.1%和69.0%的最新性能,而无需大量的时间增强。 我们如何解决活动识别问题? 演示版 GIF展示了我们的TS-LSTM和“时间-开始”方法的前3个预测结果。 顶部的文本是基本事实,三个文本是每种方法的预
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用于Unity3D中视频播放,不带水印,支持windows、ios、android、webgl等等平台,支持4k视频播放,并且非常节省性能 官网地址:http://renderheads.com/products/avpro-video/
2022-11-11 13:34:55 180.8MB AVProVideo unity3d 视频处理
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unity,Avpro Video
2022-11-11 13:28:41 36.21MB unity插件
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Video Download Helper高级(无120分钟时间限制)
2022-11-11 13:25:19 38.85MB VideoDownloadH
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视频稳定 使用opencv对实时视频进行视频稳定
2022-11-10 17:28:23 2KB C++
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VideoCompressor 适用于Android的高性能视频压缩器,使用硬件解码和编码API(MediaCodec)。 演示版 用法 调用compressVideoLow,compressVideoMedium和compressVideoHigh,表示3种压缩质量。 VideoCompressTask task = VideoCompress.compressVideoLow(tv_input.getText().toString(), destPath, new VideoCompress.CompressListener() { @Override public void onStart() { //Start Compress }
2022-11-10 00:08:33 1.28MB video compressor mediacodec 附件源码
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用卷积滤波器matlab代码可再现的视频降噪技术 流行和可复制的视频降噪作品集。 准则:作品必须有可用的代码,并且可再现的结果证明了视频降噪的前景可观或最先进的表现。 集合的这种格式类似于 请随时为这个仓库做贡献。 视频降噪算法 在线方式 ReLD 通过在线稀疏和低秩矩阵分解实现视频降噪(SSP 2016),Guo和Vaswani。 维多萨VIDOSAT-在线视频恢复的高维稀疏变换学习(TIP 2019),Wen等。 非本地方法 VBM3D 通过稀疏3D变换域协作过滤进行视频降噪(EUSIPCO 2007),Dabov等。 VBM4D 通过可分离的4-D非局部时空变换进行视频降噪,解块和增强(TIP 2012),Maggioni等。 RNLF Sutour等人,NL均值的自适应正则化:图像和视频去噪的应用(TIP 2014)。 盐Wen的联合自适应稀疏和低秩动态:用于视频降噪的在线张量重构方案(ICCV 2017),Wen等。 贝叶斯方法 越南国家广播电视台通过时空补丁的经验贝叶斯估计对视频进行降噪(JMIV 2017),Arias和Morel 深度学习 虚拟网络CNN的非本地视频降噪
2022-11-08 15:55:31 2KB 系统开源
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AnyMP4 Video Converter UltimateDVD转换器6.1.28.32992
2022-11-05 17:25:27 114.94MB AnyMP4 Video
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Columbia Consumer Video (CCV) Database --- A Benchmark for Consumer Video Analysis 我原本是用youtube-dl来直接在youtube下载的,结果youtube限制访问量,最后又另求别人要来的资源。资源内含下载链接和下载方法。数据集压缩包共30G Recognizing visual content in unconstrained videos has become a very important problem for many applications. Existing corpora for video analysis lack scale and/or content diversity, and thus limited the needed progress in this critical area. To stimulate innovative research on this challenging issue, we constructed a new database called CCV, containing 9,317 YouTube videos over 20 semantic categories. The database was collected with extra care to ensure relevance to consumer's interest and originality of video content without post-editing. Such videos typically have very little textual annotation and thus can benefit from the development of automatic content analysis techniques.
2022-11-03 22:25:21 178B video classifica
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