{"id":10,"date":"2016-11-11T00:36:58","date_gmt":"2016-11-11T00:36:58","guid":{"rendered":"http:\/\/134.74.17.190\/wordpress\/?page_id=10"},"modified":"2023-04-15T12:17:59","modified_gmt":"2023-04-15T17:17:59","slug":"datecode","status":"publish","type":"page","link":"https:\/\/media-lab.ccny.cuny.edu\/?page_id=10","title":{"rendered":"Data &#038; Code"},"content":{"rendered":"<ul>\n<li><span style=\"font-size: 14pt; font-family: 'book antiqua', palatino, serif;\"><a href=\"#Dataset\">Dataset<\/a><\/span><\/li>\n<li><span style=\"font-size: 14pt; font-family: 'book antiqua', palatino, serif;\"><a href=\"#Code\">Code<\/a><\/span><\/li>\n<\/ul>\n<h3 style=\"text-align: center;\"><span style=\"font-family: 'book antiqua', palatino, serif;\"><a id=\"Dataset\"><\/a><span style=\"font-size: 18pt;\">Dataset<\/span><\/span><\/h3>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>LuNoTim-CT<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">Description:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">Our LuNoTim-CT dataset is generated based on the LIDC-IDRI dataset [1], which contains 1,020 lung CT scans with 883 of them having lung nodules. The CTs in our dataset are tampered by three different tampering methods including copy-move, classical inpainting, and deep inpainting by removing and adding nodules from\/to the original CT scans in the original LIDC-IDRI dataset.<br \/>\n<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"https:\/\/ccnymailcuny-my.sharepoint.com\/:f:\/g\/personal\/hwang005_citymail_cuny_edu\/EkuODBX1qshGiZCpv2IHEQsBZg_i639UIrR4FdYum1NIAg?e=a11O45 https:\/\/ccnymailcuny-my.sharepoint.com\/:f:\/g\/personal\/hwang005_citymail_cuny_edu\/EkuODBX1qshGiZCpv2IHEQsBZg_i639UIrR4FdYum1NIAg?e=a11O45\" data-wplink-url-error=\"true\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"https:\/\/longlong-jing.github.io\/\">Longlong Jing<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>ASL-100-RGBD Dataset<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">Description:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">A new dataset has been collected for this research in collaboration with ASL computational linguistic researchers, from native ASL signers (individuals who have been using the language since very early childhood) who performed a word list of 100 ASL signs by using a Kinect V2 camera. Participants responded affirmatively to the following screening question: Did you use ASL at home growing up, or did you attend a school as a very young child where you used ASL? Participants were provided with a slide-show presentation that asked them to perform a sequence of 100 individual ASL signs, without lowering their hands between words. Since this new dataset includes 100 signs with RGB and depth data, we refer to it as the ASL-100-RGBD dataset. There is a total of 42 videos recorded from 22 participants each performing 100 ASL signs. Each video includes 4 channels: RGB, Skeleton, Depth and HDface recorded by Kinect V2 camera. The following video is one performance among 42 videos. We merged all channels (RGB, Depth, Skeleton and HDface) of this video into one single video for a better demonstration. The upper-left window shows the RGB chennel, the upper-right frames window shows the Skeleton joints which including 25 body joints (overlaid on RGB frames for visualization purpose), the lower-left window shows the Depth channel, and the lower-right window illustrates the HDface channel which including 1,347 points. The Kinect V2 has RGB image resolution of 1920 x 1080 pixels (same for Skeleton and HDface channels) and Depth image resolution of 512 x 424. All channels (RGB, Depth, Skeleton and HDface) are resized to 480 * 270 in this example video for visualization.<br \/>\n<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/ASL_100.avi.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"https:\/\/longlong-jing.github.io\/\">Longlong Jing<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Flow Super-Resolution Dataset (ISPRS)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">Z. Petrou, Y. Xian, and Y. Tian, Towards Breaking the Spatial Resolution Barriers: An Optical Flow and Super-Resolution Approach for Sea Ice Motion Estimation, ISPRS Journal of Photogrammetry and Remote Sensing, Accepted, 2018.<br \/>\n<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/flow_sr_dataset.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/zisis\/www\/index.html\">Zisis I. Petrou<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Barcode Dataset (IEEE-CYBER 2017)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">W. Fernandez, Y. Xian, and Y. Tian, <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/Cyber17_Barcode.pdf\">Image-Based Barcode Detection and Recognition to Assist Visually Impaired Persons<\/a>, IEEE Int. Conf. on CYBER Technology in Automation, Control, and Intelligent Systems (IEEE-CYBER), 2017.<br \/>\n<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/Barcode_dataset.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxian.info\/\">Yang Xian <\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Traffice Guide Panel Text Dataset (ECCVw 2016)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Rong, C. Yi, and Y. Tian. <a href=\"http:\/\/xrong.org\/publications\/pdf\/CVRSUAD16.pdf\">Recognizing Text-based Traffic Guide Panels with Cascaded Localization Network<\/a>. ECCV Workshop on Computer Vision for Road Scene Understanding and Autonomous Driving (CVRSUAD), 2016.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/TGPT.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.engr.ccny.cuny.edu\/~xrong\/\">Xuejian Rong<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>RGB-D Staircase Detection Dataset (MMC 2016)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">R. Munoz, X. Rong, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/ICME16-W155.pdf\">Depth-aware Indoor Staircase Detection and Recognition for the Visually Impaired<\/a>. ICME Workshop on Mobile Multimedia Computing (MMC), 2016.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/Staircase.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.engr.ccny.cuny.edu\/~xrong\/\">Xuejian Rong<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong> MODIS Sea Ice Motion Estimation Dataset (TGRS 2017)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">Z. I. Petrou and Y. Tian, \u201c<a href=\"https:\/\/urldefense.proofpoint.com\/v2\/url?u=http-3A__ieeexplore.ieee.org_document_7750626_&amp;d=DgMFAw&amp;c=4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo&amp;r=ArmDmAakTgbcJrk3gW7TeH_CYIIuko_8EznEZs4HGdI&amp;m=AFZWrgOTTGBEDgPZRuD7PqXr1cIsAKs3_bSCNjWAgTU&amp;s=euvvMGtpTOUKAkNv8tZMjp1_bPWfR7RK7rhG2eVeTCo&amp;e=\" target=\"_blank\" rel=\"noopener noreferrer\" data-saferedirecturl=\"https:\/\/www.google.com\/url?hl=en&amp;q=https:\/\/urldefense.proofpoint.com\/v2\/url?u%3Dhttp-3A__ieeexplore.ieee.org_document_7750626_%26d%3DDgMFAw%26c%3D4NmamNZG3KTnUCoC6InoLJ6KV1tbVKrkZXHRwtIMGmo%26r%3DArmDmAakTgbcJrk3gW7TeH_CYIIuko_8EznEZs4HGdI%26m%3DAFZWrgOTTGBEDgPZRuD7PqXr1cIsAKs3_bSCNjWAgTU%26s%3DeuvvMGtpTOUKAkNv8tZMjp1_bPWfR7RK7rhG2eVeTCo%26e%3D&amp;source=gmail&amp;ust=1492204396742000&amp;usg=AFQjCNHknKClwguKDk3bN9s5rI2YJspwQA\">High resolution sea ice motion estimation with optical flow using satellite spectroradiometer data<\/a>,\u201d <em>IEEE Transactions on Geoscience and Remote Sensing<\/em>, vol. 55, no. 3, pp. 1339\u20131350, March 2017.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/sea_ice_flow_dataset.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/134.74.17.190\/wordpress\/zisis\/www\/index.html\">Zisis I. Petrou<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>AMSR2 Super-Resolution Dataset (TGRS 2017)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">Y. Xian, Z. I. Petrou, Y. Tian, and W. N. Meier, \u201cSuper-resolved fine scale sea ice motion tracking,\u201d <em>IEEE<\/em> <em>Transactions on Geoscience and Remote Sensing<\/em>, vol. 55, no. 8, August 2017. DOI: 10.1109\/TGRS.2017.2699081<br \/>\n<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/amsr2_sr_dataset.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxian.info\/\">Yang Xian <\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>CCNY Cell2Ear Detection Dataset (TRECVID 2014)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">Yang. Xian, X. Rong, X. Yang, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/TRECVID_2014_SED.pdf\"> CCNY at TRECVID 2014: Surveillance Event Detection<\/a>. NIST TREC Video Retrieval Evaluation (TRECVID) Workshop, 2014.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/CCD-Dataset.zip\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <\/span><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><u><a href=\"http:\/\/xrong.org\/\">Xuejian Rong<\/a><\/u><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Orient Scene Text Dataset (CVIU 2012)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">C. Yi and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/CVIU_CYI_12.PDF\">Text Extraction from Scene Images by Character Apperance and Structure Modeling<\/a>. In <em>Computer Vision and Image Understanding<\/em>, Vol. 117, No. 2, pp. 182-194, 2013.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/SNV.zip\">Version 1.1<\/a><\/span>, <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/cyi\/project_scenetextdetection.html\"><span style=\"text-decoration: underline;\">Project Page<\/span><\/a><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\"><span style=\"text-decoration: underline;\">Chucai Yi<\/span><\/a><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Spatial Hot Maps (TRECVID 2012)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Yang, C. Yi, L. Cao, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/trecvid_workshop_2012.pdf\">MediaCCNY at TRECVID 2012: Surveillance Event Detection.<\/a> NIST Workshop on TREC Video Retrieval Evaluation (TRECVID), 2012.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/HotRegions.zip\">Version 1.0<\/a><\/span> (November 5, 2012)<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>CCNY Clothing Pattern Dataset (ACM MM 2011)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Yang, S. Yuan, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/ACMM_2011.pdf\">Recognizing Clothes Patterns for Blind People by Confidence Margin based Feature Combination<\/a>. ACM Multimedia (MM), 2011.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/Clothing_Patterns.rar\">Version 1.2<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 12pt; color: #800000;\"><strong>Door Detection Dataset (ACM MM 2010)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The dataset accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">X. Yang, Y. Tian, C. Yi, and A. Arditi. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/ACMM_2010.pdf\">Context-based Indoor Object Detection as an Aid to Blind Persons Accessing Unfamiliar Environments<\/a>. ACM Multimedia (MM), 2010.<\/span><\/p>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/Door_Detection.rar\">Version 1.0<\/a><\/span><\/span><\/p>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\"><strong>Contact<\/strong>: (questions\/error reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\" target=\"_blank\" rel=\"noopener noreferrer\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n<h3 style=\"text-align: center;\"><span style=\"font-family: 'book antiqua', palatino, serif;\"><a id=\"Code\"><\/a><span style=\"font-size: 18pt;\">Code<\/span><\/span><\/h3>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Virtual-Contrast-CT (Computerized Medial Imaging and Graphics 2022)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<br \/>\n<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">J. Liu, Y. Tian, C. Duzgol, O. Akin, A. M. Agildere, K. M. Haberal, and M. Coskun. <a href=\"_wp_link_placeholder\" data-wplink-edit=\"true\">Virtual Contrast Enhancement for CT Scans of Abdomen and Pelvis<\/a>, Accepted, Computerized Medical Imaging and Graphics, 2022. PMID: 35914340 DOI: 10.1016\/j.compmedimag.2022.102094. <\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><span style=\"text-decoration: underline;\"><a href=\"https:\/\/github.com\/JingyaLiu\/Virtual-Contrast-CT\">Github Project<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"https:\/\/scholar.google.com\/citations?user=qYsFmRoAAAAJ&amp;hl=en\">Jingya Liu<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Super Sparse Coding Vector (ECCV 2014)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<br \/>\n<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Yang and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/ECCV2014.pdf\">Action Recognition Using Super Sparse Coding Vector with Spatio-Temporal Awareness<\/a>. European Conference on Computer Vision (ECCV), 2014.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/SSCV.zip\"><span style=\"text-decoration: underline;\">Version 1.3<\/span><\/a> (January 25, 2015), <span style=\"text-decoration: underline;\"><a href=\"https:\/\/github.com\/xiaodongyang\/SSCV\">Github Project<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Super Normal Vector (CVPR 2014)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Yang and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/CVPR2014.pdf\">Super Normal Vector for Activity Recognition Using Depth Sequences<\/a>. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. <\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/SNV.zip\">Version 1.1<\/a><\/span> (May 11, 2014), <span style=\"text-decoration: underline;\"><a href=\"https:\/\/github.com\/xiaodongyang\/SNV\">Github Project<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Scene Text Detection (TIP 2012)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">C. Yi and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/TIP-Yi-2012.pdf\">Text String Detection from Natural Scenes by Structure-based Partition and Grouping<\/a>. In IEEE Transactions on Image Processing, Vol. 21, No. 9, 2012. <\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/scene_text.zip\">Version 1.2<\/a><\/span> (July 25, 2013), <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/project_scenetextdetection.html\"><span style=\"text-decoration: underline;\">Project Page<\/span><\/a><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/\"><span style=\"text-decoration: underline;\">Chucai Yi<\/span><\/a><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Daily Living Activity Recognition (IJCVIP 2012)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">C. Zhang, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/NWPJ-201209-15-CameraReady.pdf\">RGB-D Camera-based Daily Living Activity Recognition.<\/a> International Journal of Computer Vision and Image Processing (IJCVIP), Vol. 2, No. 4, December 2012.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/dataReadIn.rar\">Version 1.2<\/a><\/span> (December 22, 2012), <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/set_50.rar\">Data<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/zcy\/\">Chenyang Zhang<\/a><\/span><\/span><\/p>\n<ul>\n<li><span style=\"color: #800000; font-size: 12pt; font-family: 'book antiqua', palatino, serif;\"><strong>Cascade SVMs (TRECVID 2012)<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"font-family: 'book antiqua', palatino, serif; font-size: 10pt;\">The code package accompanies the paper:<\/span><\/p>\n<p style=\"padding-left: 30px;\"><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\">X. Yang, C. Yi, L. Cao, and Y. Tian. <a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Publications\/trecvid_workshop_2012.pdf\">MediaCCNY at TRECVID 2012: Surveillance Event Detection.<\/a> NIST Workshop on TREC Video Retrieval Evaluation (TRECVID), 2012.<\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Download<\/strong>: <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/software_download_CascadeSVM.html\">Version 1.5<\/a><\/span> (November 18, 2012), <span style=\"text-decoration: underline;\"><a href=\"http:\/\/media-lab.ccny.cuny.edu\/wordpress\/Code\/project_trecvid.html\">Project Page<\/a><\/span><\/span><\/p>\n<p><span style=\"font-size: 10pt; font-family: 'book antiqua', palatino, serif;\"><strong>Contact<\/strong>: (questions\/bug reports) <span style=\"text-decoration: underline;\"><a href=\"http:\/\/yangxd.org\">Xiaodong Yang<\/a><\/span><\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Dataset Code Dataset LuNoTim-CT Description: Our LuNoTim-CT dataset is generated based on the LIDC-IDRI dataset [1], which contains 1,020 lung CT scans with 883 of them having lung nodules. The CTs in our dataset are tampered by three different tampering methods including copy-move, classical inpainting, and deep inpainting by removing and adding nodules from\/to the [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/pages\/10"}],"collection":[{"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=10"}],"version-history":[{"count":39,"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/pages\/10\/revisions"}],"predecessor-version":[{"id":632,"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=\/wp\/v2\/pages\/10\/revisions\/632"}],"wp:attachment":[{"href":"https:\/\/media-lab.ccny.cuny.edu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=10"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}