Search Results for author: B. S. Manjunath

Found 44 papers, 18 papers with code

HAPSSA: Holistic Approach to PDF Malware Detection Using Signal and Statistical Analysis

no code implementations8 Nov 2021 Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath

Malicious PDF documents present a serious threat to various security organizations that require modern threat intelligence platforms to effectively analyze and characterize the identity and behavior of PDF malware.

Malware Detection

OMD: Orthogonal Malware Detection Using Audio, Image, and Static Features

no code implementations8 Nov 2021 Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Tejaswi Nanjundaswamy, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath

In this paper, we propose a novel and orthogonal malware detection (OMD) approach to identify malware using a combination of audio descriptors, image similarity descriptors and other static/statistical features.

Malware Detection

A computational geometry approach for modeling neuronal fiber pathways

1 code implementation2 Aug 2021 S. Shailja, Angela Zhang, B. S. Manjunath

We develop a computational geometry-based tractography representation that aims to simplify the connectivity of white matter fibers.

GTNet:Guided Transformer Network for Detecting Human-Object Interactions

1 code implementation2 Aug 2021 A S M Iftekhar, Satish Kumar, R. Austin McEver, Suya You, B. S. Manjunath

For detecting HOI, it is important to utilize relative spatial configurations and object semantics to find salient spatial regions of images that highlight the interactions between human object pairs.

Human-Object Interaction Detection

Holistic Image Manipulation Detection using Pixel Co-occurrence Matrices

no code implementations12 Apr 2021 Lakshmanan Nataraj, Michael Goebel, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath

While most detection methods in literature focus on detecting a particular type of manipulation, it is challenging to identify doctored images that involve a host of manipulations.

Image Forensics Image Manipulation +1

Adversarially Optimized Mixup for Robust Classification

no code implementations22 Mar 2021 Jason Bunk, Srinjoy Chattopadhyay, B. S. Manjunath, Shivkumar Chandrasekaran

Mixup is a procedure for data augmentation that trains networks to make smoothly interpolated predictions between datapoints.

Classification Data Augmentation +2

Attribution of Gradient Based Adversarial Attacks for Reverse Engineering of Deceptions

1 code implementation19 Mar 2021 Michael Goebel, Jason Bunk, Srinjoy Chattopadhyay, Lakshmanan Nataraj, Shivkumar Chandrasekaran, B. S. Manjunath

Machine Learning (ML) algorithms are susceptible to adversarial attacks and deception both during training and deployment.

Malware Detection Using Frequency Domain-Based Image Visualization and Deep Learning

1 code implementation26 Jan 2021 Tajuddin Manhar Mohammed, Lakshmanan Nataraj, Satish Chikkagoudar, Shivkumar Chandrasekaran, B. S. Manjunath

Motivated by the visual similarity of these images for different malware families, we compare our deep neural network models with standard image features like GIST descriptors to evaluate the performance.

Classification General Classification +3

StressNet: Detecting Stress in Thermal Videos

1 code implementation18 Nov 2020 Satish Kumar, A S M Iftekhar, Michael Goebel, Tom Bullock, Mary H. MacLean, Michael B. Miller, Tyler Santander, Barry Giesbrecht, Scott T. Grafton, B. S. Manjunath

Precise measurement of physiological signals is critical for the effective monitoring of human vital signs.

Semi supervised segmentation and graph-based tracking of 3D nuclei in time-lapse microscopy

1 code implementation26 Oct 2020 S. Shailja, Jiaxiang Jiang, B. S. Manjunath

We propose a novel weakly supervised method to improve the boundary of the 3D segmented nuclei utilizing an over-segmented image.

Exploiting Context for Robustness to Label Noise in Active Learning

no code implementations18 Oct 2020 Sudipta Paul, Shivkumar Chandrasekaran, B. S. Manjunath, Amit K. Roy-Chowdhury

Several works in computer vision have demonstrated the effectiveness of active learning for adapting the recognition model when new unlabeled data becomes available.

Active Learning Document Classification +2

Detection, Attribution and Localization of GAN Generated Images

no code implementations20 Jul 2020 Michael Goebel, Lakshmanan Nataraj, Tejaswi Nanjundaswamy, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, B. S. Manjunath

Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers.

PCAMs: Weakly Supervised Semantic Segmentation Using Point Supervision

no code implementations10 Jul 2020 R. Austin McEver, B. S. Manjunath

Current state of the art methods for generating semantic segmentation rely heavily on a large set of images that have each pixel labeled with a class of interest label or background.

Weakly-Supervised Semantic Segmentation

VSGNet: Spatial Attention Network for Detecting Human Object Interactions Using Graph Convolutions

1 code implementation CVPR 2020 Oytun Ulutan, A. S. M. Iftekhar, B. S. Manjunath

Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually.

Human-Object Interaction Detection

Predicting Clinical Outcome of Stroke Patients with Tractographic Feature

1 code implementation22 Jul 2019 Po-Yu Kao, Jefferson W. Chen, B. S. Manjunath

However, the presence of stroke lesion may cause neural disruptions to other brain regions, and these potentially damaged regions may affect the clinical outcome of stroke patients.

Ischemic Stroke Lesion Segmentation Lesion Segmentation

Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior

no code implementations29 Jun 2019 Po-Yu Kao, Jefferson W. Chen, B. S. Manjunath

We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation.

Brain Tumor Segmentation Tumor Segmentation

Predicting Fluid Intelligence of Children using T1-weighted MR Images and a StackNet

1 code implementation16 Apr 2019 Po-Yu Kao, Angela Zhang, Michael Goebel, Jefferson W. Chen, B. S. Manjunath

In this work, we utilize T1-weighted MR images and StackNet to predict fluid intelligence in adolescents.

Denoising

Detecting GAN generated Fake Images using Co-occurrence Matrices

no code implementations15 Mar 2019 Lakshmanan Nataraj, Tajuddin Manhar Mohammed, Shivkumar Chandrasekaran, Arjuna Flenner, Jawadul H. Bappy, Amit K. Roy-Chowdhury, B. S. Manjunath

The advent of Generative Adversarial Networks (GANs) has brought about completely novel ways of transforming and manipulating pixels in digital images.

Hybrid LSTM and Encoder-Decoder Architecture for Detection of Image Forgeries

1 code implementation6 Mar 2019 Jawadul H. Bappy, Cody Simons, Lakshmanan Nataraj, B. S. Manjunath, Amit K. Roy-Chowdhury

This paper proposes a high-confidence manipulation localization architecture which utilizes resampling features, Long-Short Term Memory (LSTM) cells, and encoder-decoder network to segment out manipulated regions from non-manipulated ones.

Accurate 3D Cell Segmentation using Deep Feature and CRF Refinement

1 code implementation13 Feb 2019 Jiaxiang Jiang, Po-Yu Kao, Samuel A. Belteton, Daniel B. Szymanski, B. S. Manjunath

We consider the problem of accurately identifying cell boundaries and labeling individual cells in confocal microscopy images, specifically, 3D image stacks of cells with tagged cell membranes.

Cell Segmentation

Deep Learning Methods for Event Verification and Image Repurposing Detection

no code implementations11 Feb 2019 M. Goebel, A. Flenner, L. Nataraj, B. S. Manjunath

The first method uses the features from the last convolutional layer of a pre-trained network as input to a classifier.

General Classification

Automated Segmentation of CT Scans for Normal Pressure Hydrocephalus

1 code implementation25 Jan 2019 Angela Zhang, Po-Yu Kao, Ronald Sahyouni, Ashutosh Shelat, Jefferson Chen, B. S. Manjunath

The Evan's ratio, an approximation of the ratio of ventricle to brain volume using only one 2D slice of the scan, has been proposed but is not robust.

Computed Tomography (CT) General Classification

Actor Conditioned Attention Maps for Video Action Detection

2 code implementations30 Dec 2018 Oytun Ulutan, Swati Rallapalli, Mudhakar Srivatsa, Carlos Torres, B. S. Manjunath

While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context.

Action Detection

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

1 code implementation5 Nov 2018 Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Rozycki, Marcel Prastawa, Esther Alberts, Jana Lipkova, John Freymann, Justin Kirby, Michel Bilello, Hassan Fathallah-Shaykh, Roland Wiest, Jan Kirschke, Benedikt Wiestler, Rivka Colen, Aikaterini Kotrotsou, Pamela Lamontagne, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Marc-Andre Weber, Abhishek Mahajan, Ujjwal Baid, Elizabeth Gerstner, Dongjin Kwon, Gagan Acharya, Manu Agarwal, Mahbubul Alam, Alberto Albiol, Antonio Albiol, Francisco J. Albiol, Varghese Alex, Nigel Allinson, Pedro H. A. Amorim, Abhijit Amrutkar, Ganesh Anand, Simon Andermatt, Tal Arbel, Pablo Arbelaez, Aaron Avery, Muneeza Azmat, Pranjal B., W Bai, Subhashis Banerjee, Bill Barth, Thomas Batchelder, Kayhan Batmanghelich, Enzo Battistella, Andrew Beers, Mikhail Belyaev, Martin Bendszus, Eze Benson, Jose Bernal, Halandur Nagaraja Bharath, George Biros, Sotirios Bisdas, James Brown, Mariano Cabezas, Shilei Cao, Jorge M. Cardoso, Eric N Carver, Adrià Casamitjana, Laura Silvana Castillo, Marcel Catà, Philippe Cattin, Albert Cerigues, Vinicius S. Chagas, Siddhartha Chandra, Yi-Ju Chang, Shiyu Chang, Ken Chang, Joseph Chazalon, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Chen, Kun Cheng, Ahana Roy Choudhury, Roger Chylla, Albert Clérigues, Steven Colleman, Ramiro German Rodriguez Colmeiro, Marc Combalia, Anthony Costa, Xiaomeng Cui, Zhenzhen Dai, Lutao Dai, Laura Alexandra Daza, Eric Deutsch, Changxing Ding, Chao Dong, Shidu Dong, Wojciech Dudzik, Zach Eaton-Rosen, Gary Egan, Guilherme Escudero, Théo Estienne, Richard Everson, Jonathan Fabrizio, Yong Fan, Longwei Fang, Xue Feng, Enzo Ferrante, Lucas Fidon, Martin Fischer, Andrew P. French, Naomi Fridman, Huan Fu, David Fuentes, Yaozong Gao, Evan Gates, David Gering, Amir Gholami, Willi Gierke, Ben Glocker, Mingming Gong, Sandra González-Villá, T. Grosges, Yuanfang Guan, Sheng Guo, Sudeep Gupta, Woo-Sup Han, Il Song Han, Konstantin Harmuth, Huiguang He, Aura Hernández-Sabaté, Evelyn Herrmann, Naveen Himthani, Winston Hsu, Cheyu Hsu, Xiaojun Hu, Xiaobin Hu, Yan Hu, Yifan Hu, Rui Hua, Teng-Yi Huang, Weilin Huang, Sabine Van Huffel, Quan Huo, Vivek HV, Khan M. Iftekharuddin, Fabian Isensee, Mobarakol Islam, Aaron S. Jackson, Sachin R. Jambawalikar, Andrew Jesson, Weijian Jian, Peter Jin, V Jeya Maria Jose, Alain Jungo, B Kainz, Konstantinos Kamnitsas, Po-Yu Kao, Ayush Karnawat, Thomas Kellermeier, Adel Kermi, Kurt Keutzer, Mohamed Tarek Khadir, Mahendra Khened, Philipp Kickingereder, Geena Kim, Nik King, Haley Knapp, Urspeter Knecht, Lisa Kohli, Deren Kong, Xiangmao Kong, Simon Koppers, Avinash Kori, Ganapathy Krishnamurthi, Egor Krivov, Piyush Kumar, Kaisar Kushibar, Dmitrii Lachinov, Tryphon Lambrou, Joon Lee, Chengen Lee, Yuehchou Lee, M Lee, Szidonia Lefkovits, Laszlo Lefkovits, James Levitt, Tengfei Li, Hongwei Li, Hongyang Li, Xiaochuan Li, Yuexiang Li, Heng Li, Zhenye Li, Xiaoyu Li, Zeju Li, Xiaogang Li, Wenqi Li, Zheng-Shen Lin, Fengming Lin, Pietro Lio, Chang Liu, Boqiang Liu, Xiang Liu, Mingyuan Liu, Ju Liu, Luyan Liu, Xavier Llado, Marc Moreno Lopez, Pablo Ribalta Lorenzo, Zhentai Lu, Lin Luo, Zhigang Luo, Jun Ma, Kai Ma, Thomas Mackie, Anant Madabushi, Issam Mahmoudi, Klaus H. Maier-Hein, Pradipta Maji, CP Mammen, Andreas Mang, B. S. Manjunath, Michal Marcinkiewicz, S McDonagh, Stephen McKenna, Richard McKinley, Miriam Mehl, Sachin Mehta, Raghav Mehta, Raphael Meier, Christoph Meinel, Dorit Merhof, Craig Meyer, Robert Miller, Sushmita Mitra, Aliasgar Moiyadi, David Molina-Garcia, Miguel A. B. Monteiro, Grzegorz Mrukwa, Andriy Myronenko, Jakub Nalepa, Thuyen Ngo, Dong Nie, Holly Ning, Chen Niu, Nicholas K Nuechterlein, Eric Oermann, Arlindo Oliveira, Diego D. C. Oliveira, Arnau Oliver, Alexander F. I. Osman, Yu-Nian Ou, Sebastien Ourselin, Nikos Paragios, Moo Sung Park, Brad Paschke, J. Gregory Pauloski, Kamlesh Pawar, Nick Pawlowski, Linmin Pei, Suting Peng, Silvio M. Pereira, Julian Perez-Beteta, Victor M. Perez-Garcia, Simon Pezold, Bao Pham, Ashish Phophalia, Gemma Piella, G. N. Pillai, Marie Piraud, Maxim Pisov, Anmol Popli, Michael P. Pound, Reza Pourreza, Prateek Prasanna, Vesna Prkovska, Tony P. Pridmore, Santi Puch, Élodie Puybareau, Buyue Qian, Xu Qiao, Martin Rajchl, Swapnil Rane, Michael Rebsamen, Hongliang Ren, Xuhua Ren, Karthik Revanuru, Mina Rezaei, Oliver Rippel, Luis Carlos Rivera, Charlotte Robert, Bruce Rosen, Daniel Rueckert, Mohammed Safwan, Mostafa Salem, Joaquim Salvi, Irina Sanchez, Irina Sánchez, Heitor M. Santos, Emmett Sartor, Dawid Schellingerhout, Klaudius Scheufele, Matthew R. Scott, Artur A. Scussel, Sara Sedlar, Juan Pablo Serrano-Rubio, N. Jon Shah, Nameetha Shah, Mazhar Shaikh, B. Uma Shankar, Zeina Shboul, Haipeng Shen, Dinggang Shen, Linlin Shen, Haocheng Shen, Varun Shenoy, Feng Shi, Hyung Eun Shin, Hai Shu, Diana Sima, M Sinclair, Orjan Smedby, James M. Snyder, Mohammadreza Soltaninejad, Guidong Song, Mehul Soni, Jean Stawiaski, Shashank Subramanian, Li Sun, Roger Sun, Jiawei Sun, Kay Sun, Yu Sun, Guoxia Sun, Shuang Sun, Yannick R Suter, Laszlo Szilagyi, Sanjay Talbar, DaCheng Tao, Zhongzhao Teng, Siddhesh Thakur, Meenakshi H Thakur, Sameer Tharakan, Pallavi Tiwari, Guillaume Tochon, Tuan Tran, Yuhsiang M. Tsai, Kuan-Lun Tseng, Tran Anh Tuan, Vadim Turlapov, Nicholas Tustison, Maria Vakalopoulou, Sergi Valverde, Rami Vanguri, Evgeny Vasiliev, Jonathan Ventura, Luis Vera, Tom Vercauteren, C. A. Verrastro, Lasitha Vidyaratne, Veronica Vilaplana, Ajeet Vivekanandan, Qian Wang, Chiatse J. Wang, Wei-Chung Wang, Duo Wang, Ruixuan Wang, Yuanyuan Wang, Chunliang Wang, Guotai Wang, Ning Wen, Xin Wen, Leon Weninger, Wolfgang Wick, Shaocheng Wu, Qiang Wu, Yihong Wu, Yong Xia, Yanwu Xu, Xiaowen Xu, Peiyuan Xu, Tsai-Ling Yang, Xiaoping Yang, Hao-Yu Yang, Junlin Yang, Haojin Yang, Guang Yang, Hongdou Yao, Xujiong Ye, Changchang Yin, Brett Young-Moxon, Jinhua Yu, Xiangyu Yue, Songtao Zhang, Angela Zhang, Kun Zhang, Xue-jie Zhang, Lichi Zhang, Xiaoyue Zhang, Yazhuo Zhang, Lei Zhang, Jian-Guo Zhang, Xiang Zhang, Tianhao Zhang, Sicheng Zhao, Yu Zhao, Xiaomei Zhao, Liang Zhao, Yefeng Zheng, Liming Zhong, Chenhong Zhou, Xiaobing Zhou, Fan Zhou, Hongtu Zhu, Jin Zhu, Ying Zhuge, Weiwei Zong, Jayashree Kalpathy-Cramer, Keyvan Farahani, Christos Davatzikos, Koen van Leemput, Bjoern Menze

This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i. e., 2012-2018.

Brain Tumor Segmentation Survival Prediction +1

An Order Preserving Bilinear Model for Person Detection in Multi-Modal Data

1 code implementation20 Dec 2017 Oytun Ulutan, Benjamin S. Riggan, Nasser M. Nasrabadi, B. S. Manjunath

We propose a new order preserving bilinear framework that exploits low-resolution video for person detection in a multi-modal setting using deep neural networks.

Human Detection

Weakly Supervised Manifold Learning for Dense Semantic Object Correspondence

no code implementations ICCV 2017 Utkarsh Gaur, B. S. Manjunath

We also leverage a key correspondence problem insight that the geometric structure between object parts is consistent across multiple object instances.

General Classification

Exploiting Spatial Structure for Localizing Manipulated Image Regions

no code implementations ICCV 2017 Jawadul H. Bappy, Amit K. Roy-Chowdhury, Jason Bunk, Lakshmanan Nataraj, B. S. Manjunath

In order to formulate the framework, we employ a hybrid CNN-LSTM model to capture discriminative features between manipulated and non-manipulated regions.

Image Manipulation Semantic Segmentation

Optimizing Region Selection for Weakly Supervised Object Detection

no code implementations5 Aug 2017 Wenhui Jiang, Thuyen Ngo, B. S. Manjunath, Zhicheng Zhao, Fei Su

This region selection procedure is further integrated into a CNN-based weakly supervised detection (WSD) framework, and can be performed in each stochastic gradient descent mini-batch during training.

Weakly Supervised Object Detection

Summarization of ICU Patient Motion from Multimodal Multiview Videos

no code implementations28 Jun 2017 Carlos Torres, Kenneth Rose, Jeffrey C. Fried, B. S. Manjunath

There is a small number of clinical studies, which use manual analysis of sleep poses and motion recordings to support medical benefits of patient positioning and motion monitoring.

General Classification

Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery

no code implementations16 Oct 2016 Archith J. Bency, Swati Rallapalli, Raghu K. Ganti, Mudhakar Srivatsa, B. S. Manjunath

Spatial Auto-Regression (SAR) is a common tool used to model such data, where the spatial contiguity matrix (W) encodes the spatial correlations.

Eye-CU: Sleep Pose Classification for Healthcare using Multimodal Multiview Data

no code implementations7 Feb 2016 Carlos Torres, Victor Fragoso, Scott D. Hammond, Jeffrey C. Fried, B. S. Manjunath

This work addresses these issues by introducing a new method and a new system for robust automated classification of sleep poses in an Intensive Care Unit (ICU) environment.

General Classification

Search Tracker: Human-derived object tracking in-the-wild through large-scale search and retrieval

no code implementations5 Feb 2016 Archith J. Bency, S. Karthikeyan, Carter De Leo, Santhoshkumar Sunderrajan, B. S. Manjunath

In this paper, we present a method to leverage human knowledge in the form of annotated video libraries in a novel search and retrieval based setting to track objects in unseen video sequences.

Object Tracking

Eye Tracking Assisted Extraction of Attentionally Important Objects From Videos

no code implementations CVPR 2015 Karthikeyan Shanmuga Vadivel, Thuyen Ngo, Miguel Eckstein, B. S. Manjunath

The proposed algorithm extracts dominant visual tracks using eye tracking data from multiple subjects on a video sequence by a combination of mean-shift clustering and Hungarian algorithm.

Frame Video Segmentation +1

Cannot find the paper you are looking for? You can Submit a new open access paper.