B.S. Manjunath

About

B.S. Manjunath is a Distinguished Professor and the Frank Koenig Chair in the Department of Electrical and Computer Engineering at UC Santa Barbara. His research interests include image informatics, with a focus on the integration of imaging and data analysis to advance scientific and healthcare applications. He is associated with the Vision Research Lab and is involved with centers such as the Center for Bio-Image Informatics and the Center for Multimodal Big Data Science and Healthcare. His work emphasizes the development of innovative methods for image analysis and informatics, contributing to the fields of bio-image informatics and big data in healthcare.

Research topics

• Computer Science
• Artificial Intelligence
• Machine Learning
• Computer vision
• Data Mining
• Theoretical computer science
• Speech recognition

Selected publications

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

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2020 · 243 citations

  Senior authorCorresponding

  Comprehensive visual understanding requires detection frameworks that can effectively learn and utilize object interactions while analyzing objects individually. This is the main objective in Human-Object Interaction (HOI) detection task. In particular, relative spatial reasoning and structural connections between objects are essential cues for analyzing interactions, which is addressed by the proposed Visual-Spatial-Graph Network (VSGNet) architecture. VSGNet extracts visual features from the h…

  DOI

• Improving Patch-Based Convolutional Neural Networks for MRI Brain Tumor Segmentation by Leveraging Location Information

  Frontiers in Neuroscience · 2020 · 76 citations

  Senior authorCorresponding

  The manual brain tumor annotation process is time consuming and resource consuming, therefore, an automated and accurate brain tumor segmentation tool is greatly in demand. In this paper, we introduce a novel method to integrate location information with the state-of-the-art patch-based neural networks for brain tumor segmentation. This is motivated by the observation that lesions are not uniformly distributed across different brain parcellation regions and that a locality-sensitive segmentation…

  DOI

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

  Proceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2021 · 35 citations

  Senior authorCorresponding

  We propose a novel method to detect and visualize malware through image classification. The executable binaries are represented as grayscale images obtained from the count of N-grams (N=2) of bytes in the Discrete Cosine Transform (DCT) domain and a neural network is trained for malware detection. A shallow neural network is trained for classification, and its accuracy is compared with deep-network architectures such as ResNet that are trained using transfer learning. Neither dis-assembly nor be…

  DOI

• StressNet: Detecting Stress in Thermal Videos

  2021 · 22 citations

  Senior authorCorresponding

  Precise measurement of physiological signals is critical for the effective monitoring of human vital signs. Recent developments in computer vision have demonstrated that signals such as pulse rate and respiration rate can be extracted from digital video of humans, increasing the possibility of contact-less monitoring. This paper presents a novel approach to obtaining physiological signals and classifying stress states from thermal video. The proposed network–"StressNet"–features a hybrid emissio…

  PublisherDOI

• Context-Driven Detection of Invertebrate Species in Deep-Sea Video

  International Journal of Computer Vision · 2023-02-22 · 20 citations

  articleOpen accessSenior author

  Abstract Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale…

  PublisherOA PDFDOI

Recent grants

• The connectome and neurobiology of a novel model chordate, Ciona intestinalis

  NIH · $2.2M · 2017–2023

• Information Technology Research (ITR): Next-Generation Bio-Molecular Imaging and Information Discovery

  NSF · $7.7M · 2003–2010

• The connectome and neurobiology of a novel model chordate, Ciona intestinalis

  NIH · $538k · 2017–2022

Frequent coauthors

• Shivkumar Chandrasekaran

  72 shared

• Lakshmanan Nataraj

  63 shared

• Tajuddin Manhar Mohammed

  Mayachitra (United States)

  39 shared

• Charles Kenney

  26 shared

• Jawadul H. Bappy

  20 shared

• Michael Goebel

  20 shared

• Baris Sumengen

  Google (United States)

  20 shared

• Upamanyu Madhow

  University of California, Santa Barbara

  19 shared

Labs

• Vision Research LabPI

  Principal investigator, researchers, students and staff.

Similar researchers at University of California, Santa Barbara

• Andrew Teelh-85
• João Hespanhah-79
• Kaustav Banerjeeh-71