search

UMD     This Site





Two Clark School faculty members have contributed to a new online version of the Springer reference work, Computer Vision: A Reference Guide, edited by Katsushi Ikeuchi.

Assistant Professor Behtash Babadi (ECE/ISR) has written the entry, "Learning from a Neuroscience Perspective." Babadi’s entry gives an overview of learning from a neuroscience perspective by highlighting some key chronological findings in neuroscience that have given rise to various theories of learning and have particularly inspired major developments in artificial intelligence.

Distinguished University Professor and former ECE Chair Rama Chellappa (ECE/UMIACS) contributed the entry on "Face Alignment," transforming a given face image to a canonical coordinate system. This is done by automatically detecting facial fiducial points also called facial landmarks or keypoints (such as eyes, nose, chin and mouth corners) and then using standard transformation methods such as affine/similarity transformation.

The comprehensive reference was first compiled in 2014 and has undergone revision and expansion since then. It provides easy access to relevant information on all aspects of computer vision. An A-Z format of with more than 240 entries offers a diverse range of topics for those seeking entry into any aspect within the broad field of computer vision. More than 200 authors from industry and academia, including Babadi and Chellappa, contributed to this volume.

Each entry includes synonyms, a definition and discussion of the topic, and a robust bibliography. Extensive cross-references to other entries support searches for access to relevant information. Entries were peer-reviewed by a distinguished and diverse international advisory board. In total there are 3700 bibliographic references for further reading to enable deeper exploration into any of the topics covered.

The book is a practical resource for students who are considering entering the field, as well as professionals in other fields who need to access this vital information but may not have the time to work their way through an entire text on their topic of interest.



Related Articles:
'OysterNet' + underwater robots will aid in accurate oyster count
Exploring the 'rules of life' of natural neuronal networks could lead to faster, more efficient computers
Fermüller, ARC Lab create app to improve violin instruction
CSRankings places Maryland robotics at #10 in the U.S.
UMD’s SeaDroneSim can generate simulated images and videos to help UAV systems recognize ‘objects of interest’ in the water
Autonomous drones based on bees use AI to work together
New system uses machine learning to detect ripe strawberries and guide harvests
Shamma joins former student Mounya Elhilali in new MURI soundscape project
Abshire, Ernst receive MPower seed grant for using AI in understanding chronic pain biomarkers
Physical adversarial examples could deceive an autonomous vehicle's traffic sign recognition system

April 29, 2020


«Previous Story  

 

 

Current Headlines

Remembering Rance Cleaveland (1961-2024)

Dinesh Manocha Inducted into IEEE VGTC Virtual Reality Academy

ECE Ph.D. Student Ayooluwa (“Ayo”) Ajiboye Recognized at APEC 2024

Balachandran, Cameron, Yu Receive 2024 MURI Award

UMD, Booz Allen Hamilton Announce Collaboration with MMEC

New Research Suggests Gossip “Not Always a Bad Thing”

Ingestible Capsule Technology Research on Front Cover of Journal

Governor’s Cabinet Meeting Features Peek into Southern Maryland Research and Collaboration

Celebrating the Impact of Black Maryland Engineers and Leaders

Six Clark School Faculty Receive 2024 DURIP Awards

 
 
Back to top  
Home Clark School Home UMD Home