Dr. Ofer Miller

Enthusiastic scientist with over 15 years of experience in research development and implementing cutting-edge technologies such as signal processing, and AI oriented for Computer Vision

My Videos
Computer Vision
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Education,
(its still the most Important.....)

Ph.D. Computer Science, Tel-Aviv University, Israel .
Research interests: Computer Vision, Image Processing and Signal Understanding.
Concentrations: Graph theory models in adaptive linear segmentation for video processing.
Dissertation: Advanced Spatial and Temporal Segmentation models and Their Applications.M.Sc. Computer Science , cum-laude, Tel-Aviv University, Israel.
Research interests: Image Processing (models of Illumination in still images)
Thesis: Illumination independent change detection algorithm in a pair of gray images based on connectivity analysis along gray levelsB.A. Computer Science, Tel-Aviv Academic College, Israel .
Concentrations: Graph theory models.

HONORS AND AWARDS

• "The Most Cited Paper Award" , Image and Vision Computing Journal, published by Elsevier, June 2008.• "Celia and Marcos Maus Annual Prize" in Computer Science for distinction in Ph.D research studies, May 2002• Excellent Ph.D students scholarship from the Council for Higher Education for high technology area, December 2000.• Tel-Aviv University Award for distinction in Master of Science studies, June 1999.• Tel-Aviv University Fellowship for Master of Science students, October 1998.

Publications (Contribute to Science)

[1] O. Miller, E. Navon, A. Averbuch "Tracking of Moving Objects Based on Graph edges similarity ". IEEE International Conference on Multimedia. (ICME 2003), Baltimore, USA, July 2003.[2] O. Miller, A. Pikaz, A. Averbuch "Objects based change detection in a pair of gray images", Image and Vision Computations (IVCNZ02), Auckland, November 2002.[3] A. Averbuch, Y. Keller, O. Miller, "FFTbased image registration", IEEE International Conference on Image Processing (ICIP 2002), Rochester, New York, September 2002.[4] O. Miller, A. Averbuch , "Unsupervised Segmentation of Moving MPEG Blocks Based on Classification of Temporal Information" ,Digital Image Computing (DICTA 2003), Sydney, AU.[5] O. Miller, A. Averbuch "Automatic Adaptive Segmentation of Moving Objects Based on Spatio-Temporal Information", Digital Image Computing (DICTA 2003), Sydney, AU.[6] Y. Keller, A. Averbuch, O. Miller “Robust Phase Correlation”, In Proc. of 17th International Conference On Pattern Recognition (ICPR2004), Cambridge, UK, August 2004[7] O. Miller, E. Navon, A. Averbuch, "Color Image Segmentation Based on Adaptive Local Thresholds", Elsevier , Image and Vision Computing 23 (2005) 69-85..[8] O. Miller, A. Pikaz, A. Averbuch "Objects based change detection in a pair of gray level images", Elsevier, Pattern Recognition 38 (2005) 1976-1992.[9] O. Miller, A. Averbuch, E. Navon. "Tracking of Moving Objects in Video through invariant features in their graph representation", EURASIP, Image and Video Processing Journal. (2008) , Article 328052

Some videos of the past work for Artimedia

Some Videos from my past work for TLV University

University Videos

Lets talk Computer Vision.

Reminder : we are creating the AI using our accumulated knowledge over the history, the AI just uses the knowledge better and faster ......

Segmentation
Change Detect
Tracking Obj
C Vision via AI

Main Page

Segmentation

How we , or how does AI, segments "wisely" without any prior information ?

what about that image ?

Any idea how we can segment the image without! any prior or motion information ?

NOTE: AI was learned and trained by the Computer Vision science , NOT the vice verse ....

So , is it possible to segment accurate with no prior information and no temporal information ???

The solution is : Yes !
You just need to do it using Iterative Adaptive!!!! Local Thresholds (AI is not smart enough to understand it :) )

Graph Based approach to image segmentation presented in the article below.An algorithm that integrates edges and region-based techniques while local information is considered. The local consideration enables to derive local thresholds adaptively such that any threshold is associated with a specific region. The number of thresholds is automatically determined during the process, which is also automatically terminated

Here is the Local Thresholds Adaptive Segmentation Algorithm as was published at the Digital Image Computing conference

Roll Up

Identify changes between two gray images taken in different! time slice , is it possible ?

Illumination independent change detection technique between two gray images, does it possible !

The goal of such algorithm is to extract the objects that appear only in one of two registered images. A typical application is surveillance, where a scene is sampled at different time gap. Assumption of significant illumination difference between the two images is considered. For example, one image may be captured during daylight while the other image may be captured at night with infrared device. Now the secret for the solution here is by analyzing the connectivity along gray-levels, all the blobs that are candidates to be classified as ‘change’ are extracted from both images. Then, the candidate blobs from both images are analyzed. A Blob from one image that has no matched blob in the other image is considered as a ‘change’. The algorithm is reliable, fast, accurate, and robust even under significant changes in illumination. The worst-case time complexity of the algorithm is almost linear in the image size. Therefore, it is suitable for real-time applications.

Here is the Algorithm for it