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.

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

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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 ....

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

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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

So The above algorithms are also was considered to be the Most Cited paper !!!

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Smart Tracking

Can we track after occluded objects in linear complexity ?

Yes !! , but for linear complexity Graph based theory algorithm is required

The below algorithm was published in : EURASIP Journal on Image and Video Processing
Volume 2008, Article ID 328052, 14 pages

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If we understand how AI was really built we can tailor it much much better

Lets start with understanding of what is The K-means algorithm: so its an unsupervised machine learning algorithm used to group data points into k clusters based on their similarity, with each data point assigned to the cluster with the nearest centroid (cluster center). It works by iteratively assigning data points to clusters and then updating the centroids to the mean of the assigned points, repeating the process until no data points change cluster membership. K-means is useful for applications like customer segmentation and image analysis but requires the number of clusters (k) to be specified beforehand and is best at finding spherical clusters.

Wait , it still under construction soon to be continue............

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