Jim Brailean

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James Brailean, PHD. – Co-Founder and Managing PartnerDr. Brailean co-founded Karmel Capital in 2013 and is a Managing Partner. Prior to Karmel Capital, Dr. Brailean founded PacketVideo Corporation in 1998 and became its CEO in 2000. PacketVideo launched the world’s first commercial end-to-end wireless mobile media delivery platform in 2000. PacketVideo helped create a new industry for wireless multimedia communications, shipping over one billion pieces of software. PacketVideo was acquired by NTT DOCOMO, a large Japanese telecommunications company in October 2010. Dr. Brailean has served on several public and private company boards of directors including DivX (NASDAQ: DIVX), Sonic Solutions (SNIC), NextWave Corporation (NASDAQ: WAVE), and Nik Software, Inc. (acquired by Google), Flock Freight, Intelligent Blends and BURL Concepts. Dr. Brailean is a member of Young Presidents Organization since and the Chief Executive Organization. Dr. Brailean received his PhD in Electrical Engineering from Northwestern University in 1992

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Papers by Jim Brailean

Evanston, IL 60208-3118

IEEE Circuits and Systems Society

IEEE Transactions on Biomedical Circuits and Systems, 2017

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Evanston, IL 60208-3118

Abstraet--An edge detection algorithm using multi-state adaptive linear neurons (ADALINES) is pre... more Abstraet--An edge detection algorithm using multi-state adaptive linear neurons (ADALINES) is presented. Although the tri-state ADALINE is only considered in this work, general multi-state input vectors with extreme values are shown to be linearly separable from the rest of the vectors with the same dimension. The input state of each ADALINE is defined using the local mean in a predefined mask. In addition to the binary input states + 1, the 0 input state is introduced for controlling the noise effect. If the input pattern matches one of the predefined edge patterns, the corresponding pixel is detected as an edge pixel. Experimental results are shown where the proposed detector is compared with both the Canny and LOG edge detectors. Edge detection Linear neural networks Pattern recognition 1.

Noise Reduction Filters for Dynamic lmage Sequences: A Review

In this paper, a thorough review is presented of noise reduction j l t ers for digital image sequ... more In this paper, a thorough review is presented of noise reduction j l t ers for digital image sequences. Detailed descriptions of sev-eral spatiotemporal and temporal noise reduction algorithms are provided. To aid in comparing between these different algorithms, we classrfy them based on their support (i.e., 3- 0 or I- D j l t e r) and whether or not motion compensation is employed. Several algorithms from each of the four categories are implemented and tested on real sequences degraded to various signal-to-noise ratios. These experimental results are discussed and analyzed to determine the overall advantages and disadvantages of the four general classijications, as well as, the individual j l ters. I.

A Recursive Vector

Simultaneous Recursive Motion Estimation and Restoration of Noisy and Blurred Image Sequences

Ieee Transactions on Image Processing, 1995

Method and Device for Selectively Compressing Video Codec

A method (100, 200), device (300) and microprocessor (400) are provided for selectively compressi... more A method (100, 200), device (300) and microprocessor (400) are provided for selectively compressing video frames of a motion compensated prediction-based viedo codec based on a predetermined set of compression techniques. An energy estimate of the currrent displaced frame difference, DFD, is used to compute a ratio between the estimate and a historical mean of energy estimates. The ratio is iteratively compared to a predetermined set of thresholds which are associated with the predetermined set of compression techniques. ...

Video bitstream error resilient transcoder, method, video-phone, video-communicator and device

Method, post-processing filter, and video compression system for suppressing mosquito and blocking atrifacts

Method, device and digital camera for error control and region of interest localization of a wavelet based image compression system

Device, article of manufacture, method, memory, and computer-readable memory for removing video coding errors

System and method for recommending media content

Method and apparatus for spatially adaptive filtering for video encoding

[57] ABSTRACT The present invention provides a method (200) and an apparatus (100) for spatially ... more [57] ABSTRACT The present invention provides a method (200) and an apparatus (100) for spatially adaptive filtering for video encoding. The apparatus filters a video sequence prior the encoding process. The apparatus comprises a noise variance determiner (102), a local variance determiner (104). a noise visibility function determiner (106), a Gaussian kernel determiner (108), and a convolver (110). The apparatus removes noise directly from a Displaced Frame Difference, DFD, signal. This novel approach removes noise and ...

Method and device for control and delivery of digitally compressed visual data in a heterogeneous communication network

Method and Device for Error Control in Video Compression

<title>Recursive MAP displacement estimation and restoration of noisy-blurred image sequences</title>

Visual Communications and Image Processing '93, 1993

In this paper, we develop a recursive model-based maximum a posteriori (MAP) estimator that simul... more In this paper, we develop a recursive model-based maximum a posteriori (MAP) estimator that simultaneously estimates the displacement vector field (DVF) and intensity field from a noisy- blurred image sequence. Current motion-compensated spatio-temporal filters treat the estimation of the DVF as a preprocessing step. Thus, no attempt is made to verify the accuracy of these estimates prior to their use in the filter. By simultaneously estimating these two fields, information is made available to each filter regarding the reliability of the estimates provided by the other filter. Nonstationary models are used for both the DVF and the intensity field in the proposed estimator, thus avoiding the smoothing of boundaries present in both.

Restoration of low bit rate compressed images using mean field annealing

Proceedings of ICASSP '94. IEEE International Conference on Acoustics, Speech and Signal Processing

Abstract Most of the transform-based image compression techniques produce visible artifacts in th... more Abstract Most of the transform-based image compression techniques produce visible artifacts in the reconstructed image, particularly at low bit rates. In this paper, we propose an iterative technique to reduce the unwanted degradations such as blocking artifacts while keeping the necessary detail present in the original image. The proposed technique makes use of a priori information about the original image by using a nonstationary Gauss-Markov model. A MAP estimate is obtained iteratively using mean field annealing. A additional a ...