Dr. Sergio R. Goma Profile

Dr. Sergio R. Goma

Engineer at Alkemista Tech INC

SPIE Involvement:

Conference Program Committee | Author

Area of Expertise:

image processing , programmable architectures , hardware design , high performance computing , system level design , image sensors

Websites:

Personal Website | Company Website

Profile Summary

For more than 16years Sergio Goma lead the multimedia R&D/standardization group for imaging at Qualcomm in San Diego, driving Qualcomm’s vision on future imaging technologies. His research interests include computer architecture, high-performance computing for real time, computational photography and programmable hardware architectures. Before Qualcomm, Sergio developed the image processing solution present in AMD’s Imageon series of chips. He received his Ph.D. in reliability and fault tolerance of computers in 1998 and holds several US patents on image processing algorithms and architectures. Sergio is a IEEE Fellow and is serving the image processing community on several review boards. For a detailed list of publications and patents as well as bio please see Sergio’s website for a complete list of publications and granted patents as well as downloads and bio pls use my webpage at http://www.blueflagiris.com/blueflag_wordpress/published-works/

Publications (34)

Proceedings Article | 1 October 2024 Presentation + Paper

Presentation + Paper

Silicon metalens toward a fully silicon integrated SWIR sensing

Matthieu Dupre, Jian Ma, Biay-Cheng Hseih, Sergio Goma

Proceedings Volume 13131, 131310E (2024) https://doi.org/10.1117/12.3027526

KEYWORDS: Sensors, Short wave infrared radiation, Cameras, Near infrared, Metalenses, Time of flight cameras, Eye tracking

Read Abstract +

Proceedings Article | 1 October 2024 Presentation + Paper

Presentation + Paper

A novel panoramic camera for smartphone applications

Jian Ma, Shenge Wang, Yunwen Li, Adrian Giura, Chris Miclea, Sergio Goma

Proceedings Volume 13130, 1313005 (2024) https://doi.org/10.1117/12.3028380

KEYWORDS: Cameras, Panoramic photography, Prisms, Image processing, Sensors, Active alignment, Video, Visualization

Read Abstract +

SPIE Journal Paper | 6 February 2018 Open Access

Open Access

Contrast computation methods for interferometric measurement of sensor modulation transfer function

Tharun Battula, Todor Georgiev, Jennifer Gille, Sergio Goma

JEI, Vol. 27, Issue 01, 013015, (February 2018) https://doi.org/10.1117/12.10.1117/1.JEI.27.1.013015

KEYWORDS: Modulation transfer functions, Sensors, Image sensors, Interferometry, Interferometers, Cameras, Spatial frequencies, Fourier transforms, Shape analysis, Computational imaging

Read Abstract +

Proceedings Article | 17 March 2015 Open Access

Open Access

Paper

Next gen perception and cognition: augmenting perception and enhancing cognition through mobile technologies

Sergio Goma

Proceedings Volume 9394, 93940I (2015) https://doi.org/10.1117/12.2086166

KEYWORDS: Computer aided design, Brain, Cognition, Data modeling, Prototyping, Visual process modeling, Computing systems, Artificial intelligence, Visualization, Silicon

Read Abstract +

Proceedings Article | 11 March 2015 Paper

Paper

Depth enhanced and content aware video stabilization

A. Lindner, K. Atanassov, S. Goma

Proceedings Volume 9411, 941106 (2015) https://doi.org/10.1117/12.2083443

KEYWORDS: Video, Transform theory, Cameras, 3D modeling, Motion models, Sensors, Cell phones, Visualization, 3D video streaming, Tin

Read Abstract +

Proceedings Article | 8 February 2015 Paper

Paper

MTF evaluation of white pixel sensors

Albrecht Lindner, Kalin Atanassov, Jiafu Luo, Sergio Goma

Proceedings Volume 9396, 939608 (2015) https://doi.org/10.1117/12.2080193

KEYWORDS: Sensors, Modulation transfer functions, Spectral resolution, Image sensors, RGB color model, Zone plates, Stars, Spatial frequencies, Target acquisition, Optical filters

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Proceedings Article | 15 May 2014 Paper

Paper

Deriving video content type from HEVC bitstream semantics

James Nightingale, Qi Wang, Christos Grecos, Sergio Goma

Proceedings Volume 9139, 913902 (2014) https://doi.org/10.1117/12.2051757

KEYWORDS: Video, Copper, Computer programming, Video compression, Plutonium, Video coding, Semantic video, Feature extraction, Visual process modeling, Internet

Read Abstract +

As network service providers seek to improve customer satisfaction and retention levels, they are increasingly moving from traditional quality of service (QoS) driven delivery models to customer-centred quality of experience (QoE) delivery models. QoS models only consider metrics derived from the network however, QoE models also consider metrics derived from within the video sequence itself. Various spatial and temporal characteristics of a video sequence have been proposed, both individually and in combination, to derive methods of classifying video content either on a continuous scale or as a set of discrete classes. QoE models can be divided into three broad categories, full reference, reduced reference and no-reference models. Due to the need to have the original video available at the client for comparison, full reference metrics are of limited practical value in adaptive real-time video applications. Reduced reference metrics often require metadata to be transmitted with the bitstream, while no-reference metrics typically operate in the decompressed domain at the client side and require significant processing to extract spatial and temporal features. This paper proposes a heuristic, no-reference approach to video content classification which is specific to HEVC encoded bitstreams. The HEVC encoder already makes use of spatial characteristics to determine partitioning of coding units and temporal characteristics to determine the splitting of prediction units. We derive a function which approximates the spatio-temporal characteristics of the video sequence by using the weighted averages of the depth at which the coding unit quadtree is split and the prediction mode decision made by the encoder to estimate spatial and temporal characteristics respectively. Since the video content type of a sequence is determined by using high level information parsed from the video stream, spatio-temporal characteristics are identified without the need for full decoding and can be used in a timely manner to aid decision making in QoE oriented adaptive real time streaming.

Proceedings Article | 7 March 2014 Paper

Paper

Structured light 3D depth map enhancement and gesture recognition using image content adaptive filtering

Vikas Ramachandra, James Nash, Kalin Atanassov, Sergio Goma

Proceedings Volume 9020, 902005 (2014) https://doi.org/10.1117/12.2058772

KEYWORDS: Image enhancement, Image filtering, 3D image enhancement, Gesture recognition, Digital filtering, 3D image processing, Cameras, Reflectivity, Imaging systems, Structured light

Read Abstract +

Proceedings Article | 18 February 2014 Paper

Paper

Evaluation of in-network adaptation of scalable high efficiency video coding (SHVC) in mobile environments

James Nightingale, Qi Wang, Christos Grecos, Sergio Goma

Proceedings Volume 9030, 90300B (2014) https://doi.org/10.1117/12.2036721

KEYWORDS: Video, Video coding, Video compression, Scalable video coding, Computer programming, Spatial resolution, Standards development, Mobile devices, Error analysis, Signal to noise ratio

Read Abstract +

Proceedings Article | 12 March 2013 Paper

Paper

Introducing the cut-out star target to evaluate the resolution performance of 3D structured-light systems

Tom Osborne, Vikas Ramachandra, Kalin Atanassov, Sergio Goma

Proceedings Volume 8650, 86500P (2013) https://doi.org/10.1117/12.2008560

KEYWORDS: Stars, Cameras, Imaging systems, 3D acquisition, 3D image processing, Rubidium, Target detection, Projection systems, 3D metrology, Structured light

Read Abstract +

Structured light depth map systems are a type of 3D system where a structured light pattern is projected into the object space and an adjacent receiving camera is used to capture the image of the scene. By using the distance between the camera and the projector together with the structured pattern you can estimate the depth of objects in the scene from the camera. It is important to be able to compare two systems to see how one compares to another. Accuracy, resolution, and speed are three aspects of a structured light system that are often used for performance evaluation. It would be ideal if we could use the accuracy and resolution measurements to answer questions such as how close two cubes can be together and be resolved as two objects. Or, determine how close a person must be to the structured light system in order to determine how many fingers this person is holding up. It turns out, from our experiments, a systems ability to resolve the shape of an object is dependent on a number of factors such as the shape of an object, its orientation and how close it is to other adjacent objects. This makes the task of comparing the resolution of two systems difficult. Our goal is to choose a target or a set of targets from which we make measurements that will enable us to quantify, on the average, the comparative resolution performance of one system to another without having to make multiple measurements on scenes with a large set of object shapes, orientations and proximities to each other. In this document we will go over a number of targets we evaluated and will focus on the “Cut-out Star Target” that we selected as being the best choice. Using this target we will show our evaluation results of two systems. The metrics we used for the evaluation were developed during this work. These metrics will not directly answers the question of how close two objects can be to each other and still be resolve, but it will indicate which system will perform better over a large set of objects, orientations and proximities to other objects.

Proceedings Article | 12 March 2013 Paper

Paper

Self-calibration of depth sensing systems based on structured-light 3D

Vikas Ramachandra, James Nash, Kalin Atanassov, Sergio Goma

Proceedings Volume 8650, 86500V (2013) https://doi.org/10.1117/12.2008556

KEYWORDS: Calibration, Cameras, Image processing, Projection systems, Image filtering, Sensing systems, Imaging systems, Factor analysis, Image compression, Fourier transforms

Read Abstract +

Proceedings Article | 7 March 2013 Open Access

Open Access

Paper

Lytro camera technology: theory, algorithms, performance analysis

Todor Georgiev, Zhan Yu, Andrew Lumsdaine, Sergio Goma

Proceedings Volume 8667, 86671J (2013) https://doi.org/10.1117/12.2013581

KEYWORDS: Cameras, Microlens, Image resolution, Sensors, Calibration, Modulation transfer functions, Computational imaging, Optical signal processing, Modulation, Microlens array

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Proceedings Article | 7 March 2013 Paper

Paper

Temporal image stacking for noise reduction and dynamic range improvement

Kalin Atanassov, James Nash, Sergio Goma, Vikas Ramachandra, Hasib Siddiqui

Proceedings Volume 8667, 86671P (2013) https://doi.org/10.1117/12.2008559

KEYWORDS: Denoising, Sensors, Image stacking, High dynamic range imaging, Image registration, Imaging systems, Image restoration, Quantization, Cameras, Anisotropic diffusion

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Proceedings Article | 7 March 2013 Paper

Paper

Touch HDR: photograph enhancement by user controlled wide dynamic range adaptation

Steve Verrall, Hasib Siddiqui, Kalin Atanassov, Sergio Goma, Vikas Ramachandra

Proceedings Volume 8667, 86671O (2013) https://doi.org/10.1117/12.2008557

KEYWORDS: High dynamic range imaging, Photography, Image enhancement, Tablets, Cameras, Digital cameras, Cell phones, Digital photography, Sensors, Image processing

Read Abstract +

SPIE Journal Paper | 21 February 2013 Open Access

Open Access

Special Section Guest Editorial: Mobile Computational Photography

Todor Georgiev, Andrew Lumsdaine, Sergio Goma

JEI, Vol. 22, Issue 01, 010901, (February 2013) https://doi.org/10.1117/12.10.1117/1.JEI.22.1.010901

KEYWORDS: Cameras, Computational imaging, Photography, Sensors, Cell phones, Wafer-level optics, Web 2.0 technologies, Visualization, Mobile devices, Sensor technology

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Proceedings Article | 19 February 2013 Paper

Paper

Digital ruler: real-time object tracking and dimension measurement using stereo cameras

James Nash, Kalin Atanassov, Sergio Goma, Vikas Ramachandra, Hasib Siddiqui

Proceedings Volume 8656, 865606 (2013) https://doi.org/10.1117/12.2008562

KEYWORDS: Electronic filtering, Sensors, Cameras, Optical filters, Filtering (signal processing), Nonlinear filtering, Stereoscopic cameras, Calibration, Error analysis, 3D metrology

Read Abstract +

Proceedings Article | 24 February 2012 Paper

Paper

Unassisted 3D camera calibration

Kalin Atanassov, Vikas Ramachandra, James Nash, Sergio Goma

Proceedings Volume 8288, 828808 (2012) https://doi.org/10.1117/12.909616

KEYWORDS: Calibration, Cameras, 3D image processing, Sensors, Video, Image processing, Stereoscopic cameras, Detection and tracking algorithms, 3D displays, Image quality

Read Abstract +

Proceedings Article | 18 February 2012 Paper

Paper

3D discomfort from vertical and torsional disparities in natural images

Christopher Tyler, Lora Likova, Kalin Atanassov, Vikas Ramachandra, Sergio Goma

Proceedings Volume 8291, 82910Q (2012) https://doi.org/10.1117/12.915466

KEYWORDS: 3D displays, 3D image processing, 3D modeling, Cameras, Visualization, Data modeling, 3D vision, Stereoscopic cameras, Eye models, Eye

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Proceedings Article | 5 May 2011 Paper

Paper

Target signature agnostic tracking with an ad-hoc network of omni-directional sensors

Kalin Atanassov, William Hodgkiss, Sergio Goma

Proceedings Volume 8050, 805017 (2011) https://doi.org/10.1117/12.883647

KEYWORDS: Sensors, Signal to noise ratio, Filtering (signal processing), Systems modeling, Active sensors, Error analysis, Sensor networks, Signal attenuation, Detection and tracking algorithms, Acoustics

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Proceedings Article | 2 February 2011 Paper

Paper

Multithreaded real-time 3D image processing software architecture and implementation

Vikas Ramachandra, Kalin Atanassov, Milivoje Aleksic, Sergio Goma

Proceedings Volume 7871, 78710A (2011) https://doi.org/10.1117/12.872594

KEYWORDS: 3D image processing, 3D displays, Image processing, Video acceleration, Video, Zoom lenses, Cameras, Video processing, OpenGL, Eye

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Proceedings Article | 27 January 2011 Paper

Paper

3D image processing architecture for camera phones

Kalin Atanassov, Vikas Ramachandra, Sergio Goma, Milivoje Aleksic

Proceedings Volume 7864, 786414 (2011) https://doi.org/10.1117/12.872617

KEYWORDS: 3D image processing, Cameras, 3D displays, Sensors, 3D modeling, Calibration, Zoom lenses, Stereoscopic cameras, Image processing, Logic

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Proceedings Article | 27 January 2011 Paper

Paper

Introducing the depth transfer curve for 3D capture system characterization

Sergio Goma, Kalin Atanassov, Vikas Ramachandra

Proceedings Volume 7864, 78640E (2011) https://doi.org/10.1117/12.879193

KEYWORDS: Cameras, Stereoscopic cameras, 3D image processing, Image processing, Brain, Eye, Visualization, Stereoscopy, Neuroimaging, 3D metrology

Read Abstract +

Proceedings Article | 27 January 2011 Paper

Paper

Content-based depth estimation in focused plenoptic camera

Kalin Atanassov, Sergio Goma, Vikas Ramachandra, Todor Georgiev

Proceedings Volume 7864, 78640G (2011) https://doi.org/10.1117/12.872667

KEYWORDS: Cameras, Spatial resolution, Image resolution, Reliability, Digital filtering, Resolution enhancement technologies, Diffusion, Data centers, Digital cameras, Photography

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Proceedings Article | 24 January 2011 Paper

Paper

RAW camera DPCM compression performance analysis

Katherine Bouman, Vikas Ramachandra, Kalin Atanassov, Mickey Aleksic, Sergio Goma

Proceedings Volume 7867, 78670N (2011) https://doi.org/10.1117/12.872637

KEYWORDS: Modulation transfer functions, Image compression, Image processing, Cameras, Quantization, Stars, Data compression, Eye, Data storage, Image quality standards

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Proceedings Article | 19 January 2010 Paper

Paper

Evaluation methodology for Bayer demosaic algorithms in camera phones

Sergio Goma, Kalin Atanassov

Proceedings Volume 7537, 753708 (2010) https://doi.org/10.1117/12.839103

KEYWORDS: Cameras, Zone plates, Image processing, Stars, MATLAB, Algorithm development, Signal to noise ratio, Image quality, Linear filtering, Algorithms

Read Abstract +

Proceedings Article | 18 January 2010 Paper

Paper

Evaluating the quality of EDOF in camera phones

Kalin Atanassov, Sergio Goma

Proceedings Volume 7529, 75290K (2010) https://doi.org/10.1117/12.839101

KEYWORDS: Cameras, Stars, Image quality, Sensors, Image processing, Imaging systems, Lens design, Optical design, Distortion, Digital cameras

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Proceedings Article | 5 February 2009 Paper

Paper

Real-time development system for image processing engines

Sergio Goma, Radu Gheorghe, Milivoje Aleksic

Proceedings Volume 7244, 724409 (2009) https://doi.org/10.1117/12.812372

KEYWORDS: Image processing, Sensors, Algorithm development, Signal processing, Video, Imaging systems, Cameras, Computer architecture, Operating systems, Detection and tracking algorithms

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Proceedings Article | 19 January 2009 Paper

Paper

An image-noise filter with emphasis on low-frequency chrominance noise

Radu Gheorghe, Sergiu Goma, Milivoje Aleksic

Proceedings Volume 7250, 72500B (2009) https://doi.org/10.1117/12.805751

KEYWORDS: Wavelets, Image filtering, Image processing, Optical filters, Digital filtering, Sensors, CMOS sensors, Transform theory, Wavelet transforms, Tungsten

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Proceedings Article | 19 January 2009 Paper

Paper

Applying image quality in cell phone cameras: lens distortion

Donald Baxter, Sergio Goma, Milivoje Aleksic

Proceedings Volume 7242, 724213 (2009) https://doi.org/10.1117/12.805814

KEYWORDS: Image quality, Distortion, Cameras, Chromatic aberrations, Objectives, Image compression, Image processing, Quality systems, Telecommunications, Detection and tracking algorithms

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Proceedings Article | 3 March 2008 Paper

Paper

An approach to improve cell-phone cameras' dynamic range using a non-linear lens correction

Sergio Goma, Milivoje Aleksic

Proceedings Volume 6817, 68170F (2008) https://doi.org/10.1117/12.764584

KEYWORDS: Cameras, High dynamic range imaging, Sensors, Image processing, CMOS sensors, Imaging systems, Image restoration, Optical filtering, Signal to noise ratio, Algorithm development

Read Abstract +

Proceedings Article | 12 February 2008 Paper

Paper

Improving the SNR during color image processing while preserving the appearance of clipped pixels

Sergio Goma, Milivoje Aleksic

Proceedings Volume 6811, 681102 (2008) https://doi.org/10.1117/12.765199

KEYWORDS: Image processing, Signal to noise ratio, Sensors, RGB color model, Image sensors, Cameras, Signal processing, Color image processing, Interference (communication), Optical filters

Read Abstract +

Proceedings Article | 20 February 2007 Paper

Paper

Bad pixel location algorithm for cell phone cameras

Sergio Goma, Milivoje Aleksic

Proceedings Volume 6502, 65020H (2007) https://doi.org/10.1117/12.702955

KEYWORDS: Sensors, CMOS sensors, Cameras, Calibration, Image sensors, Detection and tracking algorithms, Camera shutters, Cell phones, Manufacturing, Analog electronics

Read Abstract +

Proceedings Article | 10 February 2006 Paper

Paper

Computational inexpensive two-step auto white balance method

Sergio Goma, Milivoje Aleksic

Proceedings Volume 6069, 60690D (2006) https://doi.org/10.1117/12.643926

KEYWORDS: Colorimetry, RGB color model, Cameras, Image sensors, Sensors, Signal to noise ratio, Color reproduction, Reflectivity, Visualization, Image processing

Read Abstract +

Proceedings Article | 10 February 2006 Paper

Paper

Novel bilateral filter approach: Image noise reduction with sharpening

Milivoje Aleksic, Maxim Smirnov, Sergio Goma

Proceedings Volume 6069, 60690F (2006) https://doi.org/10.1117/12.643880

KEYWORDS: Image filtering, Nonlinear filtering, Linear filtering, Denoising, Optical filters, Electronic filtering, Gaussian filters, Digital filtering, Image processing, CMOS sensors

Read Abstract +

Showing 5 of 34 publications

Conference Committee Involvement (24)

Real-time Processing of Image, Depth and Video Information 2025

7 April 2025 | Prague, Czech Republic

Real-time Processing of Image, Depth and Video Information 2024

8 April 2024 | Strasbourg, France

Real-time Processing of Image, Depth and Video Information 2023

24 April 2023 | Prague, Czech Republic

Real-Time Image and Video Processing 2018

16 April 2018 | Orlando, FL, United States

Real-Time Image and Video Processing 2015

10 February 2015 | San Francisco, California, United States

Human Vision and Electronic Imaging XX

9 February 2015 | San Francisco, California, United States

Digital Photography and Mobile Imaging XI

9 February 2015 | San Francisco, California, United States

SPIE/IS&T Electronic Imaging

8 February 2015 | San Francisco, United States

Real-Time Image and Video Processing

17 April 2014 | Brussels, Belgium

Human Vision and Electronic Imaging XIX

3 February 2014 | San Francisco, California, United States

Digital Photography X

3 February 2014 | San Francisco, California, United States

IS&T/SPIE Electronic Imaging

2 February 2014 | San Francisco, United States

Real-Time Image and Video Processing 2013

6 February 2013 | Burlingame, California, United States

Mobile Imaging System Design and Image Quality

5 February 2013 | Burlingame, California, United States

Human Vision and Electronic Imaging XVIII

4 February 2013 | Burlingame, California, United States

Digital Photography IX

4 February 2013 | Burlingame, California, United States

Mobile Computational Photography

4 February 2013 | Burlingame, California, United States

IS&T/SPIE Electronic Imaging

3 February 2013 | Burlingame, United States

Real-Time Image and Video Processing

19 April 2012 | Brussels, Belgium

Digital Photography VIII

23 January 2012 | Burlingame, California, United States

Digital Photography VII

24 January 2011 | San Francisco Airport, California, United States

Real-Time Image and Video Processing 2011

24 January 2011 | San Francisco Airport, California, United States

Real-Time Image and Video Processing

16 April 2010 | Brussels, Belgium

Real-Time Image and Video Processing

19 January 2009 | San Jose, California, United States

Showing 5 of 24 Conference Committees

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