Dr. Sergio R. Goma Profile

Dr. Sergio R. Goma

at Qualcomm Inc

SPIE Involvement:

Conference Program Committee | Symposium Committee | Symposium Chair | Author

Publications (32)

SPIE Journal Paper | February 6, 2018

Contrast computation methods for interferometric measurement of sensor modulation transfer function

Battula, Tharun , Georgiev, Todor , Gille, Jennifer , Goma, Sergio

JEI Vol. 27 Issue 01

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

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PROCEEDINGS ARTICLE | March 17, 2015

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

Proc. SPIE. 9394, Human Vision and Electronic Imaging XX

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

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PROCEEDINGS ARTICLE | March 11, 2015

Depth enhanced and content aware video stabilization

Proc. SPIE. 9411, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2015

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

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PROCEEDINGS ARTICLE | February 8, 2015

MTF evaluation of white pixel sensors

Proc. SPIE. 9396, Image Quality and System Performance XII

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

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PROCEEDINGS ARTICLE | May 15, 2014

Deriving video content type from HEVC bitstream semantics

Proc. SPIE. 9139, Real-Time Image and Video Processing 2014

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

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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 | March 7, 2014

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

Proc. SPIE. 9020, Computational Imaging XII

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

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PROCEEDINGS ARTICLE | February 18, 2014

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

Proc. SPIE. 9030, Mobile Devices and Multimedia: Enabling Technologies, Algorithms, and Applications 2014

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

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PROCEEDINGS ARTICLE | March 12, 2013

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

Proc. SPIE. 8650, Three-Dimensional Image Processing (3DIP) and Applications 2013

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

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PROCEEDINGS ARTICLE | March 12, 2013

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

Proc. SPIE. 8650, Three-Dimensional Image Processing (3DIP) and Applications 2013

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

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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 | March 7, 2013

Lytro camera technology: theory, algorithms, performance analysis

Proc. SPIE. 8667, Multimedia Content and Mobile Devices

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

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PROCEEDINGS ARTICLE | March 7, 2013

Temporal image stacking for noise reduction and dynamic range improvement

Proc. SPIE. 8667, Multimedia Content and Mobile Devices

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

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PROCEEDINGS ARTICLE | March 7, 2013

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

Proc. SPIE. 8667, Multimedia Content and Mobile Devices

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

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SPIE Journal Paper | February 21, 2013

Special Section Guest Editorial: Mobile Computational Photography

Georgiev, Todor , Lumsdaine, Andrew , Goma, Sergio

JEI Vol. 22 Issue 01

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 | February 19, 2013

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

Proc. SPIE. 8656, Real-Time Image and Video Processing 2013

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

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PROCEEDINGS ARTICLE | February 24, 2012

Unassisted 3D camera calibration

Proc. SPIE. 8288, Stereoscopic Displays and Applications XXIII

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

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PROCEEDINGS ARTICLE | February 18, 2012

3D discomfort from vertical and torsional disparities in natural images

Proc. SPIE. 8291, Human Vision and Electronic Imaging XVII

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

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PROCEEDINGS ARTICLE | May 5, 2011

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

Proc. SPIE. 8050, Signal Processing, Sensor Fusion, and Target Recognition XX

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

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PROCEEDINGS ARTICLE | February 2, 2011

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

Proc. SPIE. 7871, Real-Time Image and Video Processing 2011

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

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PROCEEDINGS ARTICLE | January 27, 2011

Content-based depth estimation in focused plenoptic camera

Proc. SPIE. 7864, Three-Dimensional Imaging, Interaction, and Measurement

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

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PROCEEDINGS ARTICLE | January 27, 2011

Introducing the depth transfer curve for 3D capture system characterization

Proc. SPIE. 7864, Three-Dimensional Imaging, Interaction, and Measurement

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

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PROCEEDINGS ARTICLE | January 27, 2011

3D image processing architecture for camera phones

Proc. SPIE. 7864, Three-Dimensional Imaging, Interaction, and Measurement

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

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PROCEEDINGS ARTICLE | January 24, 2011

RAW camera DPCM compression performance analysis

Proc. SPIE. 7867, Image Quality and System Performance VIII

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

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PROCEEDINGS ARTICLE | January 19, 2010

Evaluation methodology for Bayer demosaic algorithms in camera phones

Proc. SPIE. 7537, Digital Photography VI

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

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PROCEEDINGS ARTICLE | January 18, 2010

Evaluating the quality of EDOF in camera phones

Proc. SPIE. 7529, Image Quality and System Performance VII

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

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PROCEEDINGS ARTICLE | February 5, 2009

Real-time development system for image processing engines

Proc. SPIE. 7244, Real-Time Image and Video Processing 2009

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

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PROCEEDINGS ARTICLE | January 19, 2009

Applying image quality in cell phone cameras: lens distortion

Proc. SPIE. 7242, Image Quality and System Performance VI

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

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PROCEEDINGS ARTICLE | January 19, 2009

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

Proc. SPIE. 7250, Digital Photography V

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

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PROCEEDINGS ARTICLE | March 3, 2008

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

Proc. SPIE. 6817, Digital Photography IV

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

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PROCEEDINGS ARTICLE | February 12, 2008

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

Proc. SPIE. 6811, Real-Time Image Processing 2008

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

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PROCEEDINGS ARTICLE | February 20, 2007

Bad pixel location algorithm for cell phone cameras

Proc. SPIE. 6502, Digital Photography III

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

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PROCEEDINGS ARTICLE | February 10, 2006

Computational inexpensive two-step auto white balance method

Proc. SPIE. 6069, Digital Photography II

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

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PROCEEDINGS ARTICLE | February 10, 2006

Novel bilateral filter approach: Image noise reduction with sharpening

Proc. SPIE. 6069, Digital Photography II

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

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