Pixel

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In digital imaging, a pixel, or pel, (picture element) is a single point in a raster image, or the smallest addressable screen element in a display device; it is the smallest unit of picture that can be represented or controlled. (Wikipedia.org)






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2019 20th International Conference on Solid-State Sensors, Actuators and Microsystems & Eurosensors XXXIII (TRANSDUCERS & EUROSENSORS XXXIII)

The world's premiere conference in MEMS sensors, actuators and integrated micro and nano systems welcomes you to attend this four-day event showcasing major technological, scientific and commercial breakthroughs in mechanical, optical, chemical and biological devices and systems using micro and nanotechnology.The major areas of activity in the development of Transducers solicited and expected at this conference include but are not limited to: Bio, Medical, Chemical, and Micro Total Analysis Systems Fabrication and Packaging Mechanical and Physical Sensors Materials and Characterization Design, Simulation and Theory Actuators Optical MEMS RF MEMS Nanotechnology Energy and Power


2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Early Vision and Sensors Color, Illumination and Texture Segmentation and Grouping Motion and TrackingStereo and Structure from Motion Image -Based Modeling Physics -Based Modeling Statistical Methods and Learning in VisionVideo Surveillance and Monitoring Object, Event and Scene Recognition Vision - Based Graphics Image and Video RetrievalPerformance Evaluation Applications


2018 13th IEEE Conference on Industrial Electronics and Applications (ICIEA)

Industrial Informatics, Computational Intelligence, Control and Systems, Cyber-physicalSystems, Energy and Environment, Mechatronics, Power Electronics, Signal and InformationProcessing, Network and Communication Technologies


2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC)

IEEE CCNC 2018 will present the latest developments and technical solutions in the areas of home networking, consumer networking, enabling technologies (such as middleware) and novel applications and services. The conference will include a peer-reviewed program of technical sessions, special sessions, business application sessions, tutorials, and demonstration sessions


2018 23rd Asia and South Pacific Design Automation Conference (ASP-DAC)

ASP-DAC 2018 is the 23rd annual international conference on VLSI design automation in Asia and South Pacific regions, one of the most active regions of design and fabrication of silicon chips in the world. The conference aims at providing the Asian and South Pacific CAD/DA and Design community with opportunities of presenting recent advances and with forums for future directions in technologies related to Electronic Design Automation (EDA). The format of the meeting intends to cultivate and promote an instructive and productive interchange of ideas among EDA researchers/developers and system/circuit/device designers. All scientists, engineers, and students who are interested in theoretical and practical aspects of VLSI design and design automation are welcomed to ASP-DAC.


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Periodicals related to Pixel

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Antennas and Propagation, IEEE Transactions on

Experimental and theoretical advances in antennas including design and development, and in the propagation of electromagnetic waves including scattering, diffraction and interaction with continuous media; and applications pertinent to antennas and propagation, such as remote sensing, applied optics, and millimeter and submillimeter wave techniques.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Broadcasting, IEEE Transactions on

Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.


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Xplore Articles related to Pixel

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3D ICs and pixel sensors: The Italian VIPIX project and the European AIDA WP3 project

[{u'author_order': 1, u'affiliation': u'INFN, Univ. of Bergamo, Bergamo, Italy', u'full_name': u'Valerio Re'}] 2010 IEEE International 3D Systems Integration Conference (3DIC), 2010

Presents a collection of slides covering the following topics: 3D integrated circuits; pixel sensors; Tezzaron vertical integration technology; microelectronics; and interconnection technology.


Improved Unsupervised Classification Based on Freeman-Durden Polarimetric Decomposition

[{u'author_order': 1, u'affiliation': u'Signal Processing Laboratory, School of Electronic Information, Wuhan University, Luoyu Road 129#, 430079, Hubei province, China', u'full_name': u'Wen Yang'}, {u'author_order': 2, u'affiliation': u'Signal Processing Laboratory, School of Electronic Information, Wuhan University, Luoyu Road 129#, 430079, Hubei province, China', u'full_name': u'Tong-yuan Zou'}, {u'author_order': 3, u'affiliation': u'Signal Processing Laboratory, School of Electronic Information, Wuhan University, Luoyu Road 129#, 430079, Hubei province, China', u'full_name': u'Hong Sun'}, {u'author_order': 4, u'affiliation': u'Signal Processing Laboratory, School of Electronic Information, Wuhan University, Luoyu Road 129#, 430079, Hubei province, China', u'full_name': u'Xin Xu'}] 7th European Conference on Synthetic Aperture Radar, 2008

An improved unsupervised classification algorithm based on Freeman-Durden decomposition is presented. We survey the four different combinations of three basic scattering mechanisms by introducing the scattering power entropy and anisotropy parameter. Classification result on NASA/JPL AIRSAR L-band PolSAR data demonstrates the effectiveness of this algorithm.


Using Lowtran 6 And Dem To Derive Patr Radiance For Spot Imageries Over Mountainous Terrain

[{u'author_order': 1, u'affiliation': u'Center For Space And Remote Sensing Research National Central University', u'full_name': u'J.-Y. Chen'}, {u'author_order': 2, u'full_name': u'A.J. Chen'}, {u'author_order': 3, u'full_name': u'H.T. Wang'}] 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,, 1989

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Modified Fast Factorized Backprojection as Applied to X-Band Data for Curved Flight Paths

[{u'author_order': 1, u'affiliation': u'EADS Deutschland GmbH, Germany', u'full_name': u'Michael Brandfass'}, {u'author_order': 2, u'affiliation': u'Schlumberger Inc., Gabon', u'full_name': u'Luis Fernando Lobianco'}] 7th European Conference on Synthetic Aperture Radar, 2008

A Fast Factorized Backprojection scheme modified to X-band frequencies and applicable to small aperture beamwidths is presented to compute SAR images from real and synthetic airborne data sets. The numerical complexity and memory consumption of the algorithm is verified and compared to ordinary Backprojection. The modified Fast Factorized Backprojection scheme is investigated for exceedingly curved flight paths and compared to ...


Digital linearity correction of a wide dynamic range current-mode image sensor

[{u'author_order': 1, u'affiliation': u'Department of Electrical Engineering, \xc9cole Polytechnique of Montreal, H3c 3A7, P.O. Box 6079, Canada', u'full_name': u'Elham Khamsehashari'}, {u'author_order': 2, u'affiliation': u'Department of Electrical Engineering, \xc9cole Polytechnique of Montreal, H3c 3A7, P.O. Box 6079, Canada', u'full_name': u'Yves Audet'}, {u'author_order': 3, u'affiliation': u'Department of Electrical Engineering, \xc9cole Polytechnique of Montreal, H3c 3A7, P.O. Box 6079, Canada', u'full_name': u'Christian Fayomi'}] Proceedings of the 8th IEEE International NEWCAS Conference 2010, 2010

A method for linearity correction of a three-transistor wide dynamic range current-mode active pixel sensor is proposed. The pixel uses a PMOS readout transistor in the linear region of operation and a PMOS reset transistor that allows for a liner-logarithmic response. One of the non-linearity contributions is the effect caused by the `on' resistance of the select transistor. To eliminate ...


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IEEE-USA E-Books

  • Radiation Imaging Detectors Using SOI Technology

    <p>Silicon-on-Insulator (SOI) technology is widely used in high-performance and low-power semiconductor devices. The SOI wafers have two layers of active silicon (Si), and normally the bottom Si layer is a mere physical structure. The idea of making intelligent pixel detectors by using the bottom Si layer as sensors for X-ray, infrared light, high-energy particles, neutrons, etc. emerged from very early days of the SOI technology. However, there have been several difficult issues with fabricating such detectors and they have not become very popular until recently.</p><p>This book offers a comprehensive overview of the basic concepts and research issues of SOI radiation image detectors. It introduces basic issues to implement the SOI detector and presents how to solve these issues. It also reveals fundamental techniques, improvement of radiation tolerance, applications, and examples of the detectors.</p><p>Since the SOI detector has both a thick sensing region and CMOS transistors in a monolithic die, many ideas have emerged to utilize this technology. This book is a good introduction for people who want to develop or use SOI detectors.</p>

  • Visual Information Retrieval using Java and LIRE

    Visual information retrieval (VIR) is an active and vibrant research area, which attempts at providing means for organizing, indexing, annotating, and retrieving visual information (images and videos) from large, unstructured repositories. The goal of VIR is to retrieve matches ranked by their relevance to a given query, which is often expressed as an example image and/or a series of keywords. During its early years (1995-2000), the research efforts were dominated by content-based approaches contributed primarily by the image and video processing community. During the past decade, it was widely recognized that the challenges imposed by the lack of coincidence between an image's visual contents and its semantic interpretation, also known as semantic gap, required a clever use of textual metadata (in addition to information extracted from the image's pixel contents) to make image and video retrieval solutions efficient and effective. The need to bridge (or at least narrow) the semantic gap has been one of the driving forces behind current VIR research. Additionally, other related research problems and market opportunities have started to emerge, offering a broad range of exciting problems for computer scientists and engineers to work on. In this introductory book, we focus on a subset of VIR problems where the media consists of images, and the indexing and retrieval methods are based on the pixel contents of those images -- an approach known as content-based image retrieval (CBIR). We present an implementation-oriented overview of CBIR concepts, techniques, algorithms, and figures of merit. Most chapters are supported by examples written in Java, using Lucene (an open-source Java-based indexing and search implementation) and LIRE (Lucene Image REtrieval), an open-source Java-based library for CBIR. Table of Contents: Introduction / Information Retrieval: Selected Concepts and Techniques / Visual Features / Indexing Visual Features / LIRE: An Extensible Java CBIR Library / Concluding Remarks

  • CMOS Dual‐mode pH‐Image Sensor

    This chapter develops a dual‐mode sensor to provide an image as well as pH information for sample analysis. In addition to the pH sensing, the chapter introduces optical sensing for the Complementary Metal Oxide Semiconductor (CMOS) ion‐sensitive field‐effect transistor (ISFET). The chapter compares the schematics for 4 T‐ CMOS Image Sensors (CIS) pixel and ISFET pixel with the proposed dual‐mode pixel. When the chip area of a CMOS sensor array is fixed, the only way to improve throughput is to reduce the pixel size and increase the pixel number. The correlated double sampling (CDS) readout circuit for CIS is realized through the signal chain of CIS pixel, column S/H, and switched‐capacitor amplifier. The chapter illustrates top architecture of the dual‐mode sensor, including a 64 × 64 dual‐mode pixel array, S/H circuit, and global switched‐capacitor operational amplifier for CDS readout, 12‐bit pipelined analog‐to‐digital conversion (ADC), and row/column decoders.

  • High Dynamic Range Video

    As new displays and cameras offer enhanced color capabilities, there is a need to extend the precision of digital content. High Dynamic Range (HDR) imaging encodes images and video with higher than normal 8 bit-per-color-channel precision, enabling representation of the complete color gamut and the full visible range of luminance.However, to realize transition from the traditional toHDRimaging, it is necessary to develop imaging algorithms that work with the high-precision data. Tomake such algorithms effective and feasible in practice, it is necessary to take advantage of the limitations of the human visual system by aligning the data shortcomings to those of the human eye, thus limiting storage and processing precision. Therefore, human visual perception is the key component of the solutions we discuss in this book. This book presents a complete pipeline forHDR image and video processing fromacquisition, through compression and quality evaluation, to display. At the HDR image and video acquisition stage specialized HDR sensors or multi- exposure techniques suitable for traditional cameras are discussed. Then, we present a practical solution for pixel values calibration in terms of photometric or radiometric quantities, which are required in some technically oriented applications. Also, we cover the problem of efficient image and video compression and encoding either for storage or transmission purposes, including the aspect of backward compatibility with existing formats. Finally, we review existing HDR display technologies and the associated problems of image contrast and brightness adjustment. For this purpose tone mapping is employed to accommodate HDR content to LDR devices. Conversely, the so-called inverse tone mapping is required to upgrade LDR content for displaying on HDR devices. We overview HDR-enabled image and video quality metrics, which are needed to verify algorithms at all stages of the pipeline. Additionally, we cover successful examples of the HDR technology applications, in particular, in computer graphics and computer vision. The goal of this book is to present all discussed components of the HDR pipeline with the main focus on video. For some pipeline stages HDR video solutions are either not well established or do not exist at all, in which case we describe techniques for single HDR images. In such cases we attempt to select the techniques, which can be extended into temporal domain. Whenever needed, relevant background information on human perception is given, which enables better understanding of the design choices behind the discussed algorithms and HDR equipment. Table of Contents: Introduction / Representation of an HDR Image / HDR Image and Video Acquisition / HDR Image Quality / HDR Image, Video, and Texture Compression / Tone Reproduction / HDR Display Devices / LDR2HDR: Recovering Dynamic Range in Legacy Content / HDRI in Computer Graphics / Software

  • CMOS Image Sensor

    The solid‐state image sensor is the critical component of photo‐electronic devices such as mobile phones, digital video cameras, automotive imaging, surveillance, and biometrics. Two types of solid‐state image sensor technologies have been developed: charged coupled devices (CCD) and Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS). This chapter introduces the low‐noise CIS sensor design for biomedical application. It analyzes the key sensor design block pixel and associated noise sources. The chapter then discusses the different sensor readout architectures. Noise appearing in a reproduced image, which is “fixed” at certain spatial positions, is referred to as fixed pattern noise (FPN), usually caused by the CIS readout circuitry. Dark current FPN due to the mismatches in the pixel photodiode leakage currents tends to dominate the non‐coherent component of FPN, especially with long exposure times. The chapter also introduces 3 Meg pixel CIS design, and evaluates sensor performance for lens less imaging system.

  • Fractal Analysis of Breast Masses in Mammograms

    Fractal analysis is useful in digital image processing for the characterization of shape roughness and gray-scale texture or complexity. Breast masses present shape and gray-scale characteristics in mammograms that vary between benign masses and malignant tumors. This book demonstrates the use of fractal analysis to classify breast masses as benign masses or malignant tumors based on the irregularity exhibited in their contours and the gray-scale variability exhibited in their mammographic images. A few different approaches are described to estimate the fractal dimension (FD) of the contour of a mass, including the ruler method, box-counting method, and the power spectral analysis (PSA) method. Procedures are also described for the estimation of the FD of the gray-scale image of a mass using the blanket method and the PSA method. To facilitate comparative analysis of FD as a feature for pattern classification of breast masses, several other shape features and texture measures are described in the book. The shape features described include compactness, spiculation index, fractional concavity, and Fourier factor. The texture measures described are statistical measures derived from the gray-level cooccurrence matrix of the given image. Texture measures reveal properties about the spatial distribution of the gray levels in the given image; therefore, the performance of texture measures may be dependent on the resolution of the image. For this reason, an analysis of the effect of spatial resolution or pixel size on texture measures in the classification of breast masses is presented in the book. The results demonstrated in the book indicate that fractal analysis is more suitable for characterization of the shape than the gray-level variations of breast masses, with area under the receiver operating characteristics of up to 0.93 with a dataset of 111 mammographic images of masses. The methods and results presented in the book are useful for computer-aided diagnosis of breast cancer. Table of Contents: Computer-Aided Diagnosis of Breast Cancer / Detection and Analysis of\newline Breast Masses / Datasets of Images of Breast Masses / Methods for Fractal Analysis / Pattern Classification / Results of Classification of Breast Masses / Concluding Remarks

  • A Survey of Photometric Stereo Techniques

    Reconstructing the shape of an object from images is an important problem in computer vision that has led to a variety of solution strategies. This monograph focuses on photometric stereo, that is, techniques that exploit the observed intensity variations caused by illumination changes to recover the orientation of the surface. In the most basic setting, a diffuse surface is illuminated from at least three directions and captured with a static camera. Under some conditions, this allows to recover per-pixel surface normals. Modern approaches generalize photometric stereo in various ways; for example, relaxing constraints on lighting, surface reflectance and camera placement, or creating different types of local surface estimates. Starting with an introduction for readers unfamiliar with the subject, A Survey of Photometric Stereo Techniques discusses the foundations of this field of research. It then summarizes important trends and developments that have emerged in the last three decades. The focus is on approaches with the potential to be applied in a broad range of scenarios. This implies, for example, simple capture setups, relaxed model assumptions, and increased robustness requirements. This is an ideal reference for anyone looking for an understanding of the diverse concepts and ideas around this topic and how we can move towards more general techniques than traditional photometric stereo

  • Image Alignment and Stitching: A Tutorial

    Image Alignment and Stitching: A Tutorial reviews image alignment and image stitching algorithms. Image alignment algorithms can discover the correspondence relationships among images with varying degrees of overlap. They are ideally suited for applications such as video stabilization, summarization, and the creation of panoramic mosaics. Image stitching algorithms take the alignment estimates produced by such registration algorithms and blend the images in a seamless manner, taking care to deal with potential problems such as blurring or ghosting caused by parallax and scene movement as well as varying image exposures. Image Alignment and Stitching: A Tutorial reviews the basic motion models underlying alignment and stitching algorithms, describes effective direct (pixel-based) and feature-based alignment algorithms, and describes blending algorithms used to produce seamless mosaics. It closes with a discussion of open research problems in the area. Image Alignment and Stitching: A Tutorial is an invaluable resource for anyone planning or conducting research in this particular area, or computer vision generally. The essentials of the topic are presented in a tutorial style and an extensive bibliography guides towards further reading.

  • Image and Video Matting: A Survey

    If you've ever wanted to add that missing member of your family to a group photograph, you can now, given newly developed technology. Simply find a picture of the missing person, determine a "matte" that will lift them out and leave the background behind, and use the matte to insert them in the original image. Matting refers to the problem of accurate foreground estimation in images and video. The matte defines which pixels are foreground, which are background, and for pixels along the boundary or in semi-transparent regions such as hair, the matte defines the mixture of foreground and background at each pixel. Matting is one of the key techniques in many image editing and film production applications, thus has been extensively studied in the research literature. With the recent advances of digital cameras, using matting techniques to create novel composites or facilitate other editing tasks has gained increasing interest from both professionals as well as consumers. Consequently, various matting techniques and systems have been proposed to try to efficiently extract high quality mattes from both still images and video sequences. Image and Video Matting: A Survey provides a comprehensive review of existing image and video matting algorithms and systems, with an emphasis on the advanced techniques that have been recently proposed. Various methods are described and contrasted and finally tested against a uniform set of examples. Image and Video Matting: A Survey is most appropriate for a graduate level course in computational photography, or for any student or serious practitioner interested in the inner workings of matting methods.

  • CMOS Dual‐mode Energy‐harvesting‐image Sensor

    Recently, with the benefit of low power, high speed, and feasibility of system‐on‐chip (SoC) integration, Complementary Metal Oxide Semiconductor (CMOS) image sensors (CIS) are replacing power‐hungry charge‐coupled devices (CCD) in many biomedical applications. This chapter introduces an energy harvesting type ultra‐low‐power CIS design with an integrated power management system (PMS) towards personal diagnosis application. It explains the design details of the new energy high‐energy harvesting image (EHI) pixel structure and pixel operations. The chapter describes the details of the readout circuitry block design, as well as other ultra‐low power functional imaging and energy harvesting blocks. It also introduces the overall architecture of the EHI imager with PMS. With the 96 × 96 sensor array under 1‐V power supply, the power consumption is only 6 μW with 5 fps speed, and simulated and measured performance characteristics of the EHI CIS are also presented.




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