Se espera que el mercado emergente de sensores de imágenes supere los 739 millones de dólares en 2034, con una tasa compuesta anual del 16%

Tecnologías emergentes de sensores de imagen 2024-2034: aplicaciones y mercados

Incluye sensores de imagen, SWIR, sensores híbridos, imágenes hiperespectrales, visión basada en eventos, imágenes de fase, OPD, sensores híbridos, CCD, fotodetectores de película delgada, fotodetectores orgánicos y de perovskita, InGaAs, sensores de imagen cuántica y espectrómetro miniaturizado


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IDTechEx's "Emerging Image Sensor Technologies 2024-2034: Applications and Markets" report evaluates the most diverse range of image sensors with varying resolutions and wavelength sensitivities. This technology is set to impact multiple industries from healthcare, biometrics, autonomous driving, agriculture, chemical sensing, and food inspection, among several others. The growing importance of these technologies is expected to contribute towards the growth of this market, with projections of it reaching US$739 million by 2034. This figure is, in fact, conservative because a much larger market value is predicted if these sensors take-off I the consumer electronics sector. As an example, the QD-on-CMOS market would note a 25X increase in revenue value if the consumer electronics space is considered.
 
Primary insight from interviews with individual players, ranging from established players to innovative start-ups, is included alongside 25 detailed company profiles that include discussion of both technology and business models and SWOT analysis. Additionally, the report includes technological and commercial readiness assessments, split by technology and application. It also discusses the commercial motivation for developing and adopting each of the emerging image sensing technologies and evaluates the barriers to entry.
 
Figure: Overview of emerging sensing technologies covered in this report.
 
Fundamental topics are covered throughout this report including the evaluation of individual technology readiness levels as well as detailed SWOT analyses for each technology.
 
From these insights it is possible to predict which technologies are most likely to succeed and which companies have positioned themselves in a more competitive position to thrive in the market.
 
This report also covers different applications that will benefit from these technologies as well as key challenges they may face in commercializing their products. The rate at which autonomy develops, for instance, will be partly dependent on the maturity of these sensors in the medium to long-term. Increased sensor maturity is synonymous with more cost effective and advanced technology, i.e., more sensitive sensors.
 
Emerging Image Sensors Go Beyond Visible/IR
While conventional CMOS detectors for visible light are well established and somewhat commoditized, at least for low value applications, there is an extensive opportunity for more complex image sensors that offer capabilities beyond that of simply acquiring red, green, and blue (RGB) intensity values. As such, extensive effort is currently being devoted to developing emerging image sensor technologies that can detect aspects of light beyond human vision. This includes imaging over a broader spectral range, over a larger area, acquiring spectral data at each pixel, and simultaneously increasing temporal resolution and dynamic range.
 
Much of this opportunity stems from the ever-increasing adoption of machine vision, in which image analysis is performed by computational algorithms. Machine learning requires as much input data as possible to establish correlations that can facilitate object identification and classification, so acquiring optical information over a different wavelength range, or with spectral resolution for example, is highly advantageous.
 
Emerging image sensor technologies offer many other benefits. Depending on the technology this can include similar capabilities at a lower cost, increased dynamic range, improve temporal resolution, spatially variable sensitivity, global shutters at high resolution, reducing the unwanted influence of scattering, flexibility/conformality, and more. A particularly important trend is the development of much cheaper alternatives to very expensive InGaAs sensors for imaging in the short-wave infra-red (SWIR, 1000-2000 nm) spectral region, which will open this capability to a much wider range of applications. This includes autonomous vehicles, in which SWIR imaging assists with distinguishing objects/materials that appear similar in the visible spectrum, while also reducing scattering from dust and fog.
 
There are several competitive emerging SWIR technologies. These include hybrid image sensors where an additional light absorbing thin film layer made of organic semiconductors or quantum dots is placed on top of a CMOS read-out circuit to increase the wavelength detection range into the SWIR region. Another technology is extended-range silicon where the properties of silicon are modified to extend the absorption range beyond its bandgap limitations. Currently dominated by expensive InGaAs sensors, these new approaches promise a substantial price reduction which is expected to encourage the adoption of SWIR imaging for new applications such as autonomous vehicles.
 
Obtaining as much information as possible from incident light is highly advantageous for applications that require object identification, since classification algorithms have more data to work with. Hyperspectral imaging, in which a complete spectrum is acquired at each pixel to product an (x, y, λ) data cube using a dispersive optical element and an image sensor, is a relatively established technology that has gained traction for precision agriculture and industrial process inspection. However, at present most hyperspectral cameras work on a line-scan principle, while SWIR hyperspectral imaging is restricted to relatively niche applications due to the high cost of InGaAs sensors that can exceed US$50,000. Emerging technologies using silicon or thin film materials look set to disrupt both these aspects, with snapshot imaging offering an alternative to line-scan cameras and with the new SWIR sensing technologies method facilitating cost reduction and adoption for a wider range of applications.
 
Another emerging image sensing technology is event-based vision, also known as dynamic vision sensing (DVS). Autonomous vehicles, drones and high-speed industrial applications require image sensing with a high temporal resolution. However, with conventional frame-based imaging a high temporal resolution produces vast amounts of data that requires computationally intensive processing. Event-based vision is an emerging technology that resolves this challenge. It is a completely new way of thinking about obtaining optical information, in which each sensor pixel reports timestamps that correspond to intensity changes. As such, event-based vision can combine greater temporal resolution of rapidly changing image regions, with much reduced data transfer and subsequent processing requirements.
 
The report also looks at the burgeoning market of miniaturized spectrometers. Driven by the growth in smart electronics and Internet of Things devices, low-cost miniaturized spectrometers are becoming increasingly relevant across different sectors. The complexity and functionalization of standard visible light sensors can be significantly improved through the integration of miniaturized spectrometers that can detect from the visible to the SWIR region of the spectrum. The future being imagined by researchers at Fraunhofer is a spectrometer weighing just 1 gram and costing a single dollar. Miniaturized spectrometers are expected to deliver inexpensive solutions to improve autonomous efficiency, particularly within industrial imaging and inspection as well as consumer electronics.
 
IDTechEx has 20 years of expertise covering emerging technologies, including image sensors, thin film materials, and semiconductors. Our analysts have closely followed the latest developments in relevant markets, interviewed key players within the industry, attended conferences, and delivered consulting projects on the field. This report examines the current status and latest trends in technology performance, supply chain, manufacturing know-how, and application development progress. It also identifies the key challenges, competition and innovation opportunities within the image sensor market.
 
Key aspects
This report provides the following information:
  • Detailed analysis of multiple emerging image sensing technologies.
  • Highly granular 10-year market forecasts, split by technology and subsequently by application. This includes over 40 individual forecast categories.
  • Technological/commercial readiness assessments, split by technology and application.
  • Commercial motivation for developing and adopting each of the emerging image sensing technologies.
  • Multiple application case studies for each image sensing technology.
  • SWOT analysis of each image sensing technology.
  • Overview of the key players within each technology category.
  • Over 25 company profiles, the majority based on recent primary interviews. These include a discussion of current status, technology, potential markets and business model, along with company financial information (where disclosed) and our SWOT analysis.
  • Selected highlights from academic research relevant to emerging image sensor technologies.
Report MetricsDetails
Historic Data2020 - 2023
CAGRThe emerging image sensing market is projected to reach US$739 million by 2034 with a CAGR of 16% from 2023.
Forecast Period2023 - 2034
Forecast UnitsVolume (thousands units), Market value (thousands USD)
Regions CoveredWorldwide
Segments CoveredSWIR, hybrid OPD-on-CMOS, QD-on-CMOS, thin film OPD PPD, hyperspectral imaging, event-based vision, wavefront imaging, x-ray image sensors, miniaturized spectrometers
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Table of Contents
1.EXECUTIVE SUMMARY
1.1.Motivation for emerging image sensor technologies
1.2.Report structure
1.3.Emerging image sensor technologies included in the report
1.4.Comparison with IDTechEx's previous Emerging Image Sensors report
1.5.Conventional image sensors: Market overview
1.6.Opportunities for SWIR image sensors
1.7.Autonomous vehicles will need machine vision
1.8.Application readiness level of SWIR detectors
1.9.Prospects for QD/OPD-on-CMOS detectors
1.10.Challenges for QD-Si technology for SWIR imaging
1.11.Future of pulse oximetry could come in the form of flexible skin patches with thin film photodetectors
1.12.Applications for hyperspectral imaging
1.13.Event-based vision promises reduced data processing and increased dynamic range
1.14.Emerging flexible x-ray sensors - Lightweight and low-cost
1.15.Miniaturised spectrometers targeting a wide range of sectors
1.16.The emergence of quantum image sensing
1.17.Emerging image sensors: Key players overview
1.18.Emerging image sensors: Key players overview (II)
1.19.10-year market forecast for emerging image sensor technologies
1.20.10-year market forecast for emerging image sensor technologies (by volume)
1.21.10-year market forecast for emerging image sensor technologies (by volume, data table)
1.22.10-year market forecast for emerging image sensor technologies (by market value)
1.23.10-year market forecast for emerging image sensor technologies (by market value, data table)
1.24.Key conclusions for emerging image sensors
2.INTRODUCTION
2.1.Motivation for emerging image sensor technologies
2.2.What is a sensor?
2.3.Sensor value chain example: Digital camera
2.4.Introduction to photodetectors
2.5.Working principle of an image sensor
2.6.Quantifying photodetector and image sensor performance
2.7.Extracting as much information as possible from light
2.8.Global autonomous car market
2.9.How many cameras needed in different automotive autonomy levels
2.10.Increasing usage of drones provides extensive market for emerging image sensors
2.11.Emerging image sensors required for drones
2.12.Industrial imaging to benefit from integrated hyperspectral "package" solutions
2.13.Advanced sensors expected to target consumer electronics
3.MARKET FORECASTS
3.1.Market forecast methodology
3.2.Parametrizing forecast curves
3.3.Determining total addressable markets
3.4.Determining revenues
3.5.10-year short-wave infra-red (SWIR) image sensors market forecast: By volume
3.6.10-year short-wave infra-red (SWIR) image sensors market forecast: by market value
3.7.10-year short-wave infra-red (SWIR) image sensors market volume forecast data table
3.8.10-year short-wave infra-red (SWIR) image sensors market value forecast data table
3.9.10-year hybrid OPD-on-CMOS image sensors market forecast: by volume
3.10.10-year hybrid OPD-on-CMOS image sensors market forecast: by market value
3.11.10-year hybrid OPD-on-CMOS image sensors market volume forecasts (data table)
3.12.10-year hybrid OPD-on-CMOS image sensors market value forecasts (data table)
3.13.10-year hybrid QD-on-CMOS image sensors market forecast: by volume without consumer electronics
3.14.10-year hybrid QD-on-CMOS image sensors market forecast: by market value without consumer electronics
3.15.10-year hybrid QD-on-CMOS image sensors market volume forecasts (data tables) without consumer electronics
3.16.10-year hybrid QD-on-CMOS image sensors market value forecasts (data tables) without consumer electronics
3.17.10-year hybrid QD-on-CMOS image sensors market forecast: by volume including consumer electronics
3.18.10-year hybrid QD-on-CMOS image sensors market forecast: by value including consumer electronics
3.19.10-year hybrid QD-on-CMOS image sensors market volume forecasts (data tables) with consumer electronics
3.20.10-year hybrid QD-on-CMOS image sensors market value forecasts (data tables) with consumer electronics
3.21.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market forecast: by volume
3.22.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market forecast: by value
3.23.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market volume forecasts (data table)
3.24.10-year thin film organic and perovskite photodetectors (OPDs and PPDs) market value forecasts (data table)
3.25.10-year hyperspectral imaging market forecast: by volume
3.26.10-year hyperspectral imaging market forecast: by value
3.27.10-year hyperspectral imaging market volume forecasts (data table)
3.28.10-year hyperspectral imaging market value forecasts (data table)
3.29.10-year event-based vision market forecast: by volume
3.30.10-year event-based vision market forecast: by value
3.31.10-year event-based vision market volume forecasts (data tables)
3.32.10-year event-based vision market value forecasts (data tables)
3.33.10-year wavefront imaging market forecast: by volume
3.34.10-year wavefront imaging market forecast: by value
3.35.10-year wavefront imaging market volume forecasts (data table)
3.36.10-year wavefront imaging market value forecasts (data table)
3.37.10-year flexible x-ray image sensors market forecast: by volume
3.38.10-year flexible x-ray image sensors market forecast: by value
3.39.10-year miniaturized spectrometers market forecast: by volume
3.40.10-year miniaturized spectrometers market forecast: by market value
3.41.10-year flexible miniaturized spectrometers market volume forecasts (data table)
3.42.10-year flexible miniaturized spectrometers market value forecasts (data table)
4.BRIEF OVERVIEW OF ESTABLISHED VISIBLE RANGE IMAGE SENSORS (CCD AND CMOS)
4.1.Conventional image sensors: Market overview
4.2.Sensor architectures: Front and backside illumination
4.3.Key components of an image sensor
4.4.Process flow for back-side-illuminated CMOS image sensors
4.5.Comparing CMOS and CCD image sensors
4.6.Image quality
4.7.CCD & CMOS image sensors
4.8.What is measured by an image sensor
4.9.Benefits of global rather than rolling shutters
4.10.Dynamic photodiodes with tuneable sensitivity
5.SHORT WAVE INFRARED (SWIR) IMAGE SENSORS
5.1.Introduction
5.1.1.Electromagnetic spectrum
5.1.2.Short-wave infrared spectrum
5.1.3.Value propositions of SWIR imaging
5.1.4.SWIR imaging reduces light scattering
5.1.5.Material choices for infrared sensors
5.1.6.Introduction to SWIR detection technologies
5.1.7.SWIR imaging: Incumbent and emerging technology options
5.1.8.Detectivity benchmarking of emerging image sensor technologies 1
5.1.9.Detectivity benchmarking of emerging image sensor technologies 2
5.1.10.Technology comparison of various image sensor technologies for SWIR imaging
5.2.Applications for SWIR Imaging
5.2.1.Applications for SWIR imaging
5.2.2.SWIR imaging for silicon wafer inspection
5.2.3.SWIR imaging for water content identifying
5.2.4.SWIR imaging for ADAS and autonomous vehicles
5.2.5.SWIR imaging for road condition sensing
5.2.6.SWIR imaging for foreign material detection
5.2.7.SWIR detection to identify different materials
5.2.8.SWIR detection for plastic sorting
5.2.9.SWIR imaging for counterfeit detection
5.2.10.SWIR imaging for temperature difference measurement
5.2.11.SWIR for live animal imaging
5.2.12.SWIR imaging for laser profiling and tracking
5.2.13.Laser profiling and tracking in medical application
5.2.14.Laser profiling and tracking in military and security application
5.2.15.SWIR detection for wearable applications
5.2.16.Battery inspection using SWIR imaging
5.2.17.SWIR sensors: Application overview
5.2.18.SWIR QD-on-CMOS imager application summary
5.2.19.SWIR image sensing for industrial process optimization
5.2.20.Industrial imaging is a growing market for SWIR sensors
5.2.21.MULTIPLE (EU Project): Focus areas, targets and participants
5.2.22.SWIR image sensors for hyperspectral imaging
5.2.23.SWIR application requirements
5.2.24.Key takeaways: SWIR Applications
5.3.InGaAs Sensors - Existing Technology for SWIR Imaging
5.3.1.InGaAs for incumbent image sensors
5.3.2.InGaAs sensor design: Solder bumps limit resolution
5.3.3.Sony improve InGaAs sensor resolution and spectral range
5.3.4.What makes InGaAs sensors expensive?
5.3.5.The challenge of high resolution and low-cost IR sensors
5.3.6.Key takeaways: InGaAs sensors
5.4.Emerging Inorganic SWIR Technologies and Players
5.4.1.Extended range silicon can be achieved through internal photoemission
5.4.2.TriEye commercialising low-cost extended silicon SWIR sensors
5.4.3.Increasing silicon CMOS sensitivity at the band edge
5.4.4.OmniVision: Making silicon CMOS sensitive to NIR
5.4.5.Germanium SWIR sensors are just now available
5.4.6.SWOT analysis: SWIR image sensors (non-hybrid, non-InGaAs)
5.4.7.Key players in the SWIR sensor market (monolithic, non-InGaAs)
5.4.8.Key takeaways: Detecting SWIR with silicon
6.HYBRID OPD-ON-CMOS IMAGE SENSORS (INCLUDING SWIR)
6.1.OPD-on-CMOS hybrid image sensors
6.2.Panasonic postponed launch of OPD-on-CMOS broadcast cameras
6.3.Fraunhofer developing affordable OPD-on-CMOS sensors
6.4.Fraunhofer's OPD-on-CMOS SWIR sensor architecture
6.5.Twisted bilayer graphene sensitive to longer wavelength IR light
6.6.Technology readiness level of OPD-on-CMOS detectors by application
6.7.SWOT analysis of OPD-on-CMOS image sensors
6.8.Supplier overview: OPD-on-CMOS hybrid image sensors
6.9.Key takeaways: Hybrid OPD on CMOS
7.HYBRID QD-ON-CMOS IMAGE SENSORS
7.1.Introduction to Hybrid QD-on-CMOS Image Sensors
7.1.1.Quantum dots absorption dependence: Size
7.1.2.Quantum dots absorption dependence: Materials
7.1.3.Quantum dots: PbS
7.1.4.Types of commercial QD sensor arrays
7.1.5.Hybrid QD-on-CMOS image sensor architecture
7.1.6.QD-on-CMOS pixelation
7.1.7.Manufacturing of QD-on-CMOS
7.1.8.QD-on-CMOS fabrication processes
7.1.9.Solution processing techniques
7.1.10.QD-on-CMOS: From solution to photodiode
7.1.11.Business model for producing QD-on-CMOS sensors
7.1.12.QD optical layer: Approaches to increase conductivity of QD films
7.1.13.Required performance level of SWIR image sensors used for ADAS / autonomous vehicles
7.1.14.Challenges for QD-Si technology for SWIR imaging
7.1.15.Pixel pitch evolution
7.1.16.Alternative QDs
7.2.QD Image Sensor for Visible Spectra
7.2.1.QD-Si hybrid image sensors: Increased sensitivity and reduced thickness
7.2.2.TFPD vs Si PD
7.2.3.QD-Si hybrid image sensors: Enabling high resolution global shutter
7.2.4.Global shutter image sensor comparison
7.3.QD Image Sensor for UV Imaging
7.3.1.Motivation
7.3.2.QD can improve sensitivity in the UV region
7.3.3.Integration with the image sensors
7.3.4.Perovskite QDs for UV sensors
7.3.5.QD-on-CMOS for UV imaging is emerging
7.3.6.Potential applications
7.4.Case Studies of Hybrid QD-on-CMOS Image Sensors
7.4.1.Hybrid QD-on-CMOS with global shutter for SWIR imaging
7.4.2.Early efforts from RTI International
7.4.3.SWIR Vision Systems' 2-layer QD system
7.4.4.SWIR Vision Systems' CQD photodetectors
7.4.5.Emberion's VS20 VIS-SWIR camera
7.4.6.Emberion's QD-graphene SWIR photoconductor
7.4.7.ST Microelectronic's QD image sensor technology
7.4.8.ST Microelectronic's QD image sensor technology
7.4.9.ICFO's graphene/QD image sensor
7.4.10.Wide spectrum image sensor enabled by Qurv Technologies
7.4.11.Imec's TFPD image sensor
7.4.12.IMEC outline QD-on-CMOS architecture roadmap
7.4.13.Applications for QD-on-CMOS image sensors
7.4.14.Pixel engine improvement to increase SNR
7.4.15.Specs of existing QD-on-CMOS image sensors
8.THIN FILM PHOTODETECTORS (ORGANIC AND PEROVSKITE)
8.1.Overview
8.1.1.Introduction to thin film photodetectors (organic and perovskite)
8.1.2.Organic photodetectors (OPDs)
8.1.3.Thin film photodetectors: Advantages and disadvantages
8.1.4.Reducing dark current to increase dynamic range
8.1.5.Tailoring the detection wavelength to specific applications
8.1.6.Extending OPDs to the NIR region: Use of cavities
8.1.7.Technical challenges for manufacturing thin film photodetectors from solution
8.1.8.Materials for thin film photodetectors
8.2.Thin Film Photodetectors: Applications and Key Players
8.2.1.Applications of organic photodetectors
8.2.2.OPDs for biometric security
8.2.3.Spray-coated organic photodiodes for medical imaging
8.2.4.ISORG develops fingerprint-on-display with OPDs
8.2.5.Flexible OPD imaging applications with a TFT active-matrix backplane
8.2.6.First OPD production line
8.2.7.Future of pulse oximetry could come in the form of flexible skin patches with organic photodetectors
8.2.8.Perovskite based image sensors offer high dynamic range
8.2.9.Commercial challenges for large-area OPD adoption
8.2.10.Technical requirements for thin film photodetector applications
8.2.11.Thin-film OPD and PPD application requirements
8.2.12.Application assessment for thin film OPDs and PPDs
8.2.13.Technology readiness level of organic and perovskite photodetectors by applications
8.2.14.SWOT analysis of large area OPD image sensors
8.2.15.Key takeaways: Thin film photodetectors
9.HYPERSPECTRAL IMAGING
9.1.Overview
9.1.1.Introduction to hyperspectral imaging
9.1.2.Multiple methods to acquire a hyperspectral data-cube
9.1.3.Contrasting device architectures for hyperspectral data acquisition
9.1.4.Line-scan (push-broom) cameras ideal for conveyor belts and satellite images
9.1.5.Comparison between 'push-broom' and older hyperspectral imaging methods
9.1.6.Line-scan hyperspectral camera design
9.1.7.Snapshot hyperspectral imaging
9.1.8.Illumination for hyperspectral imaging
9.1.9.Pansharpening for multi/hyper-spectral image enhancement
9.1.10.Hyperspectral imaging as a development of multispectral imaging
9.1.11.Trade-offs between hyperspectral and multi spectral imaging
9.1.12.High-throughput hyperspectral imaging without image degradation
9.1.13.Towards broadband hyperspectral imaging
9.2.Applications of Hyperspectral Imaging
9.2.1.Applications of hyperspectral imaging
9.2.2.Encouraging adoption of hyperspectral imaging in a production environment
9.2.3.Hyperspectral imaging and precision agriculture
9.2.4.Hyperspectral imaging for UAVs (drones)
9.2.5.Agricultural drones ecosystem develops
9.2.6.Satellite imaging with hyperspectral cameras
9.2.7.Historic drone investment creates demand for hyperspectral imaging
9.2.8.In-line inspection with hyperspectral imaging
9.2.9.Object identification with in-line hyperspectral imaging
9.2.10.Distinguishing materials from spectral differences
9.2.11.Sorting objects for recycling with hyperspectral imaging
9.2.12.Food inspection with hyperspectral imaging
9.2.13.Hyperspectral imaging for skin diagnostics
9.2.14.Hyperspectral imaging application requirements
9.2.15.Hyperspectral imaging - Barriers to entry
9.2.16.SWOT analysis: Hyperspectral imaging
9.3.Hyperspectral Imaging: Key Players
9.3.1.Specim: Market leaders in line-scan imaging
9.3.2.Headwall Photonics providing integrated software solutions
9.3.3.Resonon Inc: High-throughput hyperspectral imaging without image degradation
9.3.4.Cubert: Specialists in snapshot spectral imaging
9.3.5.Wavelength ranges vary by manufacturer
9.3.6.Hyperspectral wavelength range vs spectral resolution
9.3.7.Hyperspectral camera parameter table
9.3.8.Condi Food: Food quality monitoring with hyperspectral imaging
9.3.9.Orbital Sidekick: Hyperspectral imaging from satellites
9.3.10.Gamaya: Hyperspectral imaging for agricultural analysis
9.3.11.Telops: Infrared hyperspectral imaging for gas sensing
9.3.12.Telops: Mapping gas distribution from airborne hyperspectral cameras
9.3.13.Key players in hyperspectral imaging
9.3.14.Key takeaways: Hyperspectral imaging
10.MINIATURIZED SPECTROSCOPY
10.1.Introduction: Miniaturized spectrometers
10.2.Conventional diffractive optics - Lower resolution with decreasing spectrometer size
10.3.SWOT analysis: Diffractive optics
10.4.Filter arrays can enable more compact spectrometer designs with higher resolution
10.5.SWOT analysis: Filter arrays
10.6.Reconstructive spectroscopy is an emerging technique
10.7.SWOT analysis: Reconstructive spectroscopy
10.8.Miniaturised spectrometers targeting a wide range of sectors
10.9.Minimum specification varies widely depending on application
10.10.Consumer electronics could be a growing market for mini-spectrometers
10.11.High spectral resolution enabled on CMOS sensors
10.12.Photonic crystals as a dispersive element
10.13.Key players in mini-spectrometry
10.14.Resolution and cost are key differentiators among key players
10.15.Key takeaways: Miniaturised spectroscopy
11.EVENT-BASED VISION
11.1.Introduction
11.1.1.What is event-based sensing?
11.1.2.General event-based sensing: Pros and cons
11.1.3.What is event-based vision? 1
11.1.4.What is event-based vision? 2
11.1.5.What does event-based vision data look like?
11.1.6.Event-based vision: Pros and cons
11.1.7.Event-based vision sensors enable increased dynamic range
11.1.8.Cost and software of event-based sensors
11.2.Applications of Event-Based Vision
11.2.1.Promising applications for event-based vision
11.2.2.Event-based vision for autonomous vehicles
11.2.3.Event-based vision for unmanned ariel vehicle (UAV) collision avoidance
11.2.4.Occupant tracking (fall detection) in smart buildings
11.2.5.Event-based vision for augmented/virtual reality
11.2.6.Event-based vision for optical alignment/beam profiling
11.2.7.Event-based vision application requirements
11.2.8.Technology readiness level of event-based vision by application
11.3.Event-based Vision: Key Players
11.3.1.Event-based vision: Company landscape
11.3.2.IniVation: Aiming for organic growth
11.3.3.Prophesee: Well-funded and targeting autonomous mobility
11.3.4.Smaller companies being acquired by household names
11.3.5.Sony has gone to production with smallest pixel event-based sensor
11.3.6.SWOT analysis: Event-based vision
11.3.7.Key players in event-based vision
11.3.8.Key takeaways: Event-based vision
12.WAVEFRONT (PHASE) IMAGING
12.1.Motivation for wavefront imaging
12.2.Conventional Shack-Hartman wavefront sensors
12.3.Applications of wavefront imaging
12.4.Phasics: Innovators in wavefront imaging
12.5.Wooptix: Light-field and wavefront imaging
12.6.SWOT analysis: Wavefront imaging
12.7.Key takeaways: Wavefront imaging
13.FLEXIBLE AND DIRECT X-RAY IMAGE SENSORS
13.1.Conventional x-ray sensing
13.2.Flexible x-ray image sensors based on amorphous-Si
13.3.Spray-coated organic photodiodes for medical imaging
13.4.Direct x-ray sensing with organic semiconductors
13.5.Holst Centre develop perovskite-based x-ray sensors 1
13.6.Holst Centre develop perovskite-based x-ray sensors 2
13.7.Siemens Healthineers: Direct x-ray sensing with perovskites 1
13.8.Siemens Healthineers: Direct x-ray sensing with perovskites 2
13.9.Technology readiness level of flexible and direct x-ray sensors
13.10.SWOT analysis: Flexible and direct x-ray image sensors
13.11.Key takeaways: Flexible x-ray sensors
14.QUANTUM IMAGE SENSORS
14.1.Introduction: Quantum image sensors
14.2.Fraunhofer exploring quantum ghost imaging
14.3.Dartmouth University: Binary quanta image sensors (QIS)
14.4.Gigajot commercialising quanta image sensors
14.5.Scalable quanta image sensors
14.6.SWOT analysis: Quantum image sensing
14.7.Key takeaways: Quantum image sensors
15.COMPUTER VISION
15.1.Computer vision is segmented into 3R's
15.2.Recognition, registration and reconstruction
15.3.Sliding window algorithm - a modern foundation to computer vision
15.4.Feature extraction mechanisms - pixel-based approach
15.5.Feature extraction mechanisms - color-based approach
15.6.Feature extraction mechanisms - gradient-based approach
15.7.Using Histogram of Oriented Gradients to find people and objects in images
15.8.From artificial intelligence, to machine learning and deep learning
15.9.Artificial intelligence in the development of human-machine interactions
15.10.Terminologies and scopes
15.11.Sub-technology of computer vision
15.12.Computers can recognize objects better than humans for the first time
15.13.Computers interpret an image in a different way
15.14.Image recognition by convolutional neural networks
15.15.Object detection
16.COMPANY PROFILES
16.1.Brilliant Matters
16.2.Condi Food
16.3.Cubert
16.4.DpiX
16.5.Emberion
16.6.Fraunhofer
16.7.Gamaya
16.8.Headwall
16.9.Holst Centre
16.10.imec
16.11.IniVation
16.12.ISORG
16.13.Omnivision
16.14.Orbital Sidekick
16.15.Panasonic
16.16.Phasics
16.17.Prophesee
16.18.Qurv
16.19.Siemens Healthineers
16.20.Specim
16.21.Spectricity
16.22.Stratio
16.23.SWIR Vision Systems
16.24.TriEye
16.25.Wooptix
 

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Report Statistics

Slides 381
Companies 25
Forecasts to 2034
ISBN 9781915514943
 

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