Es wurden 40 Produkte zu dem Suchbegriff computervision in 5 Shops gefunden:

Harness the untapped potential of combining a decentralized Internet of Things (IoT) with the ability to make predictions on real-world fuzzy data. This book covers the theory behind machine learning models and shows you how to program and assemble a voice-controlled security. You’ll learn the differences between supervised and unsupervised learning and how the nuts-and-bolts of a neural network actually work. You’ll also learn to identify and measure the metrics that tell how well your classifier is doing. An overview of other types of machine learning techniques, such as genetic algorithms, reinforcement learning, support vector machines, and anomaly detectors will get you up and running with a familiarity of basic machine learning concepts. Chapters focus on the best practices to build models that can actually scale and are flexible enough to be embedded in multiple applications and easily reusable. With those concepts covered, you’ll dive into the tools for setting up a network to collect and process the data points to be fed to our models by using some of the ubiquitous and cheap pieces of hardware that make up today's home automation and IoT industry, such as the RaspberryPi, Arduino, ESP8266, etc. Finally, you’ll put things together and work through a couple of practical examples. You’ll deploy models for detecting the presence of people in your house, and anomaly detectors that inform you if some sensors have measured something unusual. And you’ll add a voice assistant that uses your own model to recognize your voice. What You'll Learn Develop a voice assistant to control your IoT devices Implement Computer Vision to detect changes in an environment Go beyond simple projects to also gain a grounding machine learning in general See how IoT can become "smarter" with the inception of machine learning techniques Build machine learning models using TensorFlow and OpenCV Who This Book Is For Makers and amateur programmers interested in taking simple IoT projects to the next level using TensorFlow and machine learning. Also more advanced programmers wanting an easy on ramp to machine learning concepts.

Harness the untapped potential of combining a decentralized Internet of Things (IoT) with the ability to make predictions on real-world fuzzy data. This book covers the theory behind machine learning models and shows you how to program and assemble a voice-controlled security. You’ll learn the differences between supervised and unsupervised learning and how the nuts-and-bolts of a neural network actually work. You’ll also learn to identify and measure the metrics that tell how well your classifier is doing. An overview of other types of machine learning techniques, such as genetic algorithms, reinforcement learning, support vector machines, and anomaly detectors will get you up and running with a familiarity of basic machine learning concepts. Chapters focus on the best practices to build models that can actually scale and are flexible enough to be embedded in multiple applications and easily reusable. With those concepts covered, you’ll dive into the tools for setting up a network to collect and process the data points to be fed to our models by using some of the ubiquitous and cheap pieces of hardware that make up today's home automation and IoT industry, such as the RaspberryPi, Arduino, ESP8266, etc. Finally, you’ll put things together and work through a couple of practical examples. You’ll deploy models for detecting the presence of people in your house, and anomaly detectors that inform you if some sensors have measured something unusual. And you’ll add a voice assistant that uses your own model to recognize your voice. What You'll Learn Develop a voice assistant to control your IoT devices Implement Computer Vision to detect changes in an environment Go beyond simple projects to also gain a grounding machine learning in general See how IoT can become "smarter" with the inception of machine learning techniques Build machine learning models using TensorFlow and OpenCV Who This Book Is For Makers and amateur programmers interested in taking simple IoT projects to the next level using TensorFlow and machine learning. Also more advanced programmers wanting an easy on ramp to machine learning concepts.

Computer Vision Metrics provides an extensive survey and analysis of over 100 current and historical feature description and machine vision methods, with a detailed taxonomy for local, regional and global features. This book provides necessary background to develop intuition about why interest point detectors and feature descriptors actually work, how they are designed, with observations about tuning the methods for achieving robustness and invariance targets for specific applications. The survey is broader than it is deep, with over 540 references provided to dig deeper. The taxonomy includes search methods, spectra components, descriptor representation, shape, distance functions, accuracy, efficiency, robustness and invariance attributes, and more. Rather than providing ‘how-to’ source code examples and shortcuts, this book provides a counterpoint discussion to the many fine opencv community source code resources available for hands-on practitioners.

Lenovo ThinkVision E65 LFD 165 cm (65") Klasse (163.8 cm (64.5") sichtbar) LCD-Display mit LED-Hintergrundbeleuchtung - 4K - für interaktive Kommunikation (64ACGAT1EK)Produktbeschreibung Lenovo ThinkVision E65 LFD 165 cm (65") Klasse (163.8 cm (64.5") sichtbar) LCD-Display mit LED-Hintergrundbeleuchtung - 4K - für interaktive Kommunikation Produkttyp LCD-Flatpanel mit LED-Hintergrundbeleuchtung Diagonale Klasse 165 cm (65") Diagonale Größe 163.8 cm (64.5") Kommerzielle Verwendung Ja - interaktiv Farbe Schwarz Auflösung 3840 x 2160 Anzeigeformat 4K UHD (2160p) HDR-Format HDR10 Framerate & Enhancement-Technologie 60 Hz Refresh Rate Videoschnittstelle HDMI Anzahl der HDMI-Anschlüsse 2 Anschlüsse Konnektivität LAN PC-Schnittstelle DisplayPort, USB-C Technologie IPS LCD Hintergrundlicht-Technologie Direct LED Seitenverhältnis des Bildes 16:9 TV-Tuner Kein Tuner Profi-Funktionen Lenovo ThinkColour Unterstützung, Lenovo Zubehör- und Display-Manager Bildverbesserungen 72 % Farbraum NTSC, 75 % DCI-P3 Farbraum, 95 % sRGB-Farbraum Lautsprechersystem 2 Lautsprecher USB 8 Anschlüsse Leistungsaufnahme im Betrieb 110 Watt Abmessungen (Breite x Tiefe x Höhe) - ohne Fuß 144.63 cm x 12.6 cm x 84.7 cm Gewicht 25.1 kg Umweltschutzstandards ENERGY STAR-qualifiziert, EPEAT Silver EAN: 0198156603623 Hersteller-ArtNr.: 64ACGAT1EK Hersteller: LenovoDetailsAllgemeinProdukttypLCD-Flatpanel mit LED-HintergrundbeleuchtungStromverbrauch (max.)380 WattLeistungsaufnahme im Betrieb110 WattDiagonale Klasse165 cm (65")Diagonale Größe163.8 cm (64.5")Kommerzielle VerwendungJa - interaktivHDR fähigJaHDR-FormatHDR10TV-TunerKein TunerTV-Tuner vorhandenNeinVideoschnittstelleHDMIAnzahl der HDMI-Anschlüsse2 AnschlüssePC-SchnittstelleDisplayPort, USB-CModiEnergiesparmodusBildverbesserungen72 % Farbraum NTSC, 75 % DCI-P3 Farbraum, 95 % sRGB-FarbraumAbmessungOhne FußBreite144.63 cmTiefe12.6 cmHöhe84.7 cmGewicht25.1 kgFarbeSchwarzBildschirmLCD-TechnologieIPSPixelabstand0.372 mmFarbunterstützung1.070.000.000 FarbenAuflösung3840 x 2160Anzeigeformat4K UHD (2160p)Seitenverhältnis des Bildes16:9Framerate & Enhancement-Technologie60 Hz Refresh RateLCD Hintergrundlicht-TechnologieDirect LEDBeschichtungBlendfreiKontrastverhältnis5000:1Helligkeit400 cd/m²Blickwinkel178 GradBetrachtungswinkel (vertikal)178 GradReaktionszeit8 msProfi-FunktionenLenovo ThinkColour Unterstützung, Lenovo Zubehör- und Display-ManagerFunktionenModularer Kameraständer, Bild in Bild (kein Tuner), Bild auf Bild (kein Tuner), Daisy Chain, Low Blue Light Technology, KVM-Switch, 10-bit UnterstützungStänder & HalterungenStandfußmerkmaleNeigbarSchnittstelle für Flachbildschirmbefestigung300 x 300 mmVESA-KompatibilitätJaMedien-PlayerUSB-PortJaAnzahl der USB-Anschlüsse8AudiosystemLautsprechersystem2 LautsprecherAusgangsleistung/gesamt20 WattLautsprecher2 x Hauptkanallautsprecher - 10 WattNetzwerk & Internet-MultimediaKonnektivitätLANLAN-ProtokollGigabit EthernetVerbindungenAnschlusstyp4 x USB USB-C mit 15 W Spannungsversorgung USB-Upstream USB-C Upstream mit 96W Power 2 x HDMI DisplayPort-Eingang DisplayPort-Ausgang USB-C mit DisplayPort 1.4 LANVerschiedenesEnthaltene KabelKabel USB-C auf USB-CKennzeichnungRoHS, TUV Rheinland Low Blue Light Certification, TUV Rheinland Flicker Free Certification, High Bit Rate 3 (HBR3)Informationen zur NachhaltigkeitEPEAT-kompatibelEPEAT SilverJaStromversorgungFunktionenIntelligente LeistungAbmessungen & Gewicht (Transport)Breite ()162 cmTiefe ()18.8 cmHöhe ()94.5 cmGewicht ()31.4 kg [TAB:Produktsicherheit]Informationen zur ProduktsicherheitHersteller Name: Lenovo (Deutschland) GmbH Adresse: Meitnerstr. 9, 70563 Stuttgart, Germany Tel: 071165690807 : Fragen_DE@lenovo.com Produktkennzeichen Entdecken Sie den Lenovo ThinkVision E65 Monitor: Ihr Fenster in die ZukunftDer Lenovo ThinkVision E65 ist ein 4K-Ultra-HD-Monitor, der die Art und Weise, wie Sie arbeiten und Unterhaltung genießen, verändern wird. Dieser schwarze 64, 5-Zoll-LCD-Monitor wurde für anspruchsvolle Profis und Technikbegeisterte entwickelt und bietet eine außergewöhnliche Bildqualität sowie fortschrittliche Funktionen zur Optimierung Ihrer Produktivität.Scharfe und detaillierte Bilder mit 4K Ultra HD-AuflösungDer ThinkVision E65 bietet eine Auflösung von 3840 x 2160 Pixeln und liefert damit unglaublich scharfe und detaillierte Bilder. Ganz gleich, ob Sie an komplexen Grafikprojekten arbeiten, hochauflösende Filme ansehen oder Ihre Lieblingsvideospiele spielen - jedes Detail wird mit leuchtenden Farben und beeindruckender Klarheit zum Leben erweckt.Elegantes und funktionales DesignNeben seiner herausragenden Leistung besticht der Lenovo ThinkVision E65 auch durch sein schlankes und minimalistisches Design. Die mattschwarze Oberfläche fügt sich nahtlos in jede Arbeits- oder Wohnumgebung ein, während der integrierteUSB-Hub den Anschluss von Peripheriegeräten erleichtert und für Ordnung auf dem Schreibtisch sorgt.IntegrierterUSB-Hub für vereinfachte K

Erleben Sie Klarheit mit dem Lenovo ThinkVision E55 MonitorDer Lenovo ThinkVision E55 wurde für all diejenigen entwickelt, die nach überragender visueller Qualität und fortschrittlicher Funktionalität in einem einzigen Gerät suchen. Dieser 4K-Monitor bietet eine außergewöhnliche Auflösung und vielseitige Anschlussmöglichkeiten und ist damit ideal für Profis und Technikbegeisterte gleichermaßen.Kompromisslose visuelle QualitätDank IPS-Technologie liefert der Lenovo ThinkVision E55 lebendige Farben und weite Betrachtungswinkel - perfekt für die Zusammenarbeit und das Teilen von Inhalten. Die 4K-Ultra-HD-Auflösung (3840 x 2160 Pixel) liefert scharfe und detailreiche Bilder, die das visuelle Erlebnis bei jeder Aufgabe, von der Arbeit bis zur Unterhaltung, verbessern.4K Ultra HD-Auflösung: Unglaublich detaillierte und scharfe Bilder.IPS-Technologie: Lebendige Farben und große Betrachtungswinkel.8 ms Reaktionszeit: Glatte, verwacklungsfreie Bilder, ideal für Videos und Spiele.Erweiterte Konnektivität und FunktionenDer Lenovo ThinkVision E55 verfügt über einen integrierten USB-Hub, über den Sie externe Peripheriegeräte problemlos anschließen können. Die HDMI- und DisplayPort-Anschlüsse bieten vielseitige Anschlussmöglichkeiten, und die integrierten Lautsprecher machen externe Lautsprecher überflüssig und sorgen so für mehr Ordnung auf dem Schreibtisch.Integrierter USB-Hub: Einfacher Anschluss von Peripheriegeräten und Geräten.HDMI- und DisplayPort-Anschlüsse: Vielseitige Anschlussmöglichkeiten für verschiedene Videoquellen.Integrierte Lautsprecher: Integriertes Audio für ein umfassendes Multimedia-Erlebnis.Elegantes und nachhaltiges DesignMit seinem schlanken und modernen schwarzen Design fügt sich der Lenovo ThinkVision E55 nahtlos in jede Arbeits- oder Heimumgebung ein.Elegantes Design: Passt in jede Umgebung.VESA-Standardbefestigung: Flexible Installation und Positionierung.Für wen ist dieser Monitor ideal?Der Lenovo ThinkVision E55 ist perfekt für professionelle Anwender, die einen zuverlässigen, leistungsstarken 4K-Monitor für Grafik-, Videoschnitt- und Designaufgaben benötigen. Er ist auch eine gute Wahl für alle, die ein beeindruckendes visuelles Erlebnis für Spiele und Home Entertainment suchen.Unterschiede im Vergleich zu anderen MonitorenIm Gegensatz zu anderen Monitoren auf dem Markt bietet der Lenovo ThinkVision E55 ein ideales Gleichgewicht aus visueller Qualität, fortschrittlicher Funktionalität und Nachhaltigkeit. Der integrierte USB-Hub und die vielseitigen Anschlussmöglichkeiten machen ihn zu einer hervorragenden Wahl für alle, die einen umfassenden und vielseitigen Monitor suchen.Entdecken Sie den Unterschied, den ein hochwertiger Monitor ausmachen kann. Wählen Sie den Lenovo ThinkVision E55 für ein unvergleichliches Seherlebnis. Jetzt bei BestDigit kaufen! Lenovo ThinkVision E55 LFD 140 cm (55") Klasse (138.7 cm (54.6") sichtbar) LCD-Display mit LED-Hintergrundbeleuchtung - 4K - für interaktive Kommunikation (64A9GAT1EK)Produktbeschreibung Lenovo ThinkVision E55 LFD 140 cm (55") Klasse (138.7 cm (54.6") sichtbar) LCD-Display mit LED-Hintergrundbeleuchtung - 4K - für interaktive Kommunikation Produkttyp LCD-Flatpanel mit LED-Hintergrundbeleuchtung Diagonale Klasse 140 cm (55") Diagonale Größe 138.7 cm (54.6") Kommerzielle Verwendung Ja - interaktiv Farbe Schwarz Auflösung 3840 x 2160 Anzeigeformat 4K UHD (2160p) HDR-Format HDR10 Framerate & Enhancement-Technologie 60 Hz Refresh Rate Videoschnittstelle HDMI Anzahl der HDMI-Anschlüsse 2 Anschlüsse Konnektivität LAN PC-Schnittstelle DisplayPort, USB-C Technologie IPS LCD Hintergrundlicht-Technologie Direct LED Seitenverhältnis des Bildes 16:9 TV-Tuner Kein Tuner Profi-Funktionen Lenovo ThinkColour Unterstützung, Lenovo Zubehör- und Display-Manager Bildverbesserungen 72 % Farbraum NTSC, 75 % DCI-P3 Farbraum, 95 % sRGB-Farbraum Lautsprechersystem 2 Lautsprecher USB 8 Anschlüsse Leistungsaufnahme im Betrieb 100 Watt Abmessungen (Breite x Tiefe x Höhe) - ohne Fuß 122.7 cm x 12.51 cm x 72.07 cm Gewicht 17.9 kg Umweltschutzstandards ENERGY STAR-qualifiziert, EPEAT Silver EAN: 0198156603630 Hersteller-ArtNr.: 64A9GAT1EK Hersteller: LenovoDetailsAllgemeinProdukttypLCD-Flatpanel mit LED-HintergrundbeleuchtungStromverbrauch (max.)300 WattLeistungsaufnahme im Betrieb100 WattDiagonale Klasse140 cm (55")Diagonale Größe138.7 cm (54.6")Kommerzielle VerwendungJa - interaktivHDR fähigJaHDR-FormatHDR10TV-TunerKein TunerTV-Tuner vorhandenNeinVideoschnittstelleHDMIAnzahl der HDMI-Anschlüsse2 AnschlüssePC-SchnittstelleDisplayPort, USB-CModiEnergiesparmodusBildverbesserungen72 % Farbraum NTSC, 75 % DCI-P3 Farbraum, 95 % sRGB-FarbraumAbmessungOhne FußBreite122.7 cmTiefe12.51 cmHöhe72.07 cmGewicht17.9 kgFarbeSchwarzBildschirmLCD-TechnologieIPSPixelabstand0.315 mmFarbunterstützung1.070.000.000 FarbenAuflösung3840 x 2160Anzeigeformat4K UHD (2160p)Seitenverhältnis des Bildes16:9Framerate & Enhancement-Te

This indispensable text introduces the foundations of three-dimensional computer vision and describes recent contributions to the field. Fully revised and updated, this much-anticipated new edition reviews a range of triangulation-based methods, including linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Also covered are intensity-based techniques that evaluate the pixel grey values in the image to infer three-dimensional scene structure, and point spread function based approaches that exploit the effect of the optical system. The text shows how methods which integrate these concepts are able to increase reconstruction accuracy and robustness, describing applications in industrial quality inspection and metrology, human-robot interaction, and remote sensing.

This indispensable text introduces the foundations of three-dimensional computer vision and describes recent contributions to the field. Fully revised and updated, this much-anticipated new edition reviews a range of triangulation-based methods, including linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Also covered are intensity-based techniques that evaluate the pixel grey values in the image to infer three-dimensional scene structure, and point spread function based approaches that exploit the effect of the optical system. The text shows how methods which integrate these concepts are able to increase reconstruction accuracy and robustness, describing applications in industrial quality inspection and metrology, human-robot interaction, and remote sensing.

This indispensable text introduces the foundations of three-dimensional computer vision and describes recent contributions to the field. Fully revised and updated, this much-anticipated new edition reviews a range of triangulation-based methods, including linear and bundle adjustment based approaches to scene reconstruction and camera calibration, stereo vision, point cloud segmentation, and pose estimation of rigid, articulated, and flexible objects. Also covered are intensity-based techniques that evaluate the pixel grey values in the image to infer three-dimensional scene structure, and point spread function based approaches that exploit the effect of the optical system. The text shows how methods which integrate these concepts are able to increase reconstruction accuracy and robustness, describing applications in industrial quality inspection and metrology, human-robot interaction, and remote sensing.

Computer Vision: Algorithms and Applications explores the variety of techniques commonly used to analyze and interpret images. It also describes challenging real-world applications where vision is being successfully used, both for specialized applications such as medical imaging, and for fun, consumer-level tasks such as image editing and stitching, which students can apply to their own personal photos and videos. More than just a source of “recipes,” this exceptionally authoritative and comprehensive textbook/reference also takes a scientific approach to basic vision problems, formulating physical models of the imaging process before inverting them to produce descriptions of a scene. These problems are also analyzed using statistical models and solved using rigorous engineering techniques. Topics and features: structured to support active curricula and project-oriented courses, with tips in the Introduction for using the book in a variety of customized courses; presents exercises at the end of each chapter with a heavy emphasis on testing algorithms and containing numerous suggestions for small mid-term projects; provides additional material and more detailed mathematical topics in the Appendices, which cover linear algebra, numerical techniques, and Bayesian estimation theory; suggests additional reading at the end of each chapter, including the latest research in each sub-field, in addition to a full Bibliography at the end of the book; supplies supplementary course material for students at the associated website, http://szeliski.org/Book/. Suitable for an upper-level undergraduate or graduate-level course in computer science or engineering, this textbook focuses on basic techniques that work under real-world conditions and encourages students to push their creative boundaries. Its design and exposition also make it eminently suitable as a unique reference to the fundamental techniques and current research literature in computer vision.

LENOVO ThinkVision P40W-20 39,7" 5120x2160 62C1GAT6EU Lenovo ThinkVision P40w-20 - LED-Monitor - gebogen - 101.6 cm (40") - HDR (62C1GAT6EU)Produktbeschreibung Lenovo ThinkVision P40w-20 - LED-Monitor - gebogen - 101.6 cm (40") - HDR Gerätetyp LED-hintergrundbeleuchteter LCD-Monitor - 101.6 cm (40") Leistungsaufnahme im Ein-Zustand 51.6 W Geschwungener Bildschirm Ja Leistungsmerkmale Gigabit Ethernet-Netzwerkadapter, USB 3.2 Gen 1/USB-C Hub, USB PD 96 Watt Bildschirmtyp IPS Seitenverhältnis 21:9 Native Auflösung WUHD 5120 x 2160 bei 75 Hz Pixelpitch 0.1815 mm Helligkeit 300 cd/m² Kontrast 1000:1 / 3000000:1 (dynamisch) Reaktionszeit 4 ms (extrem), 6 ms (typisch) Farbunterstützung 1,07 Mrd. Farben Farbraum 98% DCI-P3, 99% sRGB Eingangsanschlüsse 2xThunderbolt 4, DisplayPort, HDMI Ethernet integriert Ja Einstellungen der Anzeigeposition Höhe, Drehung, Neigung Bildschirmbeschichtung Blendfrei Spannung Wechselstrom 120/230 V (50/60 Hz) Farbe Raven Black Abmessungen (Breite x Tiefe x Höhe) - mit Standfuß (höchste Position) 94.76 cm x 27.94 cm x 64.12 cm Gewicht 14.65 kg Lokalisierung Englisch / Europa Umweltschutzstandards TCO Certified Displays 8, TCO Certified Edge Displays, ENERGY STAR-qualifiziert EPEAT Silver Kennzeichnung CCC, TUV Rheinland Low Blue Light Certification, TUV Rheinland Flicker Free Certification, China Energy standard Tier 2, TUV Rheinland Eye Comfort Certification, RoHS 2011/65/EC, TUV Eyesafe Display Certification, TUV Hardware Low Blue Light Certification Beschreibung Der professionelle Thunderbolt 4-Monitor mit Unterstützung der Intel Active Management Technology für Intel vPro-Clients. Der ThinkVision P40w-20 bietet Ihnen genau das mit einer Vielzahl branchenführender Funktionen. Der beeindruckende 39,7-Zoll-21:9-Bildschirm bietet eine umfangreiche WUHD-Auflösung von 5120 x 2160, mit der Sie jedes Detail scharf sehen können, während Sie das Gesamtbild betrachten, egal ob Sie Marktaktualisierungen verfolgen oder langfristige Rechtsverträge vergleichen. Verlassen Sie sich auf konsistente, zuverlässige und naturgetreue Farben aus jedem Betrachtungswinkel dank des professionellen Displays mit In-Plane-Switching-Panel, das Farben bis zu 98% DCI-P3 reproduziert und über eine werkseitig kalibrierte Farbe mit 2E<2 Varianz verfügt. Geschwindigkeit, Leistung und Einfachheit vereinen sich durch branchenführende Thunderbolt 4-Konnektivität, die Modellkonformität mit der USB4-Technologie, die Ihnen eine Daten- und Videoübertragung von bis zu 40 Gbit/s sowie Strom- und Netzwerkkonnektivität zwischen Ihren angeschlossenen Geräten ermöglicht - auch mit Nicht- Lenovo Markengeräte - alles über ein einziges Thunderbolt 4-Kabel. Highlights39,7 Zoll, 21:9-Seitenverhältnis, 5120 x 2160 WUHD-AuflösungMit bis zu insgesamt 12 Ports, darunter zwei Thunderbolt 4, entspricht das Modell der USB4-TechnologieIntel AMT-fähiger Thunderbolt 4-Monitor mit Intel Active Management TechnologyEAN: 0195713047593 Hersteller-ArtNr.: 62C1GAT6EU Hersteller: LenovoDetailsAllgemeinDisplay-TypLED-hintergrundbeleuchteter LCD-Monitor / TFT-AktivmatrixDiagonalabmessung101.6 cm (40")Sichtbare Bildfläche101 cm (39.7")Geschwungener BildschirmJa (2500R)Integrierte PeripheriegeräteGigabit Ethernet-Netzwerkadapter, USB 3.2 Gen 1/USB-C HubUSB-Spannungsversorgung96 WattBildschirmtypIPSSeitenverhältnis21:9Native AuflösungWUHD 5120 x 2160 bei 75 HzPixelpitch0.1815 mmPpi140Helligkeit300 cd/m²Kontrast1000:1 / 3000000:1 (dynamisch)Farbunterstützung1,07 Mrd. FarbenFarbraum98% DCI-P3, 99% sRGBReaktionszeit4 ms (extrem), 6 ms (typisch)Horizontaler Betrachtungswinkel178Vertikaler Betrachtungswinkel178BildschirmbeschichtungBlendfreiHintergrundbeleuchtungs-TechnologieWLEDBesonderheitenBild in Bild, flimmerfreie Technologie, PBP, Low Blue Light-Technologie, Monitorkalibrierung Delta E<2, 10-bit Farbtiefe, NearEdgeless-Design an 3 Seiten, Smart Power TechnologyFarbeRaven BlackAbmessungen (Breite x Tiefe x Höhe)94.76 cm x 27.94 cm x 64.12 cm - mit Standfuß (höchste Position)Gewicht14.65 kgKonnektivitätSchnittstellenThunderbolt 4 Downstream (bis zu 27 W) HDMI DisplayPort 4 x USB 3.2 Gen 1 Downstream USB 3.2 Gen 1 Upstream USB-C 3.2 Gen 1 Downstream (bis zu 27 W) Audio Line-Out Netzwerk (RJ-45) Thunderbolt 4 Upstream (Leistung bis zu 65 W (typisch) / 96 W (max.) in Smart Power-Modus)MechanischEinstellungen der AnzeigepositionHöhe, Drehung, NeigungNeigungswinkel-5/+22Schwenkwinkel90Höheneinstellung130 mmVESA-Halterung100 x 100 mm, 200 x 100 mmVerschiedenesLeistungsmerkmaleSlot für Sicherheitsschloss (Kabelschloss getrennt erhältlich), integrierte KabelführungEnthaltene Kabel1 x Kabel Thunderbolt 4KennzeichnungCCC, TUV Rheinland Low Blue Light Certification, TUV Rheinland Flicker Free Certification, China Energy standard Tier 2, TUV Rheinland Eye Comfort Certification, RoHS 2011/65/EC, TUV Eyesafe Display Certification, TUV Hardware Low Blue Light CertificationLokalisierungEnglisch / EuropaSchlosstyp zur Diebstahls

This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.

This book addresses perception and monitoring challenges in aerospace systems by employing innovative unsupervised learning techniques, thereby providing solutions for scenarios characterized by limited labelled data or dynamic environments. It explores practical methods such as domain adaptation for cross-modal pose estimation, causal inference for point cloud segmentation, and lightweight vision models optimized for edge computing. Key features include algorithm flowcharts, performance comparison tables, and real-world case studies covering planetary crater detection and spacecraft pose estimation. The integration of generative adversarial networks (GANs) for satellite jitter estimation and multistep adaptation strategies for defect detection offers actionable insights, supported by real industrial datasets, embedded hardware schematics, software code snippets, and optimization guidelines for real-time deployment. Engineers and researchers will obtain tools to enhance robustness across modalities and domains, ensuring generalizability in resource-constrained settings. This book serves as a valuable reference for aerospace engineers, computer vision specialists, and remote sensing practitioners and also empowers aerospace infrastructure inspectors adopting advanced vision technologies.

Understand how to combine computer vision and generative art. The book shows how computer vision methods can be applied to art. The book will start with an introductory chapter on computer vision and generative art followed by chapters on specific means of producing genre artworks generatively. The following seven chapters deal with the seven traditional bases for art education (i.e., elements of art: line, value, color, shape, texture, form, and space); each will show specifically how machine vision and computer graphic methods can be applied to that element. The book provides a comprehensive view of combining machine vision and art; some of the code provided has no analogy elsewhere. The book is intended to explain: What exactly is generative art? What does a generative artist do and know? How does one start creating generative artwork? What resources are out there to help artists who are not really programmers, or programmers who are not really artist? You Will Find out about extracting line data from images as well as drawing lines Understand contrast and brightness enhancements Learn about subtractive color schemes and Munsell color in a practical way Who Is This Book For Generative artists who don't program well or at all will greatly appreciate this book. There will be plenty of material for those who do not code, including simple code and an appendix to help them get started.

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will Learn Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Who This Book Is For Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

Build practical applications of computer vision using the OpenCV library with Python. This book discusses different facets of computer vision such as image and object detection, tracking and motion analysis and their applications with examples. The author starts with an introduction to computer vision followed by setting up OpenCV from scratch using Python. The next section discusses specialized image processing and segmentation and how images are stored and processed by a computer. This involves pattern recognition and image tagging using the OpenCV library. Next, you’ll work with object detection, video storage and interpretation, and human detection using OpenCV. Tracking and motion is also discussed in detail. The book also discusses creating complex deep learning models with CNN and RNN. The author finally concludes with recent applications and trends in computer vision. After reading this book, you will be able to understand and implement computer vision and its applications with OpenCV using Python. You will also be able to create deep learning models with CNN and RNN and understand how these cutting-edge deep learning architectures work. What You Will Learn Understand what computer vision is, and its overall application in intelligent automation systems Discover the deep learning techniques required to build computer vision applications Build complex computer vision applications using the latest techniques in OpenCV, Python, and NumPy Create practical applications and implementations such as face detection and recognition, handwriting recognition, object detection, and tracking and motion analysis Who This Book Is For Those who have a basic understanding of machine learning and Python and are looking to learn computer vision and its applications.

Understand how to combine computer vision and generative art. The book shows how computer vision methods can be applied to art. The book will start with an introductory chapter on computer vision and generative art followed by chapters on specific means of producing genre artworks generatively. The following seven chapters deal with the seven traditional bases for art education (i.e., elements of art: line, value, color, shape, texture, form, and space); each will show specifically how machine vision and computer graphic methods can be applied to that element. The book provides a comprehensive view of combining machine vision and art; some of the code provided has no analogy elsewhere. The book is intended to explain: What exactly is generative art? What does a generative artist do and know? How does one start creating generative artwork? What resources are out there to help artists who are not really programmers, or programmers who are not really artist? You Will Find out about extracting line data from images as well as drawing lines Understand contrast and brightness enhancements Learn about subtractive color schemes and Munsell color in a practical way Who Is This Book For Generative artists who don't program well or at all will greatly appreciate this book. There will be plenty of material for those who do not code, including simple code and an appendix to help them get started.

Dieses Lehrbuch behandelt die Grundlagen, Algorithmen und Anwendungen eines Teilbereichs der digitalen Bildverarbeitung: die Szenenanalyse bzw. die 3D-Bildanalyse. Methoden der Bildbearbeitung und der Musteranalyse stellen Werkzeuge dar, um aus den Bildinformationen von dreidimensionalen Objekten Ausgangsdaten für die Bildinterpretation zu erzeugen.

Der cuplex kryos NEXT stellt einen neuen Meilenstein der Entwicklung von CPUWasserkühlern dar. Jedes Detail wurde analysiert optimiert und getestet um Perfektion in den Bereichen Kühlleistung Montage und Funktionsumfang zu erreichen. Entstanden ist nicht nur ein CPUKühler der neue Maßstäbe bei der Kühlleistung setzt eine ganze Modellfamilie deckt ein Spektrum von Farb und Ausstattungsvarianten ab. Weltweit einmalig bei CPUWasserkühlern ist bei den VARIOModellen eine im Betrieb verformbare Bod...

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.

Organizations spend huge resources in developing software that can perform the way a human does. Image classification, object detection and tracking, pose estimation, facial recognition, and sentiment estimation all play a major role in solving computer vision problems. This book will bring into focus these and other deep learning architectures and techniques to help you create solutions using Keras and the TensorFlow library. You'll also review mutliple neural network architectures, including LeNet, AlexNet, VGG, Inception, R-CNN, Fast R-CNN, Faster R-CNN, Mask R-CNN, YOLO, and SqueezeNet and see how they work alongside Python code via best practices, tips, tricks, shortcuts, and pitfalls. All code snippets will be broken down and discussed thoroughly so you can implement the same principles in your respective environments. Computer Vision Using Deep Learning offers a comprehensive yet succinct guide that stitches DL and CV together to automate operations, reduce human intervention, increase capability, and cut the costs. What You'll Learn Examine deep learning code and concepts to apply guiding principals to your own projects Classify and evaluate various architectures to better understand your options in various use cases Go behind the scenes of basic deep learning functions to find out how they work Who This Book Is For Professional practitioners working in the fields of software engineering and data science. A working knowledge of Python is strongly recommended. Students and innovators working on advanced degrees in areas related to computer vision and Deep Learning.