Digital camera

About: Digital camera is a research topic. Over the lifetime, 12169 publications have been published within this topic receiving 137431 citations. The topic is also known as: digicam & digital still camera.

A Survey on Digital Camera Image Forensic Methods

Abstract: There are two main interests in digital camera image forensics, namely source identification and forgery detection. In this paper, we first briefly provide an introduction to the major processing stages inside a digital camera and then review several methods for source digital camera identification and forgery detection. Existing methods for source identification explore the various processing stages inside a digital camera to derive the clues for distinguishing the source cameras while forgery detection checks for inconsistencies in image quality or for presence of certain characteristics as evidence of tampering.

Digital camera device with internal printer

Abstract: The specification discloses a range of printer applications including a sticker printing camera device and cartridge assembly, a PC disk drive bay printer, a PCMCIA printer and camera using such a printer, digital mobile phones with and without a print media supply, a video game console device and a digital printer cartridge with integral print media transport mechanism. As a result, the specification contains 37 pages of description, 143 claims and 88 sheets of drawings. The first invention is a digital camera (1) with an internal printer. The camera has a photo take button (14), view finder (10), camera lens (12), and a print button (15) that operates an internal inkjet printhead. The camera further has a sticker storage and feed means, an image sensor, an ink supply and means to deliver ink to the printhead to enable printing of the sensed image on a sticker.

Handbook of Machine Vision

Abstract: 1 Processing of Information in the Human Visual System (Prof. Dr. F. Schaeffel, University of Tubingen). 1.1 Preface. 1.2 Design and Structure of the Eye. 1.3 Optical Aberrations and Consequences for Visual Performance. 1.4 Chromatic Aberration. 1.5 Neural Adaptation to Monochromatic Aberrations. 1.6 Optimizing Retinal Processing with Limited Cell Numbers, Space and Energy. 1.7 Adaptation to Different Light Levels. 1.8 Rod and Cone Responses. 1.9 Spiking and Coding. 1.10 Temporal and Spatial Performance. 1.11 ON/OFF Structure, Division of theWhole Illuminance Amplitude in Two Segments. 1.12 Consequences of the Rod and Cone Diversity on Retinal Wiring. 1.13 Motion Sensitivity in the Retina. 1.14 Visual Information Processing in Higher Centers. 1.15 Effects of Attention. 1.16 Color Vision, Color Constancy, and Color Contrast. 1.17 Depth Perception. 1.18 Adaptation in the Visual System to Color, Spatial, and Temporal Contrast. 1.19 Conclusions. References. 2 Introduction to Building a Machine Vision Inspection (Axel Telljohann, Consulting Team Machine Vision (CTMV)). 2.1 Preface. 2.2 Specifying a Machine Vision System. 2.3 Designing a Machine Vision System. 2.4 Costs. 2.5 Words on Project Realization. 2.6 Examples. 3 Lighting in Machine Vision (I. Jahr, Vision & Control GmbH). 3.1 Introduction. 3.2 Demands on Machine Vision lighting. 3.3 Light used in Machine Vision. 3.4 Interaction of Test Object and Light. 3.5 Basic Rules and Laws of Light Distribution. 3.6 Light Filters. 3.7 Lighting Techniques and Their Use. 3.8 Lighting Control. 3.9 Lighting Perspectives for the Future. References. 4 Optical Systems in Machine Vision (Dr. Karl Lenhardt, Jos. Schneider OptischeWerke GmbH). 4.1 A Look on the Foundations of Geometrical Optics. 4.2 Gaussian Optics. 4.3 The Wave Nature of Light. 4.4 Information Theoretical Treatment of Image Transfer and Storage. 4.5 Criteria for Image Quality. 4.6 Practical Aspects. References. 5 Camera Calibration (R. Godding, AICON 3D Systems GmbH). 5.1 Introduction. 5.2 Terminology. 5.3 Physical Effects. 5.4 Mathematical Calibration Model. 5.5 Calibration and Orientation Techniques. 5.6 Verification of Calibration Results. 5.7 Applications. References. 6 Camera Systems in Machine Vision (Horst Mattfeldt, Allied Vision Technologies GmbH). 6.1 Camera Technology. 6.2 Sensor Technologies. 6.3 CCD Image Artifacts. 6.4 CMOS Image Sensor. 6.5 Block Diagrams and their Description. 6.6 Digital Cameras. 6.7 Controlling Image Capture. 6.8 Configuration of the Camera. 6.9 Camera Noise1. 6.10 Digital Interfaces. References. 7 Camera Computer Interfaces (Tony Iglesias, Anita Salmon, Johann Scholtz, Robert Hedegore, Julianna Borgendale, Brent Runnels, Nathan McKimpson, National Instruments). 7.1 Overview. 7.2 Analog Camera Buses. 7.3 Parallel Digital Camera Buses. 7.4 Standard PC Buses. 7.5 Choosing a Camera Bus. 7.6 Computer Buses. 7.7 Choosing a Computer Bus. 7.8 Driver Software. 7.9 Features of a Machine Vision System. 8 Machine Vision Algorithms (Dr. Carsten Steger, MVTec Software GmbH). 8.1 Fundamental Data Structures. 8.2 Image Enhancement. 8.3 Geometric Transformations. 8.4 Image Segmentation. 8.5 Feature Extraction. 8.6 Morphology. 8.7 Edge Extraction. 8.8 Segmentation and Fitting of Geometric Primitives. 8.9 Template Matching. 8.10 Stereo Reconstruction. 8.11 Optical Character Recognition. References. 9 Machine Vision in Manufacturing (Dr.-Ing. Peter Waszkewitz, Robert Bosch GmbH). 9.1 Introduction. 9.2 Application Categories. 9.3 System Categories. 9.4 Integration and Interfaces. 9.5 Mechanical Interfaces. 9.6 Electrical Interfaces. 9.7 Information Interfaces. 9.8 Temporal Interfaces. 9.9 Human-Machine Interfaces. 9.10 Industrial Case Studies. 9.11 Constraints and Conditions. References. Index.

Spectral Estimation Using Trichromatic Digital Cameras

Abstract: In this paper we present an alternative way to capture multi-spectral images using a conventional trichromatic digital camera combined with either absorption filters or multi-illumination. The goal in this research has focused on reducing the cost and complexity of the image acquisition system while preserving its colorimetric and spectral accuracy. This paper describes this new paradigm and summarizes recent research results.

Camera adapter based optical imaging apparatus

Abstract: The invention describes several embodiments of an adapter which can make use of the devices in any commercially available digital cameras to transform the digital camera into a fundus camera for inspecting the back of the eye, or into a microscope. The camera adapter is adapted to be placed between the camera device and the object. The devices in the camera being used are at least its optical source, photodetector sensor, memory, shutter and autofocus. Means in the adapter are provided to employ these devices and allow camera to operate its autofocus capability and its different color sensors. Methods of investigation of an object are also presented of using the adapter to transform the camera into an imaging instrument, where the effect of adjustments of elements inside the adapter are guided by the displaying screen of the camera.