Planospheric camera model

Crotoxin complex is the principal neurotoxin of the South American rattlesnake, Crotalus durissus terrificus and has a molecular weight of 24 kDa. The protein is a heterodimer with subunit A assigneda chaperone function. Subunit B carries the lethal activity, which is exerted on both sides ofthe neuro-muscular junction, and which is thought to involve binding to the acetylcholine receptor. Insight in crotoxin complex’ mode of action can be gained from a 3 Å resolution structure obtained by electron crystallography. This abstract communicates our progress in merging the electron diffraction amplitudes into a 3-dimensional (3D) intensity data set close to completion. Since the thickness of crotoxin complex crystals varies from one crystal to the other, we chose to collect tilt series of electron diffraction patterns after determining their thickness. Furthermore, by making use of the symmetry present in these tilt data, intensities collected only from similar crystals will be merged.Suitable crystals of glucose-embedded crotoxin complex were searched for in the defocussed diffraction mode with the goniometer tilted to 55° of higher in a JEOL4000 electron cryo-microscopc operated at 400 kV with the crystals kept at -120°C in a Gatan 626 cryo-holder. The crystal thickness was measured using the local contrast of the crystal relative to the supporting film from search-mode images acquired using a 1024 x 1024 slow-scan CCD camera (model 679, Gatan Inc.).

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Skin Chromophore Estimation from Mobile Selfie Images using Constrained Independent Component Analysis

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-357 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 357-1-357-6

Author(s):

Luisa F. Polanía ◽

Raja Bala ◽

Ankur Purwar ◽

Paul Matts ◽

Martin Maltz

Keyword(s):

Independent Component Analysis ◽

Spectral Reflectance ◽

Source Separation ◽

Principal Component ◽

Component Analysis ◽

Previous Method ◽

Independent Component ◽

Light Transport ◽

Camera Model ◽

Constrained Independent Component Analysis

Human skin is made up of two primary chromophores: melanin, the pigment in the epidermis giving skin its color; and hemoglobin, the pigment in the red blood cells of the vascular network within the dermis. The relative concentrations of these chromophores provide a vital indicator for skin health and appearance. We present a technique to automatically estimate chromophore maps from RGB images of human faces captured with mobile devices such as smartphones. The ultimate goal is to provide a diagnostic aid for individuals to monitor and improve the quality of their facial skin. A previous method approaches the problem as one of blind source separation, and applies Independent Component Analysis (ICA) in camera RGB space to estimate the chromophores. We extend this technique in two important ways. First we observe that models for light transport in skin call for source separation to be performed in log spectral reflectance coordinates rather than in RGB. Thus we transform camera RGB to a spectral reflectance space prior to applying ICA. This process involves the use of a linear camera model and Principal Component Analysis to represent skin spectral reflectance as a lowdimensional manifold. The camera model requires knowledge of the incident illuminant, which we obtain via a novel technique that uses the human lip as a calibration object. Second, we address an inherent limitation with ICA that the ordering of the separated signals is random and ambiguous. We incorporate a domain-specific prior model for human chromophore spectra as a constraint in solving ICA. Results on a dataset of mobile camera images show high quality and unambiguous recovery of chromophores.

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Reliable Camera Model Identification Using Sparse Gaussian Processes

IEEE Signal Processing Letters ◽

10.1109/lsp.2021.3070206 ◽

2021 ◽

pp. 1-1

Author(s):

Benedikt Lorch ◽

Franziska Schirrmacher ◽

Anatol Maier ◽

Christian Riess

Keyword(s):

Gaussian Processes ◽

Model Identification ◽

Camera Model

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A study of co-occurrence based local features for camera model identification

Multimedia Tools and Applications ◽

10.1007/s11042-016-3663-0 ◽

2016 ◽

Vol 76 (4) ◽

pp. 4765-4781 ◽

Cited By ~ 21

Author(s):

Francesco Marra ◽

Giovanni Poggi ◽

Carlo Sansone ◽

Luisa Verdoliva

Keyword(s):

Model Identification ◽

Local Features ◽

Camera Model

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ESTIMATING EXTERIOR ORIENTATION PARAMETERS OF HYPERSPECTRAL BANDS BASED ON POLYNOMIAL MODELS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w3-19-2017 ◽

2017 ◽

Vol XLII-3/W3 ◽

pp. 19-25 ◽

Cited By ~ 1

Author(s):

A. Berveglieri ◽

A. M. G. Tommaselli ◽

E. Honkavaara

Keyword(s):

Real Data ◽

Bundle Adjustment ◽

Time Dependent ◽

Camera Model ◽

Exterior Orientation ◽

Spectral Bands ◽

Acquisition Mode ◽

Polynomial Models ◽

Orientation Parameters ◽

Sequential Acquisition

Hyperspectral camera operating in sequential acquisition mode produces spectral bands that are not recorded at the same instant, thus having different exterior orientation parameters (EOPs) for each band. The study presents experiments on bundle adjustment with time-dependent polynomial models for band orientation of hyperspectral cubes sequentially collected. The technique was applied to a Rikola camera model. The purpose was to investigate the behaviour of the estimated polynomial parameters and the feasibility of using a minimum of bands to estimate EOPs. Simulated and real data were produced for the analysis of parameters and accuracy in ground points. The tests considered conventional bundle adjustment and the polynomial models. The results showed that both techniques were comparable, indicating that the time-dependent polynomial model can be used to estimate the EOPs of all spectral bands, without requiring a bundle adjustment of each band. The accuracy of the block adjustment was analysed based on the discrepancy obtained from checkpoints. The root mean square error (RMSE) indicated an accuracy of 1&thinsp;GSD in planimetry and 1.5&thinsp;GSD in altimetry, when using a minimum of four bands per cube.

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IMAGE NETWORK GENERATION OF UNCALIBRATED UAV IMAGES WITH LOW-COST GPS DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b3-31-2016 ◽

2016 ◽

Vol XLI-B3 ◽

pp. 31-37 ◽

Cited By ~ 1

Author(s):

Shan Huang ◽

Zuxun Zhang ◽

Jianan He ◽

Tao Ke

Keyword(s):

Coordinate System ◽

Low Cost ◽

Reference Image ◽

Model Parameters ◽

Camera Model ◽

Orientation Data ◽

Network Generation ◽

Free Network ◽

Uav Images ◽

Image Network

The use of unmanned air vehicle (UAV) images acquired by a non-metric digital camera to establish an image network is difficult in cases without accurate camera model parameters. Although an image network can be generated by continuously calculating camera model parameters during data processing as an incremental structure from motion (SfM) methods, the process is time consuming. In this study, low-cost global position system (GPS) information is employed in image network generation to decrease computational expenses. Each image is considered as reference, and its neighbor images are determined based on GPS coordinates during processing. The reference image and its neighbor images constitute an image group, which is used to generate a free network through image matching and relative orientation. Data are then transformed from the free network coordinate system of each group into the GPS coordinate system by using the GPS coordinates of each image. After the exterior elements of each image are determined in the GPS coordinate system, the initial image network is established. Finally, self-calibration bundle adjustment constrained by GPS coordinates is conducted to refine the image network. The proposed method is validated on three fields. Results confirm that the method can achieve good image network when accurate camera model parameters are unavailable.

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