Many digital image applications like digitization of cultural heritage for preservation purposes operate with compressed files in one or more image observing steps. For this kind of applications JPEG compression is one of the most widely used. Compression level, final file size and quality loss are parameters that must be managed optimally. Although this loss can be monitored by means of objective image quality measurements, the real challenge is to know how it can be related with the perceived image quality by observers. A pictorial image has been degraded by two different procedures. The first, applying different levels of low pass filtering by convolving the image with progressively broad Gauss kernels. The second, saving the original file to a series of JPEG compression levels. In both cases, the objective image quality measurement is done by analysis of the image power spectrum. In order to obtain a measure of the perceived image quality, both series of degraded images are displayed on a computer screen organized in random pairs. The observers are compelled to choose the best image of each pair. Finally, a ranking is established applying Thurstone scaling method. Results obtained by both measurements are compared between them and with other objective measurement method as the Slanted Edge Test.
KEYWORDS: Image processing, Cameras, Modulation transfer functions, Sensors, Cultural heritage, Photography, Image sensors, Color management, Light sources and illumination, Opacity
The digitization of both volumetric and flat objects is the nowadays-preferred method in order to preserve cultural heritage items. High quality digital files obtained from photographic plates, films and prints, paintings, drawings, gravures, fabrics and sculptures, allows not only for a wider diffusion and on line transmission, but also for the preservation of the original items from future handling. Early digitization procedures used scanners for flat opaque or translucent objects and camera only for volumetric or flat highly texturized materials. The technical obsolescence of the high-end scanners and the improvement achieved by professional cameras has result in a wide use of cameras with digital back to digitize any kind of cultural heritage item. Since the lens, the digital back, the software controlling the camera and the digital image processing provide a wide range of possibilities, there is necessary to standardize the methods used in the reproduction work leading to preserve as high as possible the original item properties. This work presents an overview about methods used for camera system characterization, as well as the best procedures in order to identify and counteract the effect of the lens residual aberrations, sensor aliasing, image illumination, color management and image optimization by means of parametric image processing. As a corollary, the work shows some examples of reproduction workflow applied to the digitization of valuable art pieces and glass plate photographic black and white negatives.
Translucent fabrics transmit light but provide sufficient diffusion to eliminate perception of distinct images. Traditional
translucent woven fabrics include organdie, silk chiffon and muslin cotton, among many others. When such materials are
used in curtains they provide even light transmission, solar protection and natural lighting, with a wide range of degrees
of view-through. Since all these properties can be referred to the concepts of privacy, space and boundary, they are
highly appreciated in interior design and architecture. The conventional metrics used to characterize a translucent fabric
are the UV or Visible light transmission, the cover factor and the shading coefficient. In this work we propose to use
other metrics commonly utilized to characterize imaging systems such as the Modulation Transfer Function (MTF).
When looking through a curtain, the translucent fabric can be modeled as a low-pass filter that is combined with human
eye imaging system. We replace our visual system by a high-quality still photographic camera. The MTF curve allows
one to characterize the view-through performance of the translucent fabric in a more realistic way than the simple light
transmittance, cover factor or shading coefficient. Two object tests, placed at a distance from the fabric, have been used
to experimentally derive the MTF of the whole imaging system (translucent fabric and camera): a USAF test and a
Slanted Edge (ES) test. In the latter case the Line Spread Function (LSF) is firstly obtained and the MTF estimated. The
method has been applied to a set of translucent fabrics with different thread diameters and densities. From the MTF
curves obtained using both tests, the transparency of the fabrics is objectively and quantitatively characterized in terms of
view-through. The results are presented and discussed.
Digitization of existing documents containing images is an important body of work for many archives ranging from
individuals to institutional organizations. The methods and file formats used in this digitization is usually a trade off
between budget, file volume size and image quality, while not necessarily in this order. The use of most commons and
standardized file formats, JPEG and TIFF, prompts the operator to decide the compression ratio that affects both the final
file volume size and the quality of the resulting image version. The evaluation of the image quality achieved by a system
can be done by means of several measures and methods, being the Modulation Transfer Function (MTF) one of most
used. The methods employed by the compression algorithms affect in a different way the two basic features of the image
contents, edges and textures. Those basic features are too differently affected by the amount of noise generated at the
digitization stage. Therefore, the target used in the measurement should be related with the features usually presents in
general imaging. This work presents a comparison between the results obtained by measuring the MTF of images taken
with a professional camera system and saved in several file formats compression ratios. In order to accomplish with the
needs early stated, the MTF measurement has been done by two separate methods using the slanted edge and dead leaves
targets respectively. The measurement results are shown and compared related with the respective file volume size.
Several professional photographic applications uses the merging of consecutive overlapping images in order to obtain
bigger files by means of stitching techniques or extended field of view (FOV) for panoramic images. All of those
applications share the fact that the final composed image is obtained by overlapping the neighboring areas of consecutive
individual images taken as a mosaic or a series of tiles over the scene, from the same point of view. Any individual
image taken with a given lens can carry residual aberrations and several of them will affect more probably the borders of
the image frame. Furthermore, the amount of distortion aberration present in the images of a given lens will be reversed
in position for the two overlapping areas of a pair of consecutive takings. Finally, the different images used in composing
the final one have corresponding overlapping areas taken with different perspective. From all the previously stated can
be derived that the software employed must remap all the pixel information in order to resize and match image features
in those overlapping areas, providing a final composed image with the desired perspective projection. The work
presented analyse two panoramic format images taken with a pair of lenses and composed by means of a state of the art
stitching software. Then, a series of images are taken to cover an FOV three times the original lens FOV, the images are
merged by means of a software of common use in professional panoramic photography and the final image quality is
evaluated through a series of targets positioned in strategic locations over the whole taking field of view. That allows
measuring the resulting Resolution and Modulation Transfer Function (MTF). The results are shown compared with the
previous measures on the original individual images.
The amount of images produced to be viewed as soft copies on output displays are significantly increasing. This
growing occurs at the expense of the images targeted to hard copy versions on paper or any other physical support. Even
in the case of high quality hard copy production, people working in professional imaging uses different displays in
selecting, editing, processing and showing images, from laptop screen to specialized high end displays. Then, the quality
performance of these devices is crucial in the chain of decisions to be taken in image production. Metrics of this quality
performance can help in the equipment acquisition. Different metrics and methods have been described to determine the
quality performance of CRT and LCD computer displays in clinical area. One of most important metrics in this field is
the device spatial frequency response obtained measuring the modulation transfer function (MTF). This work presents a
comparison between the MTF of three different LCD displays, Apple MacBook Pro 15", Apple LED Cinema Display
24" and Apple iPhone4, measured by the white noise stimulus method, over vertical and horizontal directions.
Additionally, different displays show particular pixels structure pattern. In order to identify this pixel structure, a set of
high magnification images is taken from each display to be related with the respective vertical and horizontal MTF.
An unsupervised novelty detection method for automatic flaw segmentation in textile materials that has no need of any
defect-free references or a training stage is presented in this paper. The algorithm is based on the structural feature
extraction of the weave repeat from the Fourier transform of the sample image. These features are used to define a set of
multiresolution bandpass filters adapted to the fabric structure that operate in the Fourier domain. Inverse Fourier
transformation, binarization and merging of the information obtained at different scales lead to the output image that
contains flaws segmented from the fabric background. The whole process is fully automatic and can be implemented
either optical or electronically.
Fabrics having a superstructure of colored squares, bands, etc. superimposed to the basic web structure can be
advantageously analyzed using NIR illumination and a camera sensitive to this region of the spectrum. The contrast
reduction of the superstructure signal in the NIR image facilitates fabric structure inspection and defect segmentation.
Underdetection and misdetection errors can be noticeably reduced in comparison with the inspection performed under
visible illumination. Experimental results are presented and discussed for a variety of fabrics and defects.
Recent advances in computer-generated images (CGI) have been used in commercial and industrial photography
providing a broad scope in product advertising. Mixing real world images with those rendered from virtual space
software shows a more or less visible mismatching between corresponding image quality performance. Rendered images
are produced by software which quality performance is only limited by the resolution output. Real world images are
taken with cameras with some amount of image degradation factors as lens residual aberrations, diffraction, sensor low
pass anti aliasing filters, color pattern demosaicing, etc. The effect of all those image quality degradation factors can be
characterized by the system Point Spread Function (PSF). Because the image is the convolution of the object by the
system PSF, its characterization shows the amount of image degradation added to any taken picture. This work explores
the use of image processing to degrade the rendered images following the parameters indicated by the real system PSF,
attempting to match both virtual and real world image qualities. The system MTF is determined by the slanted edge
method both in laboratory conditions and in the real picture environment in order to compare the influence of the
working conditions on the device performance; an approximation to the system PSF is derived from the two
measurements. The rendered images are filtered through a Gaussian filter obtained from the taking system PSF. Results
with and without filtering are shown and compared measuring the contrast achieved in different final image regions.
Surface defect detection is an important task of industrial inspection that has traditionally relied on trained human vision.
Automated and objective inspection methods based on image analysis have played a decisive role in the industrial
progress of the last decades. We propose a new unsupervised novelty detection method for defect segmentation in
textures. It uses a multiresolution Gabor filter scheme and shows the following properties: no need of any defect-free
references or a training stage; any adjustable parameters, and applicability to both random and periodic textures. We
apply the odd part of Gabor filters to the sample image, analyze the details obtained at different scales and orientations,
and extract a number of background texture features from the sample under inspection. In the analysis, we assume that
the wavelet coefficients of pixels can be suitably fitted by Gaussian mixtures, more specifically, by combining two
normal distributions. One of them would correspond to the background texture whereas the other would account for the
defective area. Since all the information is obtained from the sample image itself, the threshold selection is robust against
possible sample to sample fluctuations such as heterogeneities in the material, inplane positioning errors, scale variations
and lack of homogeneous illumination. The efficacy of the statistical analysis is demonstrated. The method is applied to
a variety of samples that exhibit either periodic or random texture. A comparison with other unsupervised method
designed for defect segmentation in periodic textures is done.
A digital image processing approach to develop an automatic method for the objective measure of some thread
parameters in a non closely-woven fabric is presented. The parameters addressed by this method are thickness,
periodicity, regularity, area of holes between threads and cover factor. Applied techniques range from image
thresholding for the segmentation of the threads from the background, to morphological operations such as
skeletonization and non-lineal filters. They are successfully applied to segment vertical and horizontal lines describing
the geometry of the threads. The mentioned parameters are derived from further labeling of the skeletonized images.
Current photographic still cameras, in the professional SLR step, are available in two basic sensor sizes, 16x24mm and
24x36mm and both of them can be used with the same or similar range of focal length lenses. Lens aperture determines
resolving power and diffraction effects and indeed, MTF function. In order to preserve an acceptable image quality level,
it must be taken into account that a high lens resolving power at larger apertures can be replicated by the sensor as a false
response or aliasing, while the size of the Airy disc must be related with photo receptors pitch. Provided that a
standardized metric of image quality is the system MTF, this work compares different lenses resolving power as a
function of aperture with the lens and system MTF; both aliasing and resolution affectations can be observed in the
system MTF. Lens resolving power has been measured by visual inspection of the aerial image of an USAF1951 test
target through a suitable microscope. The lens PSF and MTF has been measured by means of a Shack-Hartmann optical
wave front sensor. The system MTF is measured by the slanted edge method. The different experimental procedures
have been applied to two professional SLR cameras equipped with the same general use lens.
We present a new method to automatically segment local defects in a woven fabric that does not require any additional
defect-free reference for comparison. Firstly, the structural features of the repetition pattern of the minimal weave repeat
are extracted from the Fourier spectrum of the sample under inspection. The corresponding peaks are automatically
identified and removed from the fabric frequency spectrum. Secondly, we define a set of multi-scale oriented bandpass
filters, adapted to the specific structure of the sample, that operate in the Fourier domain. The filter design is the key part
of the method. Using the set of filters, local defects can be extracted. Thirdly, the filtered images obtained at different
scales are inverse Fourier transformed, binarized and merged to obtain an output image where flaws are segmented from
the fabric background. The method can be applied to fabrics of uniform color as well as to fabrics woven with threads of
different colors. It is Euclidean motion invariant and texture adaptive and it is useful for automatic inspection both online
and off-line. The whole process is fully automatic and can be implemented either optical or electronically. A variety
of experimental results are presented and discussed.
Some fabrics, either woven or knitted, show a fibrous, fluffy appearance that hides their thread interlacing structure. In such a case tasks related with inspection of the fabric structure, identification, classification and fault detection are difficult to carry out. A remarkable improvement can be obtained when the fabric image to evaluate is captured under near infrared (NIR) illumination. NIR illumination penetrates in the material more than visible illumination and the reflected image contains more information about its structure. Although humans are not sensitive in this region, we can observe a NIR image of a fabric by exploiting the residual sensitivity of a conventional monochrome camera that reaches up to 1000 nm. The light source used is an array of NIR LEDs emitting in a band to which the camera is still sensitive. This inexpensive image acquisition system is completed with a monochrome TV monitor to display the NIR image and a computer for image analysis. Some results obtained by applying Fourier analysis to the fabric image obtained under either visible or NIR illumination are provided and discussed.
Fabric cover factor is the ratio of the area covered by the yarns to the whole area of the fabric. In this work we propose a straightforward method to evaluate the fabric cover factor by automatic thresholding of a digital image of the fabric.
KEYWORDS: Near infrared, Inspection, Cameras, Image segmentation, Machine vision, Statistical analysis, Diffuse reflectance spectroscopy, Defect detection, Chemical analysis, Human vision and color perception
Fabric inspection is improved using near-infrared (NIR) illumination and a conventional monochrome camera. It takes advantage of the residual sensitivity of a monochrome camera that commonly reaches up to 1000 nm. Fabrics having a superstructure of colored squares, bands, etc. superimposed to the basic web structure can be advantageously analyzed because the contrast of the superstructure signal appears reduced under NIR illumination.
A new method to identify the weave repeat and the repetition pattern from the power spectrum of a woven fabric is presented. The results obtained from the analysis of some real samples are presented and discussed.
Various techniques based on image processing are presented for the automatic quality control of textiles. General defects (shrinking, abrasion, etc.) are detected by using operations in the frequency domain. Local defects (broken threads, mispicks, double yarns, etc.) are detected using a method based on a multiscale and multiorientation Gabor scheme that imitates the visual coding in early human vision. Also pilling resistance is automatically evaluated in wear-and-tear fabrics by a new algorithm which combines operations in both the spatial and frequency domain.
Gabor functions, which localize information in both the spatial and the frequency domains, are used as filters for the inspection of common local defects in textile webs. A variety of defects are analyzed in different fabrics and in every case the flaws are finally segmented from the background.
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