Hector C. Abril

Photon-counting double-random-phase encoding for secure image verification and retrieval

The integration of photon-counting imaging techniques and optical encryption systems can improve information authentication robustness against intruder attacks. Photon-counting imaging generates distributions with far fewer photons than conventional imaging and provides substantial bandwidth reduction by generating sparse encrypted data. We show that photon-limited encrypted distributions have sufficient information for successful decryption, authentication and signal retrieval. Additional compression of the encrypted distribution is applied by limiting the number of phase values used to reproduce the phase information of the complex-valued encrypted data. The validity of this technique—with and without phase compression—is probed through simulated experiments for two types of input images: alphanumerical signs and dithered natural scenes.

Automatic method based on image analysis for pilling evaluation in fabrics

A fully automatic method for pilling evaluation in wear-and- tear fabrics is developed from the image analysis of a set of standard photographs (Zweigle KG-741 Reutlingen). The method involves opera- tions in both the spatial and frequency domains to segment pills from the textured background of the web. It calculates the total area of pilling in the sample image and assigns a degree of pilling according to the stan- dard. Two mathematical descriptions are analyzed according to the un- derlying rule established by the standard images, using the visual esti- mation of the area of pilling performed by a group of observers. A logarithmic (in base two) approach, which is consistent with human vi- sual perception laws and facilitates an optimization of the method, is eventually adopted.

Inspection of fabric resistance to abrasion by Fourier analysis

A method based on the angular correlation of the Fourier spectra of fabric images is proposed to automatically evaluate web resistance to abrasion.

Influence of the wrinkle perception with distance in the objective evaluation of fabric smoothness

We propose a method of fabric surface imaging and processing so that information about wrinkles can be extracted and evaluated. We consider two images of the sample obtained under orthogonal lateral illumination and apply a joint Canny edge detector to integrate the information about wrinkles of both images. The smoothed and scaled perception of wrinkle edges with the viewing distance and its influence in the evaluation of the fabric appearance is simulated, analyzed and proposed for a more realistic assessment of a fabric.

Pilling evaluation in fabrics by digital image processing

One problem in the quality control of textiles is the measure of the pilling degree in worn fabrics. Wear and tear make some fibers separate from the woven yarns and get entangled in pills distributed on the web. Pilling degree is often evaluated by experts from a visual comparison with standard images. In this work, we use some techniques of digital image processing to evaluate the pilling degree. From the analysis of a set of standard images we establish and develop a sequential method for an objective measurement. The method involves operations in the frequency domain as well as in the spatial domain. In the final processed images we segment pills from the background fabric and we measure the total area of pilling for each image. We have verified that there is a logarithmic relationship between the total pilling area and the degree of pilling.

Image segmentation based on a Gaussian model applied to pilling evaluation in fabrics

Wear and tear generate fluffiness and pills that remain in the web surface spoiling the appearance of a fabric. In quality control of textiles it is necessary to have an objective method to measure pilling that improves current methods based on visual estimations of the degree of pilling. In this work we optimize a method for piling evaluation based on image analysis that we proposed recently. The method combined operations in both the frequency and the spatial domains in order to better segment pills from the textured web background. We considered a logarithmic in base two relationship between the area of pilling and the degree of pilling based on the human perception mechanisms.

Anisotropy-based robust focus measure for non-mydriatic retinal imaging.

Non-mydriatic retinal imaging is an important tool for diagnosis and progression assessment of ophthalmic diseases. Because it does not require pharmacological dilation of the patients pupil, it is essential for screening programs performed by non-medical personnel. A typical camera is equipped with a manual focusing mechanism to compensate for the refractive errors in the eye. However, manual focusing is error prone, especially when performed by inexperienced photographers. In this work, we propose a new and robust focus measure based on a calculation of image anisotropy which, in turn, is evaluated from the directional variance of the normalized discrete cosine transform. Simulation and experimental results demonstrate the effectiveness of the proposed focus measure.

No-reference quality metrics for eye fundus imaging

This paper presents a comparative study on the use of noreference quality metrics for eye fundus imaging. We center on autofocusing and quality assessment as key applications for the correct operation of a fundus imaging system. Four state-of-the-art no-reference metrics were selected for the study. From these, a metric based of Renyi anisotropy yielded the best performance in both auto-focusing and quality assessment.

Image processing of standard grading scales for objective assessment of contact lens wear complications

Ocular complications in contact lens wearers are usually graded by specialists using visual inspection and comparing with established standards. The standard grading scales consist of either a set of illustrations or photographs ordered from a normal situation to a severe complication. In this work, an objective assessment of contact lens wear complications is intended by applying different image processing techniques to two standard grading scales (Efron and CCLRU grading scales). In particular, conjunctival hyperemia and papillary conjunctivitis are considered. Given a set of standard illustrations or pictures for each considered ocular disorder, image preprocessing is needed to compare equivalent areas. Histogram analysis allows segmenting vessel and background pixel populations, which are used to determine the most relevant features in the measurement of contact lens effects. Features such as color, total area of vessels and vessel length are used to evaluate bulbar and lid redness. The procedure to obtain an automatic grading method by digital image analysis of standard grading scales is described.

Automatic quality control of textile webs by image processing

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.