Automatically Evolving Rotation-invariant Texture Image Descriptors by Genetic Programming

In computer vision, training a model that performs classification effectively is highly dependent on the extracted features, and the number of training instances. Conventionally, feature detection and extraction are performed by a domain-expert who, in many cases, is expensive to employ and hard to find. Therefore, image descriptors have emerged to automate these tasks. However, designing an image descriptor still requires domain-expert intervention. Moreover, the majority of machine learning algorithms require a large number of training examples to perform well. However, labelled data is not always available or easy to acquire, and dealing with a large dataset can dramatically slow down the training process. In this paper, we propose a novel Genetic Programming based method that automatically synthesises a descriptor using only two training instances per class. The proposed method combines arithmetic operators to evolve a model that takes an image and generates a feature vector. The performance of the proposed method is assessed using six datasets for texture classification with different degrees of rotation, and is compared with seven domain-expert designed descriptors. The results show that the proposed method is robust to rotation, and has significantly outperformed, or achieved a comparable performance to, the baseline methods.

Hair as a Biomarker for Manganese Exposure Among Welders

Thesis (Ph.D.)--University of Washington, 2016-08

Effet de réduction en sodium sur la texture et la bioaccessibilité des protéines d’un fromage à pâte molle à croûte fleurie

Le sel (NaCl) joue plusieurs rôles importants dans les fromages aux niveaux technologique, microbiologique et organoleptique. Cependant, le sodium, un facteur de risque des maladies cardiovasculaires, est consommé en trop grande quantité par les Canadiens. Comme la réduction du sodium pourrait affecter la texture des fromages à pâte molle à croûte fleurie et la libération des nutriments pendant la digestion, l’impact de la réduction du sodium sur la bioaccessibilité des protéines dans le fromage Brie a été étudié. Ainsi, la composition et les caractéristiques texturales de fromages Brie ayant différentes teneurs en sodium (standard, réduite, substituée au KCl) ont été analysées. Une réduction du sodium de 23 % a été obtenue pour les fromages réduits en sodium. Le temps d’affinage affecte la protéolyse et la texture des fromages. La cinétique de dégradation de la matrice fromagère et la libération des protéines ont été déterminées par une approche de digestion in vitro. La désintégration de la matrice fromagère et la libération des protéines pendant la digestion in vitro ne sont pas différentes entre les fromages expérimentaux. Ces travaux démontrent qu’il est possible de réduire le sodium sans affecter le profil de protéolyse et la texture pendant l’affinage du fromage Brie ainsi que son comportement à la digestion.

ISA Training: Hair and beauty trainees take advantage of technology

ISA Training based in south Wales is taking steps to upskill its staff and learners in digital literacies in order to make most effective use of technology in delivery and assessment of courses in hairdressing, beauty therapy and business management. To increase the flexibility and efficiency of its provision, the company has adopted the Learning Assistant e-portfolio system.

Combining Leaf Shape and Texture for Costa Rican Plant Species Identification

In the last decade, research in Computer Vision has developed several algorithms to help botanists and non-experts to classify plants based on images of their leaves. LeafSnap is a mobile application that uses a multiscale curvature model of the leaf margin to classify leaf images into species. It has achieved high levels of accuracy on 184 tree species from Northeast US. We extend the research that led to the development of LeafSnap along two lines. First, LeafSnap’s underlying algorithms are applied to a set of 66 tree species from Costa Rica. Then, texture is used as an additional criterion to measure the level of improvement achieved in the automatic identification of Costa Rica tree species. A 25.6% improvement was achieved for a Costa Rican clean image dataset and 42.5% for a Costa Rican noisy image dataset. In both cases, our results show this increment as statistically significant. Further statistical analysis of visual noise impact, best algorithm combinations per species, and best value of , the minimal cardinality of the set of candidate species that the tested algorithms render as best matches is also presented in this research

Automatic Glaucoma Detection Based on Optic Disc Segmentation and Texture Feature Extraction

The use of digital image processing techniques is prominent in medical settings for the automatic diagnosis of diseases. Glaucoma is the second leading cause of blindness in the world and it has no cure. Currently, there are treatments to prevent vision loss, but the disease must be detected in the early stages. Thus, the objective of this work is to develop an automatic detection method of Glaucoma in retinal images. The methodology used in the study were: acquisition of image database, Optic Disc segmentation, texture feature extraction in different color models and classification of images in glaucomatous or not. We obtained results of 93% accuracy

A new inhight on the Portuguese traditional sweets as cultural identity: a scientific approach towards evaluation of texture in Castanhas de Ovos de Viseu

The importance of the gastronomic heritage of any people is unquestionable and Portugal is particularly rich when it comes to sweets. Hence, the objective of this work was to give a contribution colour and texture, due to their importance for consumer acceptance. The samples were bought from a pastry in Viseu and produced according to the traditional recipe. Colour evaluation was made by a colorimeter and textural analysis by a texturometer. The results obtained allowed concluding that there were differences between the top burned area and the non-burned areas, as well as when comparing the ruffled with unruffled samples either regarding colour and also texture. The burned area was darker, with a more intense red and less yellow. As to texture, the ruffles samples were harder than the unruffled samples.

Texture and composition of the Rosa Marina beach sands (Adriatic coast, southern Italy): a sedimentological/ecological approach

Beach sands from the Rosa Marina locality (Adriatic coast, southern Italy) were analysed mainly microscopically in order to trace the source areas of their lithoclastic and bioclastic components. The main cropping out sedimentary units were also studied with the objective to identify the potential source areas of lithoclasts. This allowed to establish how the various rock units contribute to the formation of beach sands. The analysis of the bioclastic components allows to estimate the actual role of organisms regarding the supply of this material to the beach. Identification of taxa that are present in the beach sands as shell fragments or other remains was carried out at the genus or family level. Ecologi- cal investigation of the same beach and the recognition of sub-environments (mainly distinguished on the basis of the nature of the substrate and of the water depth) was the key topic that allowed to establish the actual source areas of bioclasts in the Rosa Marina beach sands. The sedimentological analysis (including a physical study of the beach and the calculation of some statistical parameters concerning the grain-size curves) shows that the Rosa Marina beach is nowadays subject to erosion.

Texture-Detail Preservation Measurement in Camera Phones: An Updated Approach

Recent advances in mobile phone cameras have poised them to take over compact hand-held cameras as the consumer’s preferred camera option. Along with advances in the number of pixels, motion blur removal, face-tracking, and noise reduction algorithms have significant roles in the internal processing of the devices. An undesired effect of severe noise reduction is the loss of texture (i.e. low-contrast fine details) of the original scene. Current established methods for resolution measurement fail to accurately portray the texture loss incurred in a camera system. The development of an accurate objective method to identify the texture preservation or texture reproduction capability of a camera device is important in this regard. The ‘Dead Leaves’ target has been used extensively as a method to measure the modulation transfer function (MTF) of cameras that employ highly non-linear noise-reduction methods. This stochastic model consists of a series of overlapping circles with radii r distributed as r−3, and having uniformly distributed gray level, which gives an accurate model of occlusion in a natural setting and hence mimics a natural scene. This target can be used to model the texture transfer through a camera system when a natural scene is captured. In the first part of our study we identify various factors that affect the MTF measured using the ‘Dead Leaves’ chart. These include variations in illumination, distance, exposure time and ISO sensitivity among others. We discuss the main differences of this method with the existing resolution measurement techniques and identify the advantages. In the second part of this study, we propose an improvement to the current texture MTF measurement algorithm. High frequency residual noise in the processed image contains the same frequency content as fine texture detail, and is sometimes reported as such, thereby leading to inaccurate results. A wavelet thresholding based denoising technique is utilized for modeling the noise present in the final captured image. This updated noise model is then used for calculating an accurate texture MTF. We present comparative results for both algorithms under various image capture conditions.

Efficient representation of texture details in medical images by fusion of Ripplet and DDCT transformed images

Purpose: To evaluate and compare the performance of Ripplet Type-1 transform and directional discrete cosine transform (DDCT) and their combinations for improved representation of MRI images while preserving its fine features such as edges along the smooth curves and textures. Methods: In a novel image representation method based on fusion of Ripplet type-1 and conventional/directional DCT transforms, source images were enhanced in terms of visual quality using Ripplet and DDCT and their various combinations. The enhancement achieved was quantified on the basis of peak signal to noise ratio (PSNR), mean square error (MSE), structural content (SC), average difference (AD), maximum difference (MD), normalized cross correlation (NCC), and normalized absolute error (NAE). To determine the attributes of both transforms, these transforms were combined to represent the entire image as well. All the possible combinations were tested to present a complete study of combinations of the transforms and the contrasts were evaluated amongst all the combinations. Results: While using the direct combining method (DDCT) first and then the Ripplet method, a PSNR value of 32.3512 was obtained which is comparatively higher than the PSNR values of the other combinations. This novel designed technique gives PSNR value approximately equal to the PSNR’s of parent techniques. Along with this, it was able to preserve edge information, texture information and various other directional image features. The fusion of DDCT followed by the Ripplet reproduced the best images. Conclusion: The transformation of images using Ripplet followed by DDCT ensures a more efficient method for the representation of images with preservation of its fine details like edges and textures.