Evaluating color texture descriptors under large variations of controlled lighting conditions

The recognition of color texture under varying lighting conditions is still an open issue. Several features have been proposed for this purpose, ranging from traditional statistical descriptors to features extracted with neural networks. Still, it is not completely clear under what circumstances a feature performs better than the others. In this paper we report an extensive comparison of old and new texture features, with and without a color normalization step, with a particular focus on how they are affected by small and large variation in the lighting conditions. The evaluation is performed on a new texture database including 68 samples of raw food acquired under 46 conditions that present single and combined variations of light color, direction and intensity. The database allows to systematically investigate the robustness of texture descriptors across a large range of variations of imaging conditions.Comment: Submitted to the Journal of the Optical Society of America

Motion of glossy objects does not promote separation of lighting and surface colour

The surface properties of an object, such as texture, glossiness or colour, provide important cues to its identity. However, the actual visual stimulus received by the eye is determined by both the properties of the object and the illumination. We tested whether operational colour constancy for glossy objects (the ability to distinguish changes in spectral reflectance of the object, from changes in the spectrum of the illumination) was affected by rotational motion of either the object or the light source. The different chromatic and geometric properties of the specular and diffuse reflections provide the basis for this discrimination, and we systematically varied specularity to control the available information. Observers viewed animations of isolated objects undergoing either lighting or surface-based spectral transformations accompanied by motion. By varying the axis of rotation, and surface patterning or geometry, we manipulated: (i) motion-related information about the scene, (ii) relative motion between the surface patterning and the specular reflection of the lighting, and (iii) image disruption caused by this motion. Despite large individual differences in performance with static stimuli, motion manipulations neither improved nor degraded performance. As motion significantly disrupts frameby-frame low-level image statistics, we infer that operational constancy depends on a high-level scene interpretation, which is maintained in all condition

Perceptual-based textures for scene labeling: a bottom-up and a top-down approach

Due to the semantic gap, the automatic interpretation of digital images is a very challenging task. Both the segmentation and classification are intricate because of the high variation of the data. Therefore, the application of appropriate features is of utter importance. This paper presents biologically inspired texture features for material classification and interpreting outdoor scenery images. Experiments show that the presented texture features obtain the best classification results for material recognition compared to other well-known texture features, with an average classification rate of 93.0%. For scene analysis, both a bottom-up and top-down strategy are employed to bridge the semantic gap. At first, images are segmented into regions based on the perceptual texture and next, a semantic label is calculated for these regions. Since this emerging interpretation is still error prone, domain knowledge is ingested to achieve a more accurate description of the depicted scene. By applying both strategies, 91.9% of the pixels from outdoor scenery images obtained a correct label

A PCA approach to the object constancy for faces using view-based models of the face

The analysis of object and face recognition by humans attracts a great deal of interest, mainly because of its many applications in various fields, including psychology, security, computer technology, medicine and computer graphics. The aim of this work is to investigate whether a PCA-based mapping approach can offer a new perspective on models of object constancy for faces in human vision. An existing system for facial motion capture and animation developed for performance-driven animation of avatars is adapted, improved and repurposed to study face representation in the context of viewpoint and lighting invariance. The main goal of the thesis is to develop and evaluate a new approach to viewpoint invariance that is view-based and allows mapping of facial variation between different views to construct a multi-view representation of the face. The thesis describes a computer implementation of a model that uses PCA to generate example- based models of the face. The work explores the joint encoding of expression and viewpoint using PCA and the mapping between viewspecific PCA spaces. The simultaneous, synchronised video recording of 6 views of the face was used to construct multi-view representations, which helped to investigate how well multiple views could be recovered from a single view via the content addressable memory property of PCA. A similar approach was taken to lighting invariance. Finally, the possibility of constructing a multi-view representation from asynchronous view-based data was explored. The results of this thesis have implications for a continuing research problem in computer vision – the problem of recognising faces and objects from different perspectives and in different lighting. It also provides a new approach to understanding viewpoint invariance and lighting invariance in human observers

The role of chromatic texture and 3D shape in colour discrimination, memory colour, and colour constancy of natural objects

The primary goal of this work was to investigate colour perception in a natural environment and to contribute to the understanding of how cues to familiar object identity influence colour appearance. A large number of studies on colour appearance employ 2D uniformly coloured patches, discarding perceptual cues such as binocular disparity, 3D luminance shading, mutual reflection, and glossy highlights are integral part of a natural scene. Moreover, natural objects possess specific cues that help our recognition (shape, surface texture or colour distribution). The aim of the first main experiment presented in this thesis was to understand the effect of shape on (1) memory colour under constant and varying illumination and on (2) colour constancy for uniformly coloured stimuli. The results demonstrated the existence of a range of memory colours associated with a familiar object, the size of which was strongly object-shape-dependent. For all objects, memory retrieval was significantly faster for object-diagnostic shape relative to generic shapes. Based on two successive controls, the author suggests that shape cues to the object identity affect the range of memory colour proportionally to the original object chromatic distribution. The second experiment examined the subject’s accuracy and precision in adjusting a stimulus colour to its typical appearance. Independently on the illuminant, results showed that memory colour accuracy and precision were enhanced by the presence of chromatic textures, diagnostic shapes, or 3D configurations with a strong interaction between diagnosticity and dimensionality of the shape. Hence, more cues to the object identity and more natural stimuli facilitate the observers in accessing their colour information from memory. A direct relationship was demonstrated between chromatic surface representation, object’s physical properties, and identificability and dimensionality of shape on memory colour accuracy, suggesting high-level mechanisms. Chromatic textures facilitated colour constancy. The third and fourth experiments tested the subject’s ability to discriminate between two chromatic stimuli in a simultaneous and successive 2AFC task, respectively. Simultaneous discrimination threshold performances for polychromatic surfaces were only due to low-level mechanism of the stimulus, whereas in the successive discrimination, i.e. when memory is involved, high-level mechanisms were established. The effect of shape was strongly task- dependent and was modulate by the object memory colour. These findings together with the strong interaction between chromatic cues and shape cues to the object identity lead to the conclusion that high level mechanisms linked to object recognition facilitated both tasks. Hence, the current thesis presents new findings on memory colour and colour constancy presented in a natural context and demonstrates the effect of high-level mechanisms in chromatic discrimination as a function of cues to the object identity such as shape and texture. This work contributes to a deeper understanding of colour perception and object recognition in the natural world.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Variable illumination and invariant features for detecting and classifying varnish defects

This work presents a method to detect and classify varnish defects on wood surfaces. Since these defects are only partially visible under certain illumination directions, one image doesn\u27t provide enough information for a recognition task. A classification requires inspecting the surface under different illumination directions, which results in image series. The information is distributed along this series and can be extracted by merging the knowledge about the defect shape and light direction