Integral Channel Features

We study the performance of ‘integral channel features’ for image classification tasks, focusing in particular on pedestrian detection. The general idea behind integral channel features is that multiple registered image channels are computed using linear and non-linear transformations of the input image, and then features such as local sums, histograms, and Haar features and their various generalizations are efficiently computed using integral images. Such features have been used in recent literature for a variety of tasks – indeed, variations appear to have been invented independently multiple times. Although integral channel features have proven effective, little effort has been devoted to analyzing or optimizing the features themselves. In this work we present a unified view of the relevant work in this area and perform a detailed experimental evaluation. We demonstrate that when designed properly, integral channel features not only outperform other features including histogram of oriented gradient (HOG), they also (1) naturally integrate heterogeneous sources of information, (2) have few parameters and are insensitive to exact parameter settings, (3) allow for more accurate spatial localization during detection, and (4) result in fast detectors when coupled with cascade classifiers

Fast anisotropic Gauss filtering

Abstract. We derive the decomposition of the anisotropic Gaussian in a one dimensional Gauss filter in the x-direction followed by a one dimensional filter in a non-orthogonal direction ϕ. So also the anisotropic Gaussian can be decomposed by dimension. This appears to be extremely efficient from a computing perspective. An implementation scheme for normal convolution and for recursive filtering is proposed. Also directed derivative filters are demonstrated. For the recursive implementation, filtering an 512 × 512 image is performed within 65 msec, independent of the standard deviations and orientation of the filter. Accuracy of the filters is still reasonable when compared to truncation error or recursive approximation error. The anisotropic Gaussian filtering method allows fast calculation of edge and ridge maps, with high spatial and angular accuracy. For tracking applications, the normal anisotropic convolution scheme is more advantageous, with applications in the detection of dashed lines in engineering drawings. The recursive implementation is more attractive in feature detection applications, for instance in affine invariant edge and ridge detection in computer vision. The proposed computational filtering method enables the practical applicability of orientation scale-space analysis

The Atlas Structure of Images

Many operations of vision require image regions to be isolated and inter-related. This is challenging when they are different in detail and extent. Practical methods of Computer Vision approach this through the tools of downsampling, pyramids, cropping and patches. In this paper we develop an ideal geometric structure for this, compatible with the existing scale space model of image measurement. Its elements are apertures which view the image like fuzzy-edged portholes of frosted glass. We establish containment and cause/effect relations between apertures, and show that these link them into cross-scale atlases. Atlases formed of Gaussian apertures are shown to be a continuous version of the image pyramid used in Computer Vision, and allow various types of image description to naturally be expressed within their framework. We show that views through Gaussian apertures are approximately equivalent to the jets of derivative of Gaussian filter responses that form part of standard Scale Space theory. This supports a view of the simple cells of mammalian V1 as implementing a system of local views of the retinal image of varying extent and resolution. As a worked example we develop a keypoint descriptor scheme that outperforms previous schemes that do not make use of learning

Invariance of visual operations at the level of receptive fields

Receptive field profiles registered by cell recordings have shown that mammalian vision has developed receptive fields tuned to different sizes and orientations in the image domain as well as to different image velocities in space-time. This article presents a theoretical model by which families of idealized receptive field profiles can be derived mathematically from a small set of basic assumptions that correspond to structural properties of the environment. The article also presents a theory for how basic invariance properties to variations in scale, viewing direction and relative motion can be obtained from the output of such receptive fields, using complementary selection mechanisms that operate over the output of families of receptive fields tuned to different parameters. Thereby, the theory shows how basic invariance properties of a visual system can be obtained already at the level of receptive fields, and we can explain the different shapes of receptive field profiles found in biological vision from a requirement that the visual system should be invariant to the natural types of image transformations that occur in its environment.Comment: 40 pages, 17 figure

Shape and Perspective Extraction in Artificial Textures and Natural Scenes by Cortical Models

In this work we present a new shape from texture algorithm applied to natural scenes analysis. The originality of this approach is based on the modeling of the structure of the primary visual cortex (V1). The algorithm is able to deal with a large variety of textures presenting different types of irregularities. First to sample the amplitude spectra, we present new filters, called log-normal filters, inspired from the complex cells of V1, in replacement of the classical Gabor filters. These filters appear to be suitable for pattern analysis techniques due to their different theoretical properties, notably their radial frequency profile (adapted to the 1/f frequency profile of natural scenes) and their separability in orientation and frequency. We then use an estimation method of the local mean frequency applied to natural signals. This one does not imply the search for the adapted scale for the analysis and takes advantage of the frequencies of the used bank of filters. Finally, from a local estimation, the orientation and shape are extracted using the geometrical properties of the perspective projection. The precision of the method is evaluated on different types of textures, both regular and irregular, and on natural scenes. The presented method allows to obtain favorably comparable results to existing best known methods with a low computational cost. Finally the model can be adapted to other applications like texture analysis, characteristic points extraction or content-based image indexation.Dans ce travail nous présentons un nouvel algorithme d’extraction de la forme par la texture appliqué à l’analyse des scènes naturelles. L’originalité de cette approche est basée sur la structure du cortex visuel primaire (V1) dont elle modélise les fonctions. L’algorithme est capable d’analyser une grande variété de textures présentant différents types d’irrégularités. Tout d’abord pour réaliser l’échantillonnage du spectre d’amplitude, nous proposons de nouveaux filtres, appelés filtres log-normaux, inspirés du fonctionnement des cellules complexes de l’aire V1, en remplacement des filtres de Gabor classiques. Ces filtres s’avèrent particulièrement appropriés aux techniques de reconnaissance de forme de part leurs différentes propriétés théoriques, notamment leur profil en fréquence radiale (adapté à la décroissance en 1/f des scènes naturelles) et leur séparabilité en orientation et en fréquence. Nous utilisons ensuite une méthode d’estimation de la fréquence moyenne locale appliquées sur des signaux naturels. Celle-ci ne nécessite pas la recherche d’une échelle adaptée à l’analyse et tire avantage de l’ensemble des fréquences du banc de filtres utilisé. Finalement, à partir de l’estimation locale, l’orientation et la forme sont extraits en utilisant les propriétés géométriques de la projection perspective. La précision de la méthode est évaluée sur différents types de textures, à la fois régulières et irrégulières, et sur des scènes naturelles. La méthode présentée permet d’obtenir des résultats se comparant favorablement aux meilleures techniques existantes tout en conservant un faible coût de calcul. Enfin le modèle peut être adapté à d’autres applications telles que l’analyse de textures, l’extraction de points caractéristiques ou l’indexation d’images par le contenu