Florica Mindru

Moment invariants for recognition under changing viewpoint and illumination

Generalised color moments combine shape and color information and put them on an equal footing. Rational expressions of such moments can be designed, that are invariant under both geometric deformations and photometric changes. These generalised color moment invariants are effective features for recognition under changing viewpoint and illumination. The paper gives a systematic overview of such moment invariants for several combinations of deformations and photometric changes. Their validity and potential is corroborated through a series of experiments. Both the cases of indoor and outdoor images are considered, as illumination changes tend to differ between these circumstances. Although the generalised color moment invariants are extracted from planar surface patches, it is argued that invariant neighbourhoods offer a concept through which they can also be used to deal with 3D objects and scenes.

Recognizing color patterns irrespective of viewpoint and illumination

New invariant features are presented that can be used for the recognition of planar color patterns such as labels, logos, pictograms, etc., irrespective of the viewpoint or the illumination conditions, and without the need for error prone contour extraction. The new features are based on moments of powers of the intensities in the individual color bands and combinations thereof. These moments implicitly characterize the shape, the intensity and the color distribution of the pattern in a uniform manner. The paper gives a classification of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values of the color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of the new invariants for color pattern recognition is tested on a data set of images of outdoors advertising panels. A comparison to moment invariants presented in literature is included as well.

Model estimation for photometric changes of outdoor planar color surfaces caused by changes in illumination and viewpoint

We compare different ways of representing the global photometric changes in image intensities caused by changes in illumination and viewpoint, aiming at a balance between goodness-of-fit and low complexity. A series of model selection tests are performed for the case of outdoor imagery consisting of several views of several instances of billboards taken under different viewing angles and different illumination (natural light). Possible candidates for a transformation model on (R,G,B) color space are investigated and different approaches for the model selection problem are considered. The results are used within ongoing research into computation of new invariant features for planar color patterns, as the model choice is an important issue to decide on when extracting invariants. These results can be of benefit to other areas of research into color pattern or object recognition.

Color-Based Moment Invariants for Viewpoint and Illumination Independent Recognition of Planar Color Patterns

This paper contributes to the viewpoint and illumination independent recognition of planar color patterns such as labels, logos, signs, pictograms, etc. by means of moment invariants. It introduces the idea of using powers of the intensities in the different color bands of a color image and combinations thereof for the construction of the moments. First, a complete classification is made of all functions of such moments which are invariant under both affine deformations of the pattern (thus achieving viewpoint invariance) as well as linear changes of the intensity values in the individual color bands (hence, coping with changes in the irradiance pattern due to different lighting conditions and/or viewpoints). The discriminant power and classification performance of these new invariants for color pattern recogniti on has been tested on a data set consisting of images of real outdoors advertising panels. Furthermore, a comparison to moment invariants presented in literature ([1] and [2]) that come closest sto the aimed type of invariants is made and new approaches to improve their performance are presented.

Fully Unsupervised Clustering Using Center-Surround Receptive Fields with Applications to Color-Segmentation

In this paper we argue that the emphasis on similarity-matching within the context of Content-based Image Retrieval (CBIR) highlights the need for improved and reliable clustering-algorithms. We propose a fully unsupervised clustering algorithm that is obtained by changing the non-parametric density estimation problem in two ways. Firstly, we use cross-validation to select the appropriate width of the convolution-kernel. Secondly, using kernels with a positive centre and a negative surround (DOGS) allows for a better discrimination between clusters and frees us from having to choose an arbitrary cut-off thresh- old. No assumption about the underlying data-distribution is necessary and the algorithm can be applied in spaces of arbitrary dimension. As an illustration we have applied the algorithm to colour-segmentation problems.