Gertjan J. Burghouts

The Amsterdam Library of Object Images

We present the ALOI collection of 1,000 objects recorded under various imaging circumstances. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. These images are made publicly available for scientific research purposes.

Performance evaluation of local colour invariants

In this paper, we compare local colour descriptors to grey-value descriptors. We adopt the evaluation framework of Mikolayzcyk and Schmid. We modify the framework in several ways. We decompose the evaluation framework to the level of local grey-value invariants on which common region descriptors are based. We compare the discriminative power and invariance of grey-value invariants to that of colour invariants. In addition, we evaluate the invariance of colour descriptors to photometric events such as shadow and highlights. We measure the performance over an extended range of common recording conditions including significant photometric variation. We demonstrate the intensity-normalized colour invariants and the shadow invariants to be highly distinctive, while the shadow invariants are more robust to both changes of the illumination colour, and to changes of the shading and shadows. Overall, the shadow invariants perform best: they are most robust to various imaging conditions while maintaining discriminative power. When plugged into the SIFT descriptor, they show to outperform other methods that have combined colour information and SIFT. The usefulness of C-colour-SIFT for realistic computer vision applications is illustrated for the classification of object categories from the VOC challenge, for which a significant improvement is reported.

Color invariant object recognition using entropic graphs

We present an object recognition approach using higher‐order color invariant features with an entropy‐based similarity measure. Entropic graphs offer an unparameterized alternative to common entropy estimation techniques, such as a histogram or assuming a probability distribution. An entropic graph estimates entropy from a spanning graph structure of sample data. We extract color invariant features from object images invariant to illumination changes in intensity, viewpoint, and shading. The Henze–Penrose similarity measure is used to estimate the similarity of two images. Our method is evaluated on the ALOI collection, a large collection of object images. This object image collection consists of 1000 objects recorded under various imaging circumstances. The proposed method is shown to be effective under a wide variety of imaging conditions.

Quasi-periodic spatiotemporal filtering

This paper presents the online estimation of temporal frequency to simultaneously detect and identify the quasiperiodic motion of an object. We introduce color to increase discriminative power of a reoccurring object and to provide robustness to appearance changes due to illumination changes. Spatial contextual information is incorporated by considering the object motion at different scales. We combined spatiospectral Gaussian filters and a temporal reparameterized Gabor filter to construct the online temporal frequency filter. We demonstrate the online filter to respond faster and decay faster than offline Gabor filters. Further, we show the online filter to be more selective to the tuned frequency than Gabor filters. We contribute to temporal frequency analysis in that we both identify (what) and detect (when) the frequency. In color video, we demonstrate the filter to detect and identify the periodicity of natural motion. The velocity of moving gratings is determined in a real world example. We consider periodic and quasiperiodic motion of both stationary and nonstationary objects.