Color Based Object Recognition

We aim at combining color and shape invariants for indexing and retrieving images. To this end, color models are proposed independent of the object geometry, object pose, and illumination. From these color models, color invariant edges are derived from which shape invariant features are computed. Computational methods are described to combine the color and shape invariants into a unified high-dimensional invariant feature set for discriminatory object retrieval. Experiments have been conducted on a database consisting of 500 images taken from multicolored man-made objects in real world scenes. From the theoretical and experimental results it is concluded that object retrieval based on composite color and shape invariant features provides excellent retrieval accuracy. Object retrieval based on color invariants provides very high retrieval accuracy whereas object retrieval based entirely on shape invariants yields poor discriminative power. Furthermore, the image retrieval scheme is highly robust to partial occlusion, object clutter and a change in the object's pose. Finally, the image retrieval scheme is integrated into the PicToSeek system on-line at http://www.wins.uva.nl/research/isis/PicToSeek/ for searching images on the World Wide Web.

/pdf/pictoseek-combining-color-and-shape-invariant-features-for-1zfn2c1bcu.pdf

PicToSeek: combining color and shape invariant features for image retrieval

The appearance of an object is composed of local structure. This local structure can be described and characterized by a vector of local features measured by local operators such as Gaussian derivatives or Gabor filters. This article presents a technique where appearances of objects are represented by the joint statistics of such local neighborhood operators. As such, this represents a new class of appearance based techniques for computer vision. Based on joint statistics, the paper develops techniques for the identification of multiple objects at arbitrary positions and orientations in a cluttered scene. Experiments show that these techniques can identify over 100 objects in the presence of major occlusions. Most remarkably, the techniques have low complexity and therefore run in real-time.

/pdf/recognition-without-correspondence-using-2vbxgrlyg5.pdf

Recognition without Correspondence using MultidimensionalReceptive Field Histograms

We revisit the problem of specific object recognition using color distributions. In some applications-such as specific person identification-it is highly likely that the color distributions will be multimodal and hence contain a special structure. Although the color distribution changes under different lighting conditions, some aspects of its structure turn out to be invariants. We refer to this structure as an intradistribution structure, and show that it is invariant under a wide range of imaging conditions while being discriminative enough to be practical. Our signature uses shape context descriptors to represent the intradistribution structure. Assuming the widely used diagonal model, we validate that our signature is invariant under certain illumination changes. Experimentally, we use color information as the only cue to obtain good recognition performance on publicly available databases covering both indoor and outdoor conditions. Combining our approach with the complementary covariance descriptor, we demonstrate results exceeding the state-of-the-art performance on the challenging VIPeR and CAVIAR4REID databases.

/pdf/color-invariants-for-person-reidentification-4zzsm04o63.pdf

Color Invariants for Person Reidentification

Hyperspectral cameras provide useful discriminants for human face recognition that cannot be obtained by other imaging methods. We examine the utility of using near-infrared hyperspectral images for the recognition of faces over a database of 200 subjects. The hyperspectral images were collected using a CCD camera equipped with a liquid crystal tunable filter to provide 31 bands over the near-infrared (0.7 /spl mu/m-1.0 /spl mu/m). Spectral measurements over the near-infrared allow the sensing of subsurface tissue structure which is significantly different from person to person, but relatively stable over time. The local spectral properties of human tissue are nearly invariant to face orientation and expression which allows hyperspectral discriminants to be used for recognition over a large range of poses and expressions. We describe a face recognition algorithm that exploits spectral measurements for multiple facial tissue types. We demonstrate experimentally that this algorithm can be used to recognize faces over time in the presence of changes in facial pose and expression.

/pdf/face-recognition-in-hyperspectral-images-3fdp0r61uc.pdf

Face recognition in hyperspectral images

Color pixel distributions provide a useful cue for object recognition but are dependent on scene illumination. We develop an algorithm that assigns color descriptors to an object that depend on the surface properties of the object and not on the illumination. An object is defined by a set of possibly textured surfaces and gives rise to a color pixel distribution. For a trichromatic system, the algorithm assumes a three-dimensional linear model for surface spectral reflectance. There are no assumptions about the contents of the scene and only weak constraints on the illumination. The global color invariants can be computed in an amount of time that is proportional to the number of pixels that define an object. A set of experiments on complex scenes under various illuminants demonstrates that the global color constancy algorithm performs significantly better than previous recognition algorithms based on color distribution.

Global color constancy: recognition of objects by use of illumination-invariant properties of color distributions

The spectral properties of outdoor illumination functions can vary significantly, owing to atmospheric conditions and scene geometry. Using a statistical analysis of a comprehensive physical model, we show that the variation in outdoor illumination functions over both the visible range (0.33–0.7 µm) and the visible/near-infrared range (0.4–2.5 µm) can be represented accurately by use of seven-dimensional linear models. The physical model includes solar and scattered radiation as well as the effects of atmospheric gases and aerosols. The modtran 3.5 code was employed for computing radiative transfer aspects of the model. We show that the new model has strong agreement over the visible wavelengths with the empirical study of Judd
	 [J. Opt. Soc. Am.54, 1031 (1964)]. We also demonstrate the accuracy of the model over the 0.4–2.5-µm spectral range, using measured outdoor illumination functions.

Analyzing the spectral dimensionality of outdoor visible and near-infrared illumination functions

Color pixel distributions provide a useful cue for object recognition. Recently, for example, a technique called color indexing due to Swain and Ballard [6] used color histograms for the efficient recognition of objects from a large database in the presence of changes in scene geometry and occlusion. The effectiveness of this and other approaches that match color distributions, however, depends on the approximate constancy of the scene illumination. In this paper, we develop color histogram descriptors that are invariant to changes in the intensity and spectral distribution of the illumination. We present a set of experiments that dernonstrate the effectiveness of these descriptors for object recognition in the presence of changes in illuminant spectral power distribution.

Using Illumination Invariant Color Histogram Descriptors for Recognit ion

We consider the problem of acquiring models for unknown materials in airborne 0.4 /spl mu/m-2.5 /spl mu/m hyperspectral imagery and using these models to identify the unknown materials an image data obtained under significantly different conditions. The material models are generated using an airborne sensor spectrum measured under unknown conditions and a physical model for spectral variability. For computational efficiency, the material models are represented using low-dimensional spectral subspaces. We demonstrate the effectiveness of the material models using a set of material tracking experiments in HYDICE images acquired in a forest environment over widely varying conditions. We show that techniques based on the new representation significantly outperform methods based on direct spectral matching.

Physics-based model acquisition and identification in airborne spectral images

An increase in the off-nadir viewing angle for an airborne visible/near-infrared through short-wave infrared (VNIR/SWIR) imaging spectrometer leads to a decrease in upward atmospheric transmittance and an increase in line-of-sight scattered path radiance. These effects combine to reduce the spectral contrast between different materials in the sensed signal. We analyze the impact of viewing angle on material discriminability for 237 materials over a wide range of conditions. Material discriminability is quantified using a statistical algorithm that employs a subspace model to represent the set of spectra for a material as conditions vary. We show that reliable material discrimination is possible over a range of conditions even for large off-nadir viewing angles. We illustrate the performance of material identification over different viewing angles using simulated forest hyperspectral images.

The impact of viewing geometry on vision through the atmosphere

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