Specular free spectral imaging using orthogonal subspace projection

Specularity is an important issue in computer vision. Many algorithms have been proposed to remove highlights for color images. However, to our knowledge, no work has been done so far which specifically handles highlights in spectral imaging. In this paper, we introduce a specular invariant representation for hyperspectral images based on the dichromatic model and orthogonal subspace projection. It is a simple one step algorithm which only involves pixel-level operations, thus it does not require any segmentation. Nor does it require any pre/postprocessing or explicit spectral normalization. Importantly, unlike the previous methods for color images, it can be theoretically extended to handle highlights caused by multicolored illuminations. Experimental results demonstrate the effectiveness of our algorithm

Similarity based vehicle trajectory clustering and anomaly detection

In this paper, we proposed a hierarchical clustering frame-work to classify vehicle motion trajectories in real trafc video based on their pairwise similarities. First raw trajec-tories are pre-processed and resampled at equal space inter-vals. Then spectral clustering is used to group trajectories with similar spatial patterns. Dominant paths and lanes can be distinguished as a result of two-layer hierarchical cluster-ing. Detection of novel trajectories is also possible based on the clustering results. Experimental results demonstrate the superior performance of spectral clustering compared with conventional fuzzy K-means clustering and some results of anomaly detection are presented. 1

On automatic absorption detection for Imaging spectroscopy: A comparative study

In this paper, we aim at presenting a survey on automatic absorption recovery methods for imaging spectroscopy. We commence by viewing the algorithms in the literature from a technical perspective and presenting an overview of the derivative analysis, fingerprint, and maximum modulus wavelet transform techniques. In addition to these methods, we also present a novel absorption recovery approach based upon unimodal regression and continuum removal. With this technical review of the methods under study, we perform a complexity analysis and examine the implementation issues pertaining to each of the alternatives. We show how detected absorption bands can be used for purposes of material identification. We conclude this paper by providing a performance study and providing identification results on hyperspectral imagery. To this end, we make use of a number of distance measures to evaluate the quality of the recovered absorptions, as compared to continuum-removed spectra

Invariant Object Material Identification via Discriminant Learning on Absorption Features

In this paper, we propose a novel approach to object material identification in spectral imaging by combining the use of absorption features and statistical machine learning techniques. We depart from the significance of spectral absorption features for material identification and cast the problem into a classification setting which can be tackled using support vector machines. Hence, we commence by proposing a novel method for the robust detection of absorption bands in the spectra. With these bands at hand, we show how those absorptions which are most relevant to the classification task in hand may be selected via discriminant learning. We then train a support vector machine for purposes of classification making use of an absorption feature representation scheme which is robust to varying photometric conditions. We perform experiments on real world data and compare the results yield by our approach with those recovered using an alternative. We also illustrate the invariance of the absorption features recovered by our method to different photometric effects

Invariant object material identification via discriminant learning on absorption features

In this paper, we propose a novel approach to object material identification in spectral imaging by combining the use of absorption features and statistical machine learning techniques. We depart from the significance of spectral absorption features for material identification and link the use of spectral absorption features with statistical learning. We do this by casting the identification problem into a classification setting which can be tackled using support vector machines. Hence, we commence by proposing a novel method for the robust detection of absorption bands in the spectra. With these bands at hand, we show how those absorptions which are most relevant to the classification task in hand may be selected via discriminant learning. We then train a support vector machine for purposes of classification making use of an absorption feature representation scheme which is robust to varying photometric conditions. We perform experiments on real world data and compare the results yield by our approach with those recovered using an alternative. We also illustrate the invariance of the absorption features recovered by our method to different photometric effects. 1