Multiple return separation for a full-field ranger via continuous waveform modelling

We present two novel Poisson noise Maximum Likelihood based methods for identifying the individual returns within mixed pixels for Amplitude Modulated Continuous Wave rangers. These methods use the convolutional relationship between signal returns and the recorded data to determine the number, range and intensity of returns within a pixel. One method relies on a continuous piecewise truncated-triangle model for the beat waveform and the other on linear interpolation between translated versions of a sampled waveform. In the single return case both methods provide an improvement in ranging precision over standard Fourier transform based methods and a decrease in overall error in almost every case. We find that it is possible to discriminate between two light sources within a pixel, but local minima and scattered light have a significant impact on ranging precision. Discrimination of two returns requires the ability to take samples at less than 90 phase shifts

CNN based Learning using Reflection and Retinex Models for Intrinsic Image Decomposition

Most of the traditional work on intrinsic image decomposition rely on deriving priors about scene characteristics. On the other hand, recent research use deep learning models as in-and-out black box and do not consider the well-established, traditional image formation process as the basis of their intrinsic learning process. As a consequence, although current deep learning approaches show superior performance when considering quantitative benchmark results, traditional approaches are still dominant in achieving high qualitative results. In this paper, the aim is to exploit the best of the two worlds. A method is proposed that (1) is empowered by deep learning capabilities, (2) considers a physics-based reflection model to steer the learning process, and (3) exploits the traditional approach to obtain intrinsic images by exploiting reflectance and shading gradient information. The proposed model is fast to compute and allows for the integration of all intrinsic components. To train the new model, an object centered large-scale datasets with intrinsic ground-truth images are created. The evaluation results demonstrate that the new model outperforms existing methods. Visual inspection shows that the image formation loss function augments color reproduction and the use of gradient information produces sharper edges. Datasets, models and higher resolution images are available at https://ivi.fnwi.uva.nl/cv/retinet.Comment: CVPR 201

Robust density modelling using the student's t-distribution for human action recognition

The extraction of human features from videos is often inaccurate and prone to outliers. Such outliers can severely affect density modelling when the Gaussian distribution is used as the model since it is highly sensitive to outliers. The Gaussian distribution is also often used as base component of graphical models for recognising human actions in the videos (hidden Markov model and others) and the presence of outliers can significantly affect the recognition accuracy. In contrast, the Student's t-distribution is more robust to outliers and can be exploited to improve the recognition rate in the presence of abnormal data. In this paper, we present an HMM which uses mixtures of t-distributions as observation probabilities and show how experiments over two well-known datasets (Weizmann, MuHAVi) reported a remarkable improvement in classification accuracy. © 2011 IEEE