Consumer-grade range cameras such as the Kinect sensor have the potential to be used in mapping applications where accuracy requirements are less strict. To realize this potential insight into the geometric quality of the data acquired by the sensor is essential. In this paper we discuss the calibration of the Kinect sensor, and provide an analysis of the accuracy and resolution of its depth data. Based on a mathematical model of depth measurement from disparity a theoretical error analysis is presented, which provides an insight into the factors influencing the accuracy of the data. Experimental results show that the random error of depth measurement increases with increasing distance to the sensor, and ranges from a few millimeters up to about 4 cm at the maximum range of the sensor. The quality of the data is also found to be influenced by the low resolution of the depth measurements.

https://minerva-access.unimelb.edu.au/bitstream/handle/11343/225605/sensors-12-01437.pdf

Accuracy and Resolution of Kinect Depth Data for Indoor Mapping Applications

Pedestrian detection is a rapidly evolving area in computer vision with key applications in intelligent vehicles, surveillance, and advanced robotics. The objective of this paper is to provide an overview of the current state of the art from both methodological and experimental perspectives. The first part of the paper consists of a survey. We cover the main components of a pedestrian detection system and the underlying models. The second (and larger) part of the paper contains a corresponding experimental study. We consider a diverse set of state-of-the-art systems: wavelet-based AdaBoost cascade, HOG/linSVM, NN/LRF, and combined shape-texture detection. Experiments are performed on an extensive data set captured onboard a vehicle driving through urban environment. The data set includes many thousands of training samples as well as a 27-minute test sequence involving more than 20,000 images with annotated pedestrian locations. We consider a generic evaluation setting and one specific to pedestrian detection onboard a vehicle. Results indicate a clear advantage of HOG/linSVM at higher image resolutions and lower processing speeds, and a superiority of the wavelet-based AdaBoost cascade approach at lower image resolutions and (near) real-time processing speeds. The data set (8.5 GB) is made public for benchmarking purposes.

/pdf/monocular-pedestrian-detection-survey-and-experiments-32xcp2mo03.pdf

Monocular Pedestrian Detection: Survey and Experiments

We develop a continuous framework for the analysis of 4D light fields, and describe novel variational methods for disparity reconstruction as well as spatial and angular super-resolution. Disparity maps are estimated locally using epipolar plane image analysis without the need for expensive matching cost minimization. The method works fast and with inherent subpixel accuracy since no discretization of the disparity space is necessary. In a variational framework, we employ the disparity maps to generate super-resolved novel views of a scene, which corresponds to increasing the sampling rate of the 4D light field in spatial as well as angular direction. In contrast to previous work, we formulate the problem of view synthesis as a continuous inverse problem, which allows us to correctly take into account foreshortening effects caused by scene geometry transformations. All optimization problems are solved with state-of-the-art convex relaxation techniques. We test our algorithms on a number of real-world examples as well as our new benchmark data set for light fields, and compare results to a multiview stereo method. The proposed method is both faster as well as more accurate. Data sets and source code are provided online for additional evaluation.

Variational Light Field Analysis for Disparity Estimation and Super-Resolution

This paper describes a method for scene reconstruction of complex, detailed environments from 3D light fields. Densely sampled light fields in the order of 109 light rays allow us to capture the real world in unparalleled detail, but efficiently processing this amount of data to generate an equally detailed reconstruction represents a significant challenge to existing algorithms. We propose an algorithm that leverages coherence in massive light fields by breaking with a number of established practices in image-based reconstruction. Our algorithm first computes reliable depth estimates specifically around object boundaries instead of interior regions, by operating on individual light rays instead of image patches. More homogeneous interior regions are then processed in a fine-to-coarse procedure rather than the standard coarse-to-fine approaches. At no point in our method is any form of global optimization performed. This allows our algorithm to retain precise object contours while still ensuring smooth reconstructions in less detailed areas. While the core reconstruction method handles general unstructured input, we also introduce a sparse representation and a propagation scheme for reliable depth estimates which make our algorithm particularly effective for 3D input, enabling fast and memory efficient processing of "Gigaray light fields" on a standard GPU. We show dense 3D reconstructions of highly detailed scenes, enabling applications such as automatic segmentation and image-based rendering, and provide an extensive evaluation and comparison to existing image-based reconstruction techniques.

Scene reconstruction from high spatio-angular resolution light fields

In this paper, we suggest a variational model for optic flow computation based on non-linearised and higher order constancy assumptions. Besides the common grey value constancy assumption, also gradient constancy, as well as the constancy of the Hessian and the Laplacian are proposed. Since the model strictly refrains from a linearisation of these assumptions, it is also capable to deal with large displacements. For the minimisation of the rather complex energy functional, we present an efficient numerical scheme employing two nested fixed point iterations. Following a coarse-to-fine strategy it turns out that there is a theoretical foundation of so-called warping techniques hitherto justified only on an experimental basis. Since our algorithm consists of the integration of various concepts, ranging from different constancy assumptions to numerical implementation issues, a detailed account of the effect of each of these concepts is included in the experimental section. The superior performance of the proposed method shows up by significantly smaller estimation errors when compared to previous techniques. Further experiments also confirm excellent robustness under noise and insensitivity to parameter variations.

Highly accurate optic flow computation with theoretically justified warping

We present a novel method for the fast computation of rotation invariant ?local binary patterns? (LBP) on 3D volume data. Unlike a previous publication on 3D LBP, this new approach is not limited to ?uniform patterns?, providing a real 3D extension of the standard and rotation invariant LBP. We evaluate our methods in the context of 3D texture analysis of biological data.

3D rotation invariant local binary patterns

In this paper, we present a novel method for the generation of Epipolar-Image (EPI) representations of 4D Light Fields (LF) from raw data captured by a single lens "Focused Plenoptic Camera". Compared to other LF representations which are usually used in the context of computational photography with Plenoptic Cameras, the EPI representation is more suitable for image analysis tasks - providing direct access to scene geometry and reflectance properties.

The generation of EPIs requires a set of "all in focus" (full depth of field) images from different views of a scene. Hence, the main contribution of this paper is a novel algorithm for the rendering of such images from a single raw image captured with a Focused Plenoptic Camera. The main advantage of the proposed approach over existing full depth of field methods is that is able to cope with non-Lambertian reflectance in the scene.

Generating EPI representations of 4D Light fields with a single lens focused plenoptic camera

In this paper, we present a framework for range flow estimation from Microsoft's multi-modal imaging device Kinect. We address all essential stages of the flow computation process, starting from the calibration of the Kinect, over the alignment of the range and color channels, to the introduction of a novel multi-modal range flow algorithm which is robust against typical (technology dependent) range estimation artifacts.

https://ipm.iwr.uni-heidelberg.de/publications/PDFs/2011/gottfried_ISVC2011.pdf

Computing range flow from multi-modal Kinect data

In this paper, we present an adaptation the Bag of Features (BoF) concept to 3D shape retrieval problems. The BoF approach has recently become one of the most popular methods in 2D image retrieval. We extent this approach from 2D images to 3D shapes. Following the BoF outline, we address the necessary modifications for the 3D extension and present novel solutions for the parameterization of 3D patches, a 3D rotation invariant similarity measure for these patches and a method for the codebook generation. We experimentally evaluate the performance of our methods on the Princeton Shape Benchmark .

A Bag of Features Approach for 3D Shape Retrieval

The chicken chorioallantoic membrane (CAM) is a frequently used tissue for studying vascular growth and remodeling, notably non-sprouting angiogenesis by formation of transluminal pillars. Vascular pericytes have received increasing attention in the field of angiogenesis research and appear important for pillar growth. Our earlier observation that desmin (DES), but not alpha-smooth muscle actin (alphaSMA) was expressed in pericytes of the mature CAM capillary plexus after E12 was confirmed by others. However, in different species or vascular beds, either marker or both have been used to identify pericytes, raising the questions if (1) expression of these cytoskeletal proteins really was mutually exclusive; or (2) different types of pericytes existed in the same vascular bed. Using triple labeling with fluorochrome-conjugated markers Sambucus nigra agglutinin, DES or alphaSMA, and DNA-specific YoPro-1, we report here for the first time a delicate filamentous, circumferentially oriented alphaSMA pattern in periendothelial cells of the mature CAM capillary plexus, quite different from the coarser, axially oriented DES pattern. A new method for automatic classification of DNA-staining pattern was applied to compare nuclei of DES- or alphaSMA-positive cells. It predicted colocalisation of both proteins in most capillary pericytes, which was confirmed by double immunostaining for DES and alphaSMA. We conclude that (1) in contrast to published work, DES and alphaSMA are not mutually exclusive in most pericytes; (2) different types of pericytes may co-exist in the same vascular bed; (3) on average, one pericyte is associated with two transluminal pillars; (4) a novel imaging modality may be useful for cell identification in angiogenesis research and elsewhere.

Janis Fehr

Papers

3D rotation invariant local binary patterns

Generating EPI representations of 4D Light fields with a single lens focused plenoptic camera

Computing range flow from multi-modal Kinect data

A Bag of Features Approach for 3D Shape Retrieval

Pericytes in the mature chorioallantoic membrane capillary plexus contain desmin and α-smooth muscle actin: relevance for non-sprouting angiogenesis