Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D struc- ture from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.Comment: Accepted for publication in CVI

Single image defocus estimation by modified gaussian function

© 2019 John Wiley & Sons, Ltd. This article presents an algorithm to estimate the defocus blur from a single image. Most of the existing methods estimate the defocus blur at edge locations, which further involves the reblurring process. For this purpose, existing methods use the traditional Gaussian function in the phase of reblurring but it is found that the traditional Gaussian kernel is sensitive to the edges and can cause loss of edges information. Hence, there are more chances of missing spatially varying blur at edge locations. We offer the repeated averaging filters as an alternative to the traditional Gaussian function, which is more effective, and estimate the spatially varying defocus blur at edge locations. By using repeated averaging filters, a blur sparse map is computed. The obtained sparse map is propagated by integration of superpixels segmentation and transductive inference to estimate full defocus blur map. Our adopted method of repeated averaging filters has less computational time of defocus blur map estimation and has better visual estimates of the final defocus recovered map. Moreover, it has surpassed many previous state-of-the-art proposed systems in terms of quantative analysis

VOLUME DETERMINATION OF LEG ULCER USING REVERSE ENGINEERING METHOD

Reverse Engineering is defined as the process of obtaining a geometric CAD model by digitizing the existing objects. In medical application, it is applied to obtain the CAD model of human skin surface. Chronic leg ulcer refers to the wound which does not heal in the predictable period. Approximately 1% of the world population will develop leg ulcers in their lifespa

'Structure-from-Motion' photogrammetry: A low-cost, effective tool for geoscience applications

High-resolution topographic surveying is traditionally associated with high capital and logistical costs, so that data acquisition is often passed on to specialist third party organisations. The high costs of data collection are, for many applications in the earth sciences, exacerbated by the remoteness and inaccessibility of many field sites, rendering cheaper, more portable surveying platforms (i.e. terrestrial laser scanning or GPS) impractical. This paper outlines a revolutionary, low-cost, user-friendly photogrammetric technique for obtaining high-resolution datasets at a range of scales, termed ‘Structure-from-Motion’ (SfM). Traditional softcopy photogrammetric methods require the 3-D location and pose of the camera(s), or the 3-D location of ground control points to be known to facilitate scene triangulation and reconstruction. In contrast, the SfM method solves the camera pose and scene geometry simultaneously and automatically, using a highly redundant bundle adjustment based on matching features in multiple overlapping, offset images. A comprehensive introduction to the technique is presented, followed by an outline of the methods used to create high-resolution digital elevation models (DEMs) from extensive photosets obtained using a consumer-grade digital camera. As an initial appraisal of the technique, an SfM-derived DEM is compared directly with a similar model obtained using terrestrial laser scanning. This intercomparison reveals that decimetre-scale vertical accuracy can be achieved using SfM even for sites with complex topography and a range of land-covers. Example applications of SfM are presented for three contrasting landforms across a range of scales including; an exposed rocky coastal cliff; a breached moraine-dam complex; and a glacially-sculpted bedrock ridge. The SfM technique represents a major advancement in the field of photogrammetry for geoscience applications. Our results and experiences indicate SfM is an inexpensive, effective, and flexible approach to capturing complex topography

Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images

Modeling statistical regularity plays an essential role in ill-posed image processing problems. Recently, deep learning based methods have been presented to implicitly learn statistical representation of pixel distributions in natural images and leverage it as a constraint to facilitate subsequent tasks, such as color constancy and image dehazing. However, the existing CNN architecture is prone to variability and diversity of pixel intensity within and between local regions, which may result in inaccurate statistical representation. To address this problem, this paper presents a novel fully point-wise CNN architecture for modeling statistical regularities in natural images. Specifically, we propose to randomly shuffle the pixels in the origin images and leverage the shuffled image as input to make CNN more concerned with the statistical properties. Moreover, since the pixels in the shuffled image are independent identically distributed, we can replace all the large convolution kernels in CNN with point-wise ($1*1$) convolution kernels while maintaining the representation ability. Experimental results on two applications: color constancy and image dehazing, demonstrate the superiority of our proposed network over the existing architectures, i.e., using 1/10$\sim$1/100 network parameters and computational cost while achieving comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201

Does HDR pre-processing improve the accuracy of 3D models obtained by means of two conventional SfM-MVS software packages? the case of the corral del veleta rock glacier

La precisión de los diferentes flujos de trabajo usando la estructura del Movimiento y técnicas estéreo vista múltiple (SfM-MVS) está probado. Nubes de doce puntos del Corral del Veleta glaciar de roca, en España, se produjeron con dos diferentes paquetes de software (123D Catch y Agisoft Photoscan), utilizando imágenes de bajo rango dinámico y composiciones de alto rango dinámico (HDR) para tres años distintos (2011, 2012 y 2014). La exactitud de las nubes de puntos resultante se evaluó mediante modelos de referencia adquiridos cada año con un escáner láser terrestre. Tres parámetros se utilizaron para estimar la precisión de cada nube de puntos: el RMSE, la nube-a-nube distancia (C2C) y la Multiscale-Model a comparación de modelos (M3C2). El M3C2 el error promedio varió de 0,084 m (desviación estándar de 0,403 m) a 1.451 m (desviación estándar de 1.625 m). Agisoft Photoscan superar 123D Catch, produciendo más precisas y densas nubes de puntos en 11 de 12 casos, siendo este trabajo, la primera comparación entre ambos paquetes de software en la literatura. Ninguna mejora significativa fue observada a través de HDR de pre-procesamiento. A nuestro conocimiento, esta es la primera vez que la exactitud geométrica de modelos 3D obtenidos utilizando LDR y HDR composiciones son comparados. Estos hallazgos pueden ser de interés para los investigadores que deseen para estimar los cambios geomórficas con SfM-MVS enfoques.The accuracy of different workflows using Structure-from-Motion and Multi-View-Stereo techniques (SfM-MVS) is tested. Twelve point clouds of the Corral del Veleta rock glacier, in Spain, were produced with two different software packages (123D Catch and Agisoft Photoscan), using Low Dynamic Range images and High Dynamic Range compositions (HDR) for three different years (2011, 2012 and 2014). The accuracy of the resulting point clouds was assessed using benchmark models acquired every year with a Terrestrial Laser Scanner. Three parameters were used to estimate the accuracy of each point cloud: the RMSE, the Cloud-to-Cloud distance (C2C) and the Multiscale-Model-to-Model comparison (M3C2). The M3C2 mean error ranged from 0.084 m (standard deviation of 0.403 m) to 1.451 m (standard deviation of 1.625 m). Agisoft Photoscan overcome 123D Catch, producing more accurate and denser point clouds in 11 out 12 cases, being this work, the first available comparison between both software packages in the literature. No significant improvement was observed using HDR pre-processing. To our knowledge, this is the first time that the geometrical accuracy of 3D models obtained using LDR and HDR compositions are compared. These findings may be of interest for researchers who wish to estimate geomorphic changes using SfM-MVS approaches.peerReviewe

Image based automatic vehicle damage detection

Automatically detecting vehicle damage using photographs taken at the accident scene is very useful as it can greatly reduce the cost of processing insurance claims, as well as provide greater convenience for vehicle users. An ideal scenario would be where the vehicle user can upload a few photographs of the damaged car taken from a mobile phone and have the dam- age assessment and insurance claim processing done automatically. However, such a solution remains a challenging task due to a number of factors. For a start, the scene of the accident is typically an unknown and uncontrolled outdoor environment with a plethora of factors beyond our control including scene illumination and the presence of surrounding objects which are not known a priori. In addition, since vehicles have very reflective metallic bodies the photographs taken in such an uncontrolled environment can be expected to have a considerable amount of inter object reflection. Therefore, the application of standard computer vision techniques in this context is a very challenging task. Moreover, solving this task opens up a fascinating repertoire of computer vision problems which need to be addressed in the context of a very challenging scenario. This thesis describes research undertaken to address the problem of au- tomatic vehicle damage detection using photographs. A pipeline addressing a vertical slice of the broad problem is considered while focusing on mild vehicle damage detection. We propose to use 3D CAD models of undamaged vehicles which are used to obtain ground truth information in order to infer what the vehicle with mild damage in the photograph should have looked like, if it had not been damaged. To this end, we develop 3D pose estimation algorithms to register an undamaged 3D CAD model over a photograph of the known dam- aged vehicle. We present a 3D pose estimation method using image gradient information of the photograph and the 3D model projection. We show how the 3D model projection at the recovered 3D pose can be used to identify components of a vehicle in the photograph which may have mild damage. In addition, we present a more robust 3D pose estimation method by minimizing a novel illumination invariant distance measure, which is based on a Mahalanobis distance between attributes of the 3D model projection and the pixels in the photograph. In principle, image edges which are not present in the 3D CAD model projection can be considered to be vehicle damage. However, since the vehicle body is very reflective, there is a large amount of inter object reflection in the photograph which may be misclassified as damage. In order to detect image edges caused by inter object reflection, we propose to apply multi-view geometry techniques on two photographs of the vehicle taken from different view points. To this end, we also develop a robust method to obtain reliable point correspondences across the photographs which are dominated by large reflective and mostly homogeneous regions. The performance of the proposed methods are experimentally evaluated on real photographs using 3D CAD models of varying accuracy. We expect that the research presented in this thesis will provide the groundwork for designing an automatic photograph based vehicle damage de- tection system. Moreover, we hope that our method will provide the foundation for interesting future research