A New Structure of Stereo Algorithm Using Pixel Based Differences and Weighted Median Filter

This paper proposed a new algorithm for stereo vision system to obtain depth map or disparity map. The proposed stereo vision algorithm consists of three stages, matching cost computation, disparity optimization and disparity refinement. The first stage starts with matching cost computation, where pixel based differences methods are used. The matching methods are the combination of Absolute Difference (AD) and Gradient Matching (GM). Next, the second stage; disparity optimization utilizes Winner-Takes-All (WTA) technique to normalize the disparity values of each pixel of the image. Finally, for disparity refinement stage, weighted median (WM) filter is added to reduce and smother the noise on the disparity map

Метод бесконтактной оценки паттерна дыхания человека при помощи стереопары

The development of contactless monitoring methods of human vital signs is an important goal for modern medicine. The particular relevance of this issue appears with the control of the patient at home on their own, for example, to estimate the parameters of breathing during sleep, quality assessment and identification of various kinds of sleep disorders, such as, for example, sleep apnea disorder (a condition, which is characterized by the cessation of pulmonary ventilation more than for 10 seconds and fall of blood oxygen saturation).In this article we have implemented and tested an algorithm for non-contact monitoring of breathing pattern by two entrenched webcams aimed at the person. The algorithm is based on using the methods of computer vision and processing of video sequences.Authors pay particular attention to disparity map construction approaches and improving the signal / noise ratio by a combination of known functions comparing the intensity of pixels: AD - a function of absolute differences, and Census function, comparing bit strings of investigated image regions.An important role in the noise minimization plays a simple, but effective assumption for aggregation, the gist of which is that pixels having similar intensity belong to the same structures in the image, and hence have a similar disparity. The variability of input parameters of the method and the ability to adjust the number of iterations provide accurate disparity maps for the input image of almost any quality (testing was conducted for webcams CBR CW 833M).The main result of this approach is the breathing profile based on the reconstructed depth maps, reflecting the respiration rate of the person under examination and presenting data on the amplitude variations of his chest.The main difference of the proposed method from other publications is a high accuracy and the breath profile calculation in real-time. It was achieved through OpenCL technology and parallel computations using the graphics card.The algorithm was tested on a variety of subjects with anthropomorphic characteristics and types of breathing to investigate the limited application of the proposed method in practice.DOI: 10.7463/mathm.0415.0813373Предложен новый метод для бесконтактного мониторинга параметров дыхания человека с помощью двух веб-камер, образующих стереопару. Метод базируется на анализе полученных изображений и использует алгоритмы компьютерного зрения. Предложен подход к построению карты диспаратности и улучшению соотношения сигнал/шум для анализируемых изображений. Основным результатом исследования является алгоритм выделения дыхательного профиля на основе карт глубин, полученных при обработке стереопары изображений. Основным отличием предложенного метода от известных является более высокая точность и возможность получения профиля дыхания в режиме реального времени. Приведен пример применения разработанного метода для оценки параметров дыхания человека.DOI: 10.7463/mathm.0415.081337

Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique

Holoscopic 3D imaging is a promising technique for capturing full-colour spatial 3D images using a single aperture holoscopic 3D camera. It mimics fly’s eye technique with a microlens array, which views the scene at a slightly different angle to its adjacent lens that records three-dimensional information onto a two-dimensional surface. This paper proposes a method of depth map generation from a holoscopic 3D image based on graph cut technique. The principal objective of this study is to estimate the depth information presented in a holoscopic 3D image with high precision. As such, depth map extraction is measured from a single still holoscopic 3D image which consists of multiple viewpoint images. The viewpoints are extracted and utilised for disparity calculation via disparity space image technique and pixels displacement is measured with sub-pixel accuracy to overcome the issue of the narrow baseline between the viewpoint images for stereo matching. In addition, cost aggregation is used to correlate the matching costs within a particular neighbouring region using sum of absolute difference (SAD) combined with gradient-based metric and “winner takes all” algorithm is employed to select the minimum elements in the array as optimal disparity value. Finally, the optimal depth map is obtained using graph cut technique. The proposed method extends the utilisation of holoscopic 3D imaging system and enables the expansion of the technology for various applications of autonomous robotics, medical, inspection, AR/VR, security and entertainment where 3D depth sensing and measurement are a concern

Innovative 3D Depth Map Generation From A Holoscopic 3D Image Based on Graph Cut Technique

Holoscopic 3D imaging is a promising technique for capturing full-colour spatial 3D images using a single aperture holoscopic 3D camera. It mimics fly’s eye technique with a microlens array, which views the scene at a slightly different angle to its adjacent lens that records three-dimensional information onto a two-dimensional surface. This paper proposes a method of depth map generation from a holoscopic 3D image based on graph cut technique. The principal objective of this study is to estimate the depth information presented in a Holoscopic 3D image with high precision. As such, depth map extraction is measured from a single still holoscopic 3D image which consists of multiple viewpoint images. The viewpoints are extracted and utilised for disparity calculation via disparity space image technique and pixels displacement is measured with sub-pixel accuracy to overcome the issue of the narrow baseline between the viewpoint images for stereo matching. In addition, cost aggregation is used to correlate the matching costs within a particular neighbouring region using sum of absolute difference (SAD) combined with gradient-based metric and “winner takes all” algorithm is employed to select the minimum elements in the array as optimal disparity value. Finally, the optimal depth map is obtained using graph cut technique. The proposed method extends the utilisation of holoscopic 3D imaging system and enables the expansion of the technology for various applications of autonomous robotics, medical, inspection, AR/VR, security and entertainment where 3D depth sensing and measurement are a concern.NPR

Depth extraction in 3D holoscopic images

Holoscopy is a technology that comes as an alternative to traditional methods of capturing images and viewing 3D content. A light field camera can be used for the capture process, which allows the storage of information regarding the direction all light rays, unlike the traditional cameras. With the saved information it is possible to estimate a depth map that can be used for areas such as robotic navigation or medicine. This dissertation proposes to improve an existing depth estimation algorithm by developing new processing mechanisms which provide a dynamic balancing between computational speed and precision. All proposed solutions were implemented using CPU parallelization in order to reduce the computing time. For the proposed algorithms, qualitative tests were performed using the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Structural Similarity Index Method (SSIM). A comparative analysis between the processing times of the proposed algorithms and the original solutions was also performed. The achieved results were quite satisfactory since there was a significant decrease in processing times for any of the proposed solutions without the accuracy estimate being substantially affected.A holoscopia é uma tecnologia que surge como alternativa aos métodos tradicionais de captura de imagens e de visualização de conteúdos 3D. Para o processo de captura é utilizada uma câmera de campo de luz que permite armazenar a direção de todos os raios, ao contrário do que acontece com as câmeras tradicionais. Com a informação guardada é possível gerar um mapa de profundidade da imagem cuja utilização poderá ser útil em áreas como a navegação robótica ou a medicina. Nesta dissertação, propõe-se melhorar uma solução já existente através do desenvolvimento de novos mecanismos de processamento que permitam um balanceamento dinâmico entre a velocidade computacional e a precisão. Todas as soluções propostas foram implementadas recorrendo à paralelização da CPU para que se conseguisse reduzir substancialmente o tempo de computação. Para os algoritmos propostos foram efectuados testes qualitativos com recurso à utilização das métricas Mean Absolute Error (MAE), Root Mean Square Error (RMSE) e Structural Similarity Index Method (SSIM). Uma análise comparativa entre os tempos de processamento dos algoritmos propostos e as soluções originais foi também efectuada. Os resultados alcançados foram bastante satisfatórios dado que se registou uma redução acentuada nos tempos de processamento para qualquer uma das soluções implementadas sem que a estimativa de precisão tenha sido substancialmente afetada

Stereo Disparity through Cost Aggregation with Guided Filter

International audienceEstimating the depth, or equivalently the disparity, of a stereo scene is a challenging problem in computer vision. The method proposed by Rhemann et al. in 2011 is based on a filtering of the cost volume, which gives for each pixel and for each hypothesized disparity a cost derived from pixel-by-pixel comparison. The filtering is performed by the guided filter proposed by He et al. in 2010. It computes a weighted local average of the costs. The weights are such that similar pixels tend to have similar costs. Eventually, a winner-take-all strategy selects the disparity with the minimal cost for each pixel. Non-consistent labels according to left-right consistency are rejected; a densification step can then be launched to fill the disparity map. The method can be used to solve other labeling problems (optical flow, segmentation) but this article focuses on the stereo matching problem. Source Code A software written in C++ is available on the IPOL web page of this article 1 , which is the code used in the online demo. This gives similar results to the original authors' Matlab implemen-tation 2 . The program needs several parameters (see Section 4 for more detailed explanations). By default they are tuned as suggested in the original article, but one can adapt them to get better results. Supplementary Material In the demo, an optional rectification step can be launched before running the algorithm. The source code for this preprocessing step (not reviewed) can be found at the IPOL web page of this article 3