Abstract:This paper investigates motion estimation and segmentation of independently moving objects in video sequences that contain depth and intensity information, such as videos captured by a Time of Flight camera. Specifically, we present a motion estimation algorithm which is based on integration of depth and intensity data. The resulting motion information is used to derive long-term point trajectories. A segmentation technique groups the trajectories according to their motion and depth similarity into spatio-temp… Show more
“…Segmentation is a major and often the first step in many image processing applications (Ghuffar et al, 2014;Gonçalves et al, 2014;Wu et al, 2014). Segmentation is commonly used to partition the image into the objects/ regions of interest and non-relevant information.…”
Section: Image Processing and Segmentationmentioning
AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
“…Segmentation is a major and often the first step in many image processing applications (Ghuffar et al, 2014;Gonçalves et al, 2014;Wu et al, 2014). Segmentation is commonly used to partition the image into the objects/ regions of interest and non-relevant information.…”
Section: Image Processing and Segmentationmentioning
AbstractAlzheimer’s disease (AD) is a common health problem in elderly people. There has been considerable research toward the diagnosis and early detection of this disease in the past decade. The sensitivity of biomarkers and the accuracy of the detection techniques have been defined to be the key to an accurate diagnosis. This paper presents a state-of-the-art review of the research performed on the diagnosis of AD based on imaging and machine learning techniques. Different segmentation and machine learning techniques used for the diagnosis of AD are reviewed including thresholding, supervised and unsupervised learning, probabilistic techniques, Atlas-based approaches, and fusion of different image modalities. More recent and powerful classification techniques such as the enhanced probabilistic neural network of Ahmadlou and Adeli should be investigated with the goal of improving the diagnosis accuracy. A combination of different image modalities can help improve the diagnosis accuracy rate. Research is needed on the combination of modalities to discover multi-modal biomarkers.
“…Hosseini () applied GbSA to principle component analysis (Ghosh‐Dastidar, Adeli, & Dadmehr, ). Hosseini () applied GbSA to image segmentation of gray‐level images (Ghuffar, Brosch, Pfeifer, & Gelautz, ), where the whole image is segmented into several regions based on similarities and dissimilarities present between the pixels of the input image. Multilevel threshold is used in image segmentation, where the number of thresholds is given in advance.…”
This paper presents a review of recently developed physics‐based search and optimization algorithms that have been inspired by natural phenomena. They include Big Bang–Big Crunch, black hole search, galaxy‐based search, artificial physics optimization, electromagnetism optimization, charged system search, colliding bodies optimization, and particle collision algorithm.
“…Gotardo captured three-dimensional scene flow to provide delicate geometric details [31], while Liu utilized scene flow as a soft constraint for stereo matching and a prediction for next frame disparity estimation [34]. Ghuffar combined local estimation and global regularization in a TLS framework and utilized scene flow for segmentation and trajectory generation [35].…”
Section: Applicationsmentioning
confidence: 99%
“…Hence, scene flow under point cloud representation [1,50,51,33,52,53,54,55,56,57,58,35,59,60] can be presented as V = (∆X, ∆Y, ∆Z) = (U, V, W ), which truly reveal the three-dimensional displacement.…”
This paper is the first to review the scene flow estimation, which analyzes and compares methods, technical challenges, evaluation methodologies and performance of scene flow estimation.Existing algorithms are categorized in terms of scene representation, data source, and calculation scheme, and the pros and cons in each category are compared briefly. The datasets and evaluation protocols are enumerated, and the performance of the most representative methods is presented. A future vision is illustrated with few questions arisen for discussion. This survey presents a general introduction and analysis of scene flow estimation.
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