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We present a method for the fast 3D face reconstruction of people wearing glasses. Our method explicitly and robustly models the case in which a face to be reconstructed is partially occluded by glasses. We propose a simple and generic model for glasses that copes with a wide variety of different shapes, colors and styles, without the need for any database or learning. Our algorithm is simple, fast...
We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation...
3D models provide a common ground for different representations of human bodies. In turn, robust 2D estimation has proven to be a powerful tool to obtain 3D fits in-the-wild. However, depending on the level of detail, it can be hard to impossible to acquire labeled data for training 2D estimators on large scale. We propose a hybrid approach to this problem: with an extended version of the recently...
Detection and segmentation of cells is an important step for classifying the cells as cancerous or non-cancerous. Pathologists use microscopic images for analysis and further diagnosis of cancer. These images contain the microscopic structure of tissues and are stained using some staining components to facilitate the process. Staining process varies due to different stain manufacturers, staining practices...
Non-Gaussian statistical models fit SAR data better than Gaussian-based statistics, in most cases, but are complicated and time-consuming to use for unsupervised image segmentation via probabilistic clustering. The more advanced the model, the more complicated and slow the clustering. The U-distribution has been demonstrated to be one of the most flexible models, capturing the Gaussian/Wishart, the...
In this paper, a structural conditional random field framework (SCRF) is proposed to detect the detailed change information from high spatial resolution (HSR) remote sensing imagery. Traditional random field based methods encounter the over-smoothing problem when deal with HSR images and the boundary of changed objects cannot be preserved well. To solve this problem, in SCRF, fuzzy c means (FCM) is...
This paper presents a computer vision-based methodology for human action recognition. First, the shape based pose features are constructed based on area ratios to identify the human silhouette in images. The proposed features are invariance to translation and scaling. Once the human body features are extracted from videos, different human actions are learned individually on the training frames of...
We demonstrate an integrated strategy for identifying buildings in very high resolution satellite imagery of urban areas. Buildings are extracted using structural, contextual, and spectral information. We perform multi-resolution and spectral difference segmentation to obtain a proper object segmentation. First, we use One-Class support vector machine (SVM) in order to determine the man-made structures...
With increasing of the spatial resolution of satellite imaging sensors, object-based image analysis (OBIA) has been gaining prominence in remote sensing applications. However, scale selection in multi-scale segmentation and OBIA remains a challenge, which directly reduces efficiency of land cover mapping. In this study, we presented an object-based land cover mapping using adaptive scale segmentation...
Change detection techniques for remote sensing images are increasingly applied to many fields, such as disaster monitoring, vegetation coverage analysis and so on. How to improve the accuracy of detection has been a critical topic that confuse the researchers for a long time. In this paper, a method combining multiscale segmentation and fusion for high-resolution images is presented. The strategy...
A synthetic image analysis method is proposed for in-situ detection of particle agglomeration for monitoring crystallization processes, based on using a non-invasive imaging system. The proposed method consists of image pre-processing, feature analysis, shape identification, and re-segmentation. Firstly, in-situ captured images are pre-processed to eliminate the influence from uneven illumination...
Traditional point tracking algorithms such as the KLT use local 2D information aggregation for feature detection and tracking, due to which their performance degrades at the object boundaries that separate multiple objects. Recently, CoMaL Features have been proposed that handle such a case. However, they proposed a simple tracking framework where the points are re-detected in each frame and matched...
we present a method based on a locally affineinvariant constraint for volumetric registration of 3D solid shapes. The core idea of this method is that an affine combination of the given point in 3D solid shapes that are directly connected to the given point, and the corresponding weight of each neighboring point can be obtained by the method of generalized least square. The input of our method is...
With the recent developments in medicine and biology experiments a large amount of data is gathered in the form of multimedia elements (images, videos). Many algorithms have been developed and adapted based on the system of interest, and often the most challenging feature of the images may be used to facilitate a better analysis of the image. Herein, we developed an image analysis algorithm for quantification...
Face editing has a variety of applications, especially with the increasing popularity of photography using mobile devices. In this work, we argue that the performance of face image editing can be further improved by using semantic segmentation which marks each pixel with a label that indicates its corresponding facial part. To this end, we propose a deep learning based method for automatic pixel-level...
Recent advancement in genomics technologies has opened a new realm for early detection of diseases that shows potential to overcome the drawbacks of manual detection technologies. In this work, we have presented efficient contour aware segmentation approach based based on fully conventional network whereas for classification we have used extreme machine learning based on CNN features extracted from...
A major challenge in visual highway traffic analytics is to disaggregate individual vehicles from clusters formed in dense traffic conditions. Here we introduce a data driven 3D generative reasoning method to tackle this segmentation problem. The method is comprised of offline (learning) and online (inference) stages. In the offline stage, we fit a mixture model for the prior distribution of vehicle...
Histopathology plays a role as the gold standard in clinic for disease diagnosis. The identification and segmentation of histological structures are the prerequisite to disease diagnosis. With the advent of digital pathology, researchers' attention is attracted by the analysis of digital pathology images. In order to relieve the workload on pathologists, a robust segmentation method is needed in clinic...
Leaf can be one of the many different parameters on the basis of which a plant can be uniquely identified. Many plants types are on the verge of extinction and can be taken care of, if identified correctly. The proposed method discusses an automated image processing system for leaf classification. The leaf pixels from the image are segmented and termed as region of interest (ROI). A set of geometrical,...
Several major advances in Cell and Molecular Biology have been made possible by recent advances in live-cell microscopy imaging. To support these efforts, automated image analysis methods such as cell segmentation and tracking during a time-series analysis are needed. To this aim, one important step is the validation of such image processing methods. Ideally, the “ground truth” should be known, which...
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