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We consider the fully automated behavior understanding through visual cues in industrial environments. In contrast to most existing work, which relies on domain knowledge to construct complex handcrafted features from inputs, we exploit a Convolutional Neural Network (CNN), which is a type of deep model and can act directly on the raw inputs, to automate the process of feature construction. Although...
In this paper, we propose a novel non-invasive framework for the early diagnosis of prostate cancer from diffusion-weighted magnetic resonance imaging (DW-MRI). The proposed approach consists of three main steps. In the first step, the prostate is localized and segmented based on a new level-set model. In the second step, the apparent diffusion coefficient (ADC) of the segmented prostate volume is...
We propose a software solution which allows the user to design a realistic illumination for a given 2D image of a face. The user paints a few strokes on the image to give clues of desired novel lighting effects. The algorithm produces an image of the face under the best possible realistic illumination, accordingly. It takes advantage of a 3D Morphable Model framework and a state of the art inverse...
Fisheye cameras have become extremely popular in applications where the goal is to capture large fields of view with only one camera. However, the wide-angle fisheye imagery has special characteristics that may not be very well suited for modern video codecs that employ block-based translational motion model. This model fails to describe complex deformable motion which is often present in fisheye...
The accuracy of calibration will significantly affect the post processing capability of light field imaging. The geometry of the reconstructed scene is related to the parameters of light field closely, involving the accuracy of decoded rays and ambiguities from ray correspondences. Through exploring the ray correspondence, we derive a transformation matrix to describe the projective distortion on...
In this paper we propose to improve the localization and the 3D mapping provided by an RGBD SLAM algorithm, using a prior knowledge of the 3D model of the environment. The proposed solution relies on an feature-based RGBD SLAM algorithm to localize the camera and update the 3D map of the scene. To improve the accuracy and the robustness of the localization, we propose to combine in a local bundle...
Existing 3D lighting consistency based forensic methods have some practical problems. They usually require additional images and human labor to reconstruct the 3D face model for lighting estimation, and furthermore, they cannot deal with expressional faces effectively. These drawbacks make them unusable in many practical cases. In this paper, we propose a more practical 3D lighting based forensic...
In this paper, we propose a fast non-iterative camera pose voting method for 3D object identification. The proposed method improves the accuracy and speed upon the conventional local feature based 2D-to-3D matching between a 2D image and a 3D model reconstructed by the structure-from-motion (SfM) pipeline. Instead of performing iterative RANSAC based method for geometric verification, the proposed...
We present a novel statistical shape model and fitting process for the 3D Constrained Local Models (CLM), exploiting the properties of Independent Component Analysis (ICA), instead of the classic use of Principal Component Analysis (PCA), and adopting a non-Gaussian distribution of the shape prior information. Using ICA permits to exploit the real distribution of shape priors by adopting a Generalised...
This paper introduces a novel framework for segmenting retinal layers from optical coherence tomography (OCT) images. In order to account for the noise and inhomogeneity of OCT scans, especially for diseased ones, the proposed framework is based on unique joint model that combines shape, intensity, and spatial information, and is able to segment 12 distinct retinal layers. First, the shape prior is...
Segmentation of moving objects in a scene is difficult for non-stationary cameras, and especially challenging in the presence of fast and unstable egomotion, e.g., as encountered with car-mounted cameras or wearable devices. Based on an analysis of motion vanishing points of the scene and estimated depth, a geometric model that relates extracted 2D motion to a 3D motion field relative to the camera...
The just noticeable difference (JND) notion reflects the maximum tolerable distortion. It has been extensively used for the optimization of 2D applications. For stereoscopic 3D (S3D) content, this notion is different since it relies on different mechanisms linked to our binocular vision. Unlike 2D, 3D-JND models appeared recently and the related literature is rather limited. These models can be used...
Appearance model is widely used for image description and demonstrates an impressive performance in object detection. However, most appearance models can not be applied to more freedom object in still image, especially when dealt with variant objects whose shapes are modified by warping, rotation, etc. In this article, a simple but effective method to build a regional rotation-invariant feature descriptor...
We propose a deblurring algorithm of point cloud attributes inspired by multi-Wiener SURE-LET deconvolution. The image reconstructed by the SURE-LET approach is expressed as a linear combination of multiple filtered images by the filters defined on the frequency domain. The coefficients of the linear combination are calculated so that the estimate of mean squared error between the original and restored...
Six of the ten leading causes of death in the United States, including cancer, diabetes, and heart disease, can be directly linked to diet. Dietary intake, the process of determining what someone eats during the course of a day, provides valuable insights for mounting intervention programs for prevention of many of the above chronic diseases. Measuring accurate dietary intake is considered to be an...
We propose a new approach for accurate car pose estimation in images using only a dataset of 3D untextured models. Our algorithm detects both a car and its 3D pose. It is based on the matching of 3D models with the car in the image. With a part detector based on Convolutional Neural Networks, interest points corresponding to predefined 3D parts are extracted from the image. Then, we use the car geometry...
Object recognition based on local features computed at multiple locations is robust to occlusions, strong viewpoint changes and object deformations. These features should be repeatable, precise and distinctive. We present an operator for repeatable feature detection on depth images (relative to 3D models) as well as 2D intensity images. The proposed detector is based on estimating the curviness saliency...
CNN has shown excellent performance on object recognition based on huge amount of real images. For training with synthetic data rendered from 3D models alone to reduce the workload of collecting real images, we propose a concatenated self-restraint learning structure lead by a triplet and softmax jointed loss function for object recognition. Locally connected auto encoder trained from rendered images...
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