Road detection is a vital task for autonomous vehicles, as it has a direct link to passengers’ safety. Given its importance, researchers aimed to improve its accuracy and robustness. We look at the task from a holistic point of view, where we aim to balance computation and accuracy. A multimodal road detection pipeline is proposed, which fuses the camera image with the preprocessed LIDAR input. First, the LIDAR input is preprocessed using three-dimensional models inspired from computer graphics to generate image-like representations. Then, the preprocessed LIDAR input is combined with the camera image using a fusion module named inputs cross-fusion module, to reduce the computation amount required by other fusion strategies. To prevent the accuracy loss caused by the computation gain, we introduce the surface normal information to add distinctiveness. Furthermore, we propose a cost/benefit metric to evaluate the trade-off between computation cost and accuracy of road detection approaches. Several tests were conducted using the KITTI road detection benchmark based on deep convolutional neural networks, the obtained results were considered very satisfactory. In particular, the robustness of the proposed approach resulted in accuracies higher than 95% on different road types, comparable to those of the state-of-the-art techniques. In addition to marginally reducing the inference time of the used DCNN on images with a resolution of 1248 × 352 pixels to 130 ms using an NVIDIA GTX-1080TI.
We propose a new approach to image compression based on the principle of Shapiro's embedded zero-tree wavelet (EZW) algorithm. Our approach, the efficient EZW (E-EZW), uses six symbols instead of the four used in the original Shapiro's algorithm to minimize the redundant symbols, and optimizes the coding by binary regrouping of the information. This approach can produce results that have a significant improvement over the peak signal-to-noise ratio and compression ratio obtained by Shapiro without affecting the computing time. These results are also comparable to those obtained using the set partitioning in hierarchical trees (SPIHT), set partitioning embedded block (SPECK), and JPEG2000 algorithms.
This paper describes an ultrasonic spatial localization system for a sonometric probe, to build 3D images of a fetus. The main objective of such a system is a medical diagnostic help. A method to improve accuracy of ultrasonic telemeter is developed and gives us encouraging results. We arrive to measure a distance with accuracy around 0.6 mm for 1.5 meters ranging. To aim a localization accuracy less than 0.1 mm, we work on this system with better techniques like programmable analogic device and numerical systems.
The problem of the choice of slices angles, at the time of diagnosis of brain fetal malformations, is linked to the position of the fetus inside the uterus. The 3D reconstruction of intern parts of the brain and especially the callosus corpus can help to detect some malformations. This kind of reconstruction pass by several steps that depend all on the initial segmentation step. The main difficulties of the segmentation are linked on the one hand to the inherent noise of ultrasound imaging and on the other hand to the matching of views of the 2D sequence to process. The 3D reconstruction stage require the definition of a marker in the sequence of process. In agreement with physicians, we have used the cranial contour as reference on the one hand because it is considered as invariable and fixed and on the other hand because of its more pronounced contrast (due to the fact of its cartilaginous nature) than the other structures. Nevertheless, the classic techniques of segmentations have remained without effect (open contour, too noisy). Therefore, we have developed an algorithm allowing to define automatically the ellipse. This method is based on a parametrically deformable model using elliptic FOURIER decomposition.
KEYWORDS: 3D modeling, Laser scanners, 3D scanning, 3D acquisition, Image segmentation, Control systems, Statistical modeling, Visual process modeling, Image processing, Scanners
One of the main problems in a 3D computer-aided reconstruction lies in the fact that the obtained result depends on the cross sections acquisition way. If the interslice distance between successive contours is greater than the in-plane resolution, we can generate a coarse representation of the physical scanned object. In this paper, we present an elastic contour interpolation scheme to refine 3D object reconstruction from serial cross sections.
A new method for automatically reconstructing a three dimensional object from a serial cross section is presented in this paper. The method combines the technical chaining contour and the sampling method to construct an object. In the proposed method the initial description of the object is formed by a serial continuous contour. First we sample each cross section to determine the nods that constitute the vertices of the triangles used for the reconstruction, then we determine the tangent in these nods and we assign to each one of them a code depending on the tangent direction. For triangulation there is nothing left to do but collect the nods of two adjacent contours the code of which is identical.
We are suggesting a new approach to NMR image segmentation for multiple scleroses volume quantification. The choice of a segmentation technique is conditioned by what is known in NMR acquisition artifacts. This research has been conducted in collaboration with the Lille Neuroradiology Laboratory leading to the implementation of a disease development index.
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