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The fusion of stereo and laser range finders (LIDARs) has been proposed as a method to compensate for each individual sensor's deficiencies - stereo output is dense, but noisy for large distances, while LIDAR is more accurate, but sparse. However, stereo usually performs poorly on textureless areas and on scenes containing repetitive structures, and the subsequent fusion with LIDAR leads to a degraded...
Phase contrast and differential interference contrast, which are used to capture microscopy images of living cells, contain a few artifacts such as halo and shadow-cast effect. Removing these artifacts from microscopy images facilitates automated microscopy image analysis, such as the cell segmentation that is a critical step in cell tracking systems. We propose a restoration algorithm based on the...
Detecting the boundaries of objects is a key step in separating foreground objects from the background, which is useful for robotics and computer vision applications, such as object detection, recognition, and tracking. We propose a new method for detecting object boundaries using planar laser scanners (LIDARs) and, optionally, co-registered imagery. We formulate boundary detection as a classification...
We present a method for robustly detecting hematopoietic stem cells (HSCs) in phase contrast microscopy images. HSCs appear to be easy to detect since they typically appear as round objects. However, when HSCs are touching and overlapping, showing the variations in shape and appearance, standard pattern detection methods, such as Hough transform and correlation, do not perform well. The proposed method...
Cell segmentation in microscopy imagery is essential for many bioimage applications such as cell tracking. To segment cells from the background accurately, we present a pixel classification approach that is independent of cell type or imaging modality. We train a set of Bayesian classifiers from clustered local training image patches. Each Bayesian classifier is an expert to make decision in its specific...
We describe a novel camera calibration algorithm for square, circle, and ring planar calibration patterns. An iterative refinement approach is proposed that utilizes the parameters obtained from traditional calibration algorithms as initialization to perform undistortion and unprojection of calibration images to a canonical fronto-parallel plane. This canonical plane is then used to localize the calibration...
A GPU becomes an affordable solution for accelerating a slow process on a commercial system. The most of achievements using it for non-rendering problems are the exact re-implementation of existing algorithms designed for a serial CPU. We study about conditions of a good parallel algorithm, and show that it is possible to design an algorithm targeted to a parallel hardware, though it may be useless...
We propose a new data-driven framework for novel object detection and segmentation, or ??object pop-out??. Traditionally, this task is approached via background subtraction, which requires continuous observation from a stationary camera. Instead, we consider this an image matching problem. We detect novel objects in the scene using an unordered, sparse database of previously captured images of the...
This paper presents a target tracking framework for unstructured crowded scenes. Unstructured crowded scenes are defined as those scenes where the motion of a crowd appears to be random with different participants moving in different directions over time. This means each spatial location in such scenes supports more than one, or multi-modal, crowd behavior. The case of tracking in structured crowded...
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