Abstract In this paper, the insulator string images captured by Using unmanned aerial vehicles (UAVs) in power inspection are taken as research object. The image processing and deep learning methods used to label images, self-explosion fault of identified segmented strings is located. process semantic segmentation given data set enhanced, exchanged with algorithms. Based on SegNet, each pixel p...

As for terrestrial remote sensing, pixel-based classifiers have traditionally been used to map coral reef habitats. For pixel-based classifiers, habitat assignment is based on the spectral or textural properties of each individual pixel in the scene. More recently, however, object-based classifications, those based on information from a set of contiguous pixels with similar properties, have fou...

Remote sensing data from hyperspectral cameras suffer limited spatial resolution, in which a single pixel of image may contain information several materials the field view. Blind unmixing is process identifying pure spectra individual (i.e., endmembers) and their proportions abundances) at each pixel. In this article, we propose novel blind model based on graph total variation (gTV) regularizat...

The accuracy of data captured by sensors highly impacts the performance a computer vision system. To derive accurate data, system must be capable identifying critical objects and activities in field reconfiguring configuration space real time. majority modern reconfiguration systems rely on complex computations thus consume lots resources. This may not problem for with continuous power supply, ...

Confusion matrix and derived global indices (kappa, overall accuracy, producer accuracy) are widely accepted as a standard method for the accuracy assessment of land use/land cover maps. In order to build the confusion matrix, the ground truth labels of samples are crossed with the map labels. Most of the time, the sampling strategies are simply based on the spatial distribution of sample point...

We propose an end-to-end learning framework for foreground object segmentation. Given a single novel image, our approach produces a pixel-level mask for all “object-like” regions—even for object categories never seen during training. We formulate the task as a structured prediction problem of assigning a foreground/background label to each pixel, implemented using a deep fully convolutional net...

There has been a rapid technical progress in three-dimensional (3D) computer graphics. But gathering surface and texture data is yet a laborious task. This paper addresses the problem of mapping photographic images on the surface of a 3D object whose geometric data are already known. We propose an efficient and handy method for acquiring textures and mapping them precisely on the surface, emplo...

This study investigated the mechanisms of grouping and segregation in natural scenes of close-up foliage, an important class of scenes for human and non-human primates. Close-up foliage images were collected with a digital camera calibrated to match the responses of human L, M, and S cones at each pixel. The images were used to construct a database of hand-segmented leaves and branches that cor...

We present the SAMMI lightweight object detection method which has a high level of accuracy and robustness, and which is able to operate in an environment with a large number of cameras. Background modeling is based on DCT coefficients provided by cameras. Foreground detection uses similarity in temporal characteristics of adjacent blocks of pixels, which is a computationally inexpensive way to...