Scalable Fire and Smoke Segmentation from Aerial Images Using Convolutional Neural Networks and Quad-Tree Search.

Autonomous systems can help firefighting operations by detecting and locating the fire spot from surveillance images and videos. Similar to many other areas of computer vision, Convolutional Neural Networks (CNNs) have achieved state-of-the-art results for fire and smoke detection and segmentation. In practice, input images to a CNN are usually downsized to fit into the network to avoid computational complexities and restricted memory problems. Although in many applications downsizing is not an issue, in the early phases of fire ignitions downsizing may eliminate the fire regions since the incident regions are small. In this paper, we propose a novel method to segment fire and smoke regions in high resolution images based on a multi-resolution iterative quad-tree search algorithm , which manages the application of classification and segmentation CNNs to focus the attention on informative parts of the image. The proposed method is more computationally efficient compared to processing the whole high resolution input, and contains parameters that can be tuned based on the needed scale precision. The results show that the proposed method is capable of detecting and segmenting fire and smoke with higher accuracy and is useful for segmenting small regions of incident in high resolution aerial images in a computationally efficient way.

External Patch-Based Image Restoration Using Importance Sampling.

This paper introduces a new approach to patchbased image restoration based on external datasets and importance sampling. The minimum mean squared error (MMSE) estimate of the image patches, the computation of which requires solving a multidimensional (typically intractable) integral, is approximated using samples from an external dataset. The new method, which can be interpreted as a generalization of the external non-local means (NLM), uses self-normalized importance sampling to efficiently approximate the MMSE estimates. The use of self-normalized importance sampling endows the proposed method with great flexibility, namely regarding the statistical properties of the measurement noise. The effectiveness of the proposed method is shown in a series of experiments using both generic large-scale and class-specific external datasets.

Image Restoration Using Gaussian Mixture Models With Spatially Constrained Patch Clustering.

In this paper, we address the problem of recovering degraded images using multivariate Gaussian mixture model (GMM) as a prior. The GMM framework in our method for image restoration is based on the assumption that the accumulation of similar patches in a neighborhood are derived from a multivariate Gaussian probability distribution with a specific covariance and mean. Previous methods of image restoration with GMM have not considered spatial (geometric) distance between patches in clustering. Our conducted experiments show that in the case of constraining Gaussian estimates into a finite-sized windows, the patch clusters are more likely to be derived from the estimated multivariate Gaussian distributions, i.e., the proposed statistical patch-based model provides a better goodness-of-fit to statistical properties of natural images. A novel approach for computing aggregation weights for image reconstruction from recovered patches is introduced which is based on similarity degree of each patch to the estimated Gaussian clusters. The results admit that in the case of image denoising, our method is highly comparable with the state-of-the-art methods, and our image interpolation method outperforms previous state-of-the-art methods.