Boris Xavier Vintimilla

Feature Point Descriptors: Infrared and Visible Spectra

This manuscript evaluates the behavior of classical feature point descriptors when they are used in images from long-wave infrared spectral band and compare them with the results obtained in the visible spectrum. Robustness to changes in rotation, scaling, blur, and additive noise are analyzed using a state of the art framework. Experimental results using a cross-spectral outdoor image data set are presented and conclusions from these experiments are given.

Efficient approximation of gray-scale images through bounded error triangular meshes

This paper presents an iterative algorithm for approximating gray-scale images with adaptive triangular meshes ensuring a given tolerance. At each iteration, the algorithm applies a non-iterative adaptive meshing technique. In this way, this technique converges faster than traditional mesh refinement algorithms. The performance of the proposed technique is studied in terms of compression ratio and speed, comparing it with an optimization-based mesh refinement algorithm.

Acceleration of filtering and enhancement operations through geometric processing of gray-level images

This paper describes an algorithm to implement image filtering and enhancement operations by processing adaptive triangular meshes that represent gray-level images. Experimental results show that these operations are significantly more efficient when they are performed upon triangular meshes than by sequentially processing all the pixels contained in the given images.

Wavelet-Based Visible and Infrared Image Fusion: A Comparative Study.

This paper evaluates different wavelet-based cross-spectral image fusion strategies adopted to merge visible and infrared images. The objective is to find the best setup independently of the evaluation metric used to measure the performance. Quantitative performance results are obtained with state of the art approaches together with adaptations proposed in the current work. The options evaluated in the current work result from the combination of different setups in the wavelet image decomposition stage together with different fusion strategies for the final merging stage that generates the resulting representation. Most of the approaches evaluate results according to the application for which they are intended for. Sometimes a human observer is selected to judge the quality of the obtained results. In the current work, quantitative values are considered in order to find correlations between setups and performance of obtained results; these correlations can be used to define a criteria for selecting the best fusion strategy for a given pair of cross-spectral images. The whole procedure is evaluated with a large set of correctly registered visible and infrared image pairs, including both Near InfraRed (NIR) and Long Wave InfraRed (LWIR).

Edge Point Linking by Means of Global and Local Schemes

This chapter presents an efficient technique for linking edge points in order to generate a closed contour representation. The original intensity image, as well as its corresponding edge map, are assumed to be given as input to the algorithm (i.e., an edge map is previously computed by some of the classical edge detector algorithms). The proposed technique consists of two stages. The first stage computes an initial representation by connecting edge points according to a global measure. It relies on the use of graph theory. Spurious edge points are removed by a morphological filter. The second stage finally generates closed contours, linking unconnected edges, by using a local cost function. Experimental results with different intensity images are presented.3

Towards fault tolerant perception for autonomous vehicles: Local fusion

Many robust sensor fusion strategies have been developed in order to reliably detect the surrounding environments of an autonomous vehicle. However, in real situations there is always the possibility that sensors or other components may fail. Thus, internal modules and sensors need to be monitored to ensure their proper function. This paper introduces a general view of a perception architecture designed to detect and classify obstacles in an autonomous vehicles environment using a fault tolerant framework, whereas elaborates the object detection and local fusion modules proposed in order to achieve the modularity and real-time process required by the system.

Learning to Colorize Infrared Images

This paper focuses on near infrared (NIR) image colorization by using a Generative Adversarial Network (GAN) architecture model. The proposed architecture consists of two stages. Firstly, it learns to colorize the given input, resulting in a RGB image. Then, in the second stage, a discriminative model is used to estimate the probability that the generated image came from the training dataset, rather than the image automatically generated. The proposed model starts the learning process from scratch, because our set of images is very different from the dataset used in existing pre-trained models, so transfer learning strategies cannot be used. Infrared image colorization is an important problem when human perception need to be considered, e.g., in remote sensing applications. Experimental results with a large set of real images are provided showing the validity of the proposed approach.

A Visible-Thermal Fusion Based Monocular Visual Odometry

The manuscript evaluates the performance of a monocular visual odometry approach when images from different spectra are considered, both independently and fused. The objective behind this evaluation is to analyze if classical approaches can be improved when the given images, which are from different spectra, are fused and represented in new domains. The images in these new domains should have some of the following properties: i) more robust to noisy data; ii) less sensitive to changes (e.g., lighting); iii) more rich in descriptive information, among other. In particular in the current work two different image fusion strategies are considered. Firstly, images from the visible and thermal spectrum are fused using a Discrete Wavelet Transform (DWT) approach. Secondly, a monochrome threshold strategy is considered. The obtained representations are evaluated under a visual odometry framework, highlighting their advantages and disadvantages, using different urban and semi-urban scenarios. Comparisons with both monocular-visible spectrum and monocular-infrared spectrum, are also provided showing the validity of the proposed approach.

Cross-spectral image patch similarity using convolutional neural network

The ability to compare image regions (patches) has been the basis of many approaches to core computer vision problems, including object, texture and scene categorization. Hence, developing representations for image patches have been of interest in several works. The current work focuses on learning similarity between cross-spectral image patches with a 2 channel convolutional neural network (CNN) model. The proposed approach is an adaptation of a previous work, trying to obtain similar results than the state of the art but with a low-cost hardware. Hence, obtained results are compared with both classical approaches, showing improvements, and a state of the art CNN based approach.

A Dijkstra-Based Algorithm for Selecting the Shortest-Safe Evacuation Routes in Dynamic Environments (SSER)

In this work is proposed an approach for addressing the problem to find the shortest-safe routes in buildings with many evacuation doors and where the accessibility of internal areas could be changed by different kind of sensors. We present two advantages over the common use of Dijkstra’s algorithm, related to the problem of obtaining evacuation routes: (1) Fast search of the shortest-safe evacuation route to multiple exits with a backward approach and (2) Support to dynamic environments (graph with variable vertex availability). Four Dijkstra-based algorithms were considered in order to evaluate the performance of the proposed approach, achieving short times in evacuation to multiple exits.