J. Imaging, Volume 2, Issue 1 (March 2016) – 9 articles

The Suomi National Polar-orbiting (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) Day-Night Band (DNB) measures visible and near-infrared light extending over seven orders of magnitude of dynamic range. This makes radiometric calibration difficult. We have observed that DNB imagery has striping, banding and other nonuniformities—day or night. We identified the causes as stray light, nonlinearity, detector crosstalk, hysteresis and mirror-side variation. We found that these affect both Earth-view and calibration signals. These present an obstacle to interpretation by users of DNB products. Because of the nonlinearity we chose the histogram matching destriping technique which we found is successful for daytime, twilight and nighttime scenes. Because of the very large dynamic range of the DNB, we needed to add special processes to the histogram matching to destripe all scenes, especially imagery in the twilight regions where scene illumination changes rapidly over short distances. We show that destriping aids image analysts, and makes it possible for advanced automated cloud typing algorithms. Manual or automatic identification of other features, including polar ice and gravity waves in the upper atmosphere are also discussed. In consideration of the large volume of data produced 24 h a day by the VIIRS DNB, we present methods for reducing processing time. Full article

The deburring processes of parts with complex geometries usually present many challenges to be automated. This paper outlines the machine vision techniques involved in the design and set up of an automated adaptive cognitive robotic system for laser deburring of metal casting complex 3D high quality parts. To carry out deburring process operations of the parts autonomously, 3D machine vision techniques have been used for different purposes, explained in this paper. These machine vision algorithms used along with industrial robots and a high tech laser head, make a fully automated deburring process possible. This setup could potentially be applied to medium sized parts of different light casting alloys (Mg, AlZn, etc.). Full article

In hyperspectral images, once the pure spectra of the materials are known, hyperspectral unmixing seeks to find their relative abundances throughout the scene. We present a novel variational model for hyperspectral unmixing from incomplete noisy data, which combines a spatial regularity prior with the knowledge of the pure spectra. The material abundances are found by minimizing the resulting convex functional with a primal dual algorithm. This extends least squares unmixing to the case of incomplete data, by using total variation regularization and masking of unknown data. Numerical tests with artificial and real-world data demonstrate that our method successfully recovers the true mixture coefficients from heavily-corrupted data. Full article

In this paper, an algorithm for obstacle detection in agricultural fields is presented. The algorithm is based on an existing deep convolutional neural net, which is fine-tuned for detection of a specific obstacle. In ISO/DIS 18497, which is an emerging standard for safety of highly automated machinery in agriculture, a barrel-shaped obstacle is defined as the obstacle which should be robustly detected to comply with the standard. We show that our fine-tuned deep convolutional net is capable of detecting this obstacle with a precision of 99 . 9 % in row crops and 90 . 8 % in grass mowing, while simultaneously not detecting people and other very distinct obstacles in the image frame. As such, this short note argues that the obstacle defined in the emerging standard is not capable of ensuring safe operations when imaging sensors are part of the safety system. Full article

3D sensors such as lidars, stereo cameras, time-of-flight cameras, and the Microsoft Kinect are increasingly found in a wide range of applications, including gaming, personal robotics, and space exploration. In some cases, pattern recognition algorithms for processing depth images can be tested using actual sensors observing real-world objects. In many situations, however, it is common to test new algorithms using computer-generated synthetic images, as such simulations tend to be faster, more flexible, and less expensive than hardware tests. Computer generation of images is especially useful for Monte Carlo-type analyses or for situations where obtaining real sensor data for preliminary testing is difficult (e.g., space applications). We present Glidar, an OpenGL and GL Shading Language-based sensor simulator, capable of imaging nearly any static three-dimensional model. Glidar allows basic object manipulations, or may be connected to a physics simulator for more advanced behaviors. It permits publishing to a tcp socket at high frame-rates or can save to pcd (point cloud data) files. The software is written in C++, and is released under the open source bsd license. Full article

The increasing energy demand in recent years has resulted in a continuous growing interest in renewable energy sources, such as efficient and high-yielding energy crops. Energy sorghum is a crop that has shown great potential in this area, but needs further improvement. Plant phenotyping—measuring physiological characteristics of plants—is a laborious and time-consuming task, but it is essential for crop breeders as they attempt to improve a crop. The development of high-throughput phenotyping (HTP)—the use of autonomous sensing systems to rapidly measure plant characteristics—offers great potential for vastly expanding the number of types of a given crop plant surveyed. HTP can thus enable much more rapid progress in crop improvement through the inclusion of more genetic variability. For energy sorghum, stalk thickness is a critically important phenotype, as the stalk contains most of the biomass. Imaging is an excellent candidate for certain phenotypic measurements, as it can simulate visual observations. The aim of this study was to evaluate image analysis techniques involving K-means clustering and minimum-distance classification for use on red-green-blue (RGB) images of sorghum plants as a means to measure stalk thickness. Additionally, a depth camera integrated with the RGB camera was tested for the accuracy of distance measurements between camera and plant. Eight plants were imaged on six dates through the growing season, and image segmentation, classification and stalk thickness measurement were performed. While accuracy levels with both image analysis techniques needed improvement, both showed promise as tools for HTP in sorghum. The average error for K-means with supervised stalk measurement was 10.7% after removal of known outliers. Full article

The performance and durability of electrochemical power systems are determined by a complex interdependency of many complex and interrelated factors, temperature and heat transfer being particularly important. This has led to an increasing interest in the use of thermal imaging to understand both the fundamental phenomena and effects of operation on the temperature distribution and dynamics in these systems. This review describes the application thermal imaging and related techniques to the study of electrochemical power systems with the primary focus on fuel cells and batteries. Potential opportunities and directions for future research are also highlighted, indicating the wide scope for further insights to be gleaned using infrared thermal imaging techniques. Full article

The main objective of this paper is to investigate the effectiveness of two recently popular non-parametric models for aboveground biomass (AGB) retrieval from Synthetic Aperture Radar (SAR) L-band backscatter intensity and coherence images. An area in Siberian boreal forests was selected for this study. The results demonstrated that relatively high estimation accuracy can be obtained at a spatial resolution of 50 m using the MaxEnt and the Random Forests machine learning algorithms. Overall, the AGB estimation errors were similar for both tested models (approximately 35 t∙ha⁻¹). The retrieval accuracy slightly increased, by approximately 1%, when the filtered backscatter intensity was used. Random Forests underestimated the AGB values, whereas MaxEnt overestimated the AGB values. Full article