Hyperspectral imaging is used in various fields because it can obtain much more information than imaging by conventional RGB cameras. Hyperspectral imaging systems using active illumination, prisms, gratings, or narrowband filters have been proposed. Active illumination systems can obtain two-dimensional (2D) spectral images rapidly, and the device can be low-cost and small because of the use of LEDs. However, flicker can occur when different colors of LEDs are switched. The other methods do not have the flicker problem because they use passive imaging. However, these systems take a long time to acquire the 2D spectral images, or they tend to be high-cost or large. In our research, we propose a flickerless active LED illumination system for hyperspectral imaging. This system acquires images while switching the illumination. The switching illumination consists of many narrowband LEDs that have different spectrums. The spectral images of each LED are reconstructed from the acquired images. The switching illumination is designed to reduce the flicker based on human visual characteristics. We reduce the color changes of the switching illumination while maintaining its spectral differences. In the experiment, we obtain the optimal design of a flickerless illumination system for measuring oxygen saturation. To show the feasibility of our system, we clearly show the difference in saturation using the spectral images obtained by a prototype designed using the proposed method.
An infrared (IR) camera captures the temperature distribution of an object as an IR image. Because facial temperature is almost constant, an IR camera has the potential to be used in detecting facial regions in IR images. However, in detecting faces, a simple temperature thresholding does not always work reliably. The standard face detection algorithm used is AdaBoost with local features, such as Haar-like, MB-LBP, and HoG features in the visible images. However, there are few studies using these local features in IR image analysis. In this paper, we propose an AdaBoost-based training method to mix these local features for face detection in thermal images. In an experiment, we captured a dataset from 20 participants, comprising 14 males and 6 females, with 10 variations in camera distance, 21 poses, and participants with and without glasses. Using leave-one-out cross-validation, we show that the proposed mixed features have an advantage over all the regular local features.
Several recent studies in compressive video sensing have realized scene capture beyond the fundamental trade-off limit between spatial resolution and temporal resolution using random space-time sampling. However, most of these studies showed results for higher frame rate video that were produced by simulation experiments or using an optically simulated random sampling camera, because there are currently no commercially available image sensors with random exposure or sampling capabilities. We fabricated a prototype complementary metal oxide semiconductor (CMOS) image sensor with quasi pixel-wise exposure timing that can realize nonuniform space-time sampling. The prototype sensor can reset exposures independently by columns and fix these amount of exposure by rows for each 8x8 pixel block. This CMOS sensor is not fully controllable via the pixels, and has line-dependent controls, but it offers flexibility when compared with regular CMOS or charge-coupled device sensors with global or rolling shutters. We propose a method to realize pseudo-random sampling for high-speed video acquisition that uses the flexibility of the CMOS sensor. We reconstruct the high-speed video sequence from the images produced by pseudo-random sampling using an over-complete dictionary.
It is desirable to engineer a small camera with a wide field of view (FOV) because of current developments in the field of wearable cameras and computing products, such as action cameras and Google Glass. However, typical approaches for achieving wide FOV, such as attaching a fisheye lens and convex mirrors, require a trade-off between optics size and the FOV. We propose camera optics that achieve a wide FOV, and are at the same time small and lightweight. The proposed optics are a completely lensless and catoptric design. They contain four mirrors, two for wide viewing, and two for focusing the image on the camera sensor. The proposed optics are simple and can be simply miniaturized, since we use only mirrors for the proposed optics and the optics are not susceptible to chromatic aberration. We have implemented the prototype optics of our lensless concept. We have attached the optics to commercial charge-coupled device/complementary metal oxide semiconductor cameras and conducted experiments to evaluate the feasibility of our proposed optics.
Conference Committee Involvement (1)
The International Conference on Quality Control by Artificial Vision 2017