Fast object classification in single-pixel imaging

Abstract

In single-pixel imaging (SPI), the target object is illuminated with varying patterns sequentially and an intensity sequence is recorded by a single-pixel detector without spatial resolution. A high quality object image can only be computationally reconstructed after a large number of illuminations, with disadvantages of long imaging time and high cost. Conventionally, object classification is performed after a reconstructed object image with good fidelity is available. In this paper, we propose to classify the target object with a small number of illuminations in a fast manner for Fourier SPI. A naive Bayes classifier is employed to classify the target objects based on the single-pixel intensity sequence without any image reconstruction and each sequence element is regarded as an object feature in the classifier. Simulation results demonstrate our proposed scheme can classify the number digit object images with high accuracy (e.g. 80% accuracy using only 13 illuminations, at a sampling ratio of 0.3%).

SP-ILC: Concurrent Single-Pixel Imaging, Object Location, and Classification by Deep Learning

“…The recall was 1.000 (40/40). This classification performance matches that of current state-of-the-art classification tasks [34,37]. We note that in previous studies of the single-pixel classification there is prior knowledge that a scene contains only one object.…”

“…SP-ILC is the first work that can locate and classify multiple objects and objects of different sizes and overlap using a single-pixel detector, although there are previous studies that achieved the single-pixel classification for the scene with a single object and even the object is high-speed moving [34,37]. The metric for these works for single object classification is accuracy.…”

“…Lyu et al [33] proposed a method of computational ghost imaging based on machine learning in 2017; they were able to recreate high quality images at low sampling rates. Jiao [34] used the S-vector of a single-pixel camera for object classification without the use of images for training or classification in 2018. In addition, in 2018, Higham et al [35] used deep learning for single-pixel imaging at a frequency that enabled the detection of video images.…”

Online reconstruction-free single-pixel image classification

“…Recently, a naive Bayes classifier is applied on SPI using Fourier-based sinusoidal patterns and 1D Gaussian distributions [12] instead of the multidimensional Gaussians considered before [7]. In the context of both reconstruction and classification in the compressive domain, the use of side information is shown to improve performance [13].…”

Evaluation of a time-gated-single-pixel-camera as a promising sensor for autonomous vehicles in harsh weather conditions

“…direct classification on the single-pixel signal without reconstruction of an image, was given by Davenport et al very early after CS-theory was formulated, but was mostly unnoticed at the time [19]. Recently, image-free classification picks up interest again, due to the possibility of using neural network classifiers on the single-pixel signal [20,21]. Yang et al even proposed a scheme to classify, locate and reconstruct an image out of the single-pixel signal with one single neural network [22].…”