Compressively Sensed Image Recognition

Compressive Sensing (CS) theory asserts that sparse signal reconstruction is possible from a small number of linear measurements. Although CS enables low-cost linear sampling, it requires non-linear and costly reconstruction. Recent literature works show that compressive image classification is possible in CS domain without reconstruction of the signal. In this work, we introduce a DCT base method that extracts binary discriminative features directly from CS measurements. These CS measurements can be obtained by using (i) a random or a pseudo-random measurement matrix, or (ii) a measurement matrix whose elements are learned from the training data to optimize the given classification task. We further introduce feature fusion by concatenating Bag of Words (BoW) representation of our binary features with one of the two state-of-the-art CNN-based feature vectors. We show that our fused feature outperforms the state-of-the-art in both cases.Comment: 6 pages, submitted/accepted, EUVIP 201

MICCS: A Novel Framework for Medical Image Compression Using Compressive Sensing

The vision of some particular applications such as robot-guided remote surgery where the image of a patient body will need to be captured by the smart visual sensor and to be sent on a real-time basis through a network of high bandwidth but yet limited. The particular problem considered for the study is to develop a mechanism of a hybrid approach of compression where the Region-of-Interest (ROI) should be compressed with lossless compression techniques and Non-ROI should be compressed with Compressive Sensing (CS) techniques. So the challenge is gaining equal image quality for both ROI and Non-ROI while overcoming optimized dimension reduction by sparsity into Non-ROI. It is essential to retain acceptable visual quality to Non-ROI compressed region to obtain a better reconstructed image. This step could bridge the trade-off between image quality and traffic load. The study outcomes were compared with traditional hybrid compression methods to find that proposed method achieves better compression performance as compared to conventional hybrid compression techniques on the performances parameters e.g. PSNR, MSE, and Compression Ratio

Compressive Light Field Reconstruction using Deep Learning

abstract: Light field imaging is limited in its computational processing demands of high sampling for both spatial and angular dimensions. Single-shot light field cameras sacrifice spatial resolution to sample angular viewpoints, typically by multiplexing incoming rays onto a 2D sensor array. While this resolution can be recovered using compressive sensing, these iterative solutions are slow in processing a light field. We present a deep learning approach using a new, two branch network architecture, consisting jointly of an autoencoder and a 4D CNN, to recover a high resolution 4D light field from a single coded 2D image. This network decreases reconstruction time significantly while achieving average PSNR values of 26-32 dB on a variety of light fields. In particular, reconstruction time is decreased from 35 minutes to 6.7 minutes as compared to the dictionary method for equivalent visual quality. These reconstructions are performed at small sampling/compression ratios as low as 8%, allowing for cheaper coded light field cameras. We test our network reconstructions on synthetic light fields, simulated coded measurements of real light fields captured from a Lytro Illum camera, and real coded images from a custom CMOS diffractive light field camera. The combination of compressive light field capture with deep learning allows the potential for real-time light field video acquisition systems in the future.Dissertation/ThesisMasters Thesis Computer Engineering 201

Computer Vision from Spatial-Multiplexing Cameras at Low Measurement Rates

abstract: In UAVs and parking lots, it is typical to first collect an enormous number of pixels using conventional imagers. This is followed by employment of expensive methods to compress by throwing away redundant data. Subsequently, the compressed data is transmitted to a ground station. The past decade has seen the emergence of novel imagers called spatial-multiplexing cameras, which offer compression at the sensing level itself by providing an arbitrary linear measurements of the scene instead of pixel-based sampling. In this dissertation, I discuss various approaches for effective information extraction from spatial-multiplexing measurements and present the trade-offs between reliability of the performance and computational/storage load of the system. In the first part, I present a reconstruction-free approach to high-level inference in computer vision, wherein I consider the specific case of activity analysis, and show that using correlation filters, one can perform effective action recognition and localization directly from a class of spatial-multiplexing cameras, called compressive cameras, even at very low measurement rates of 1\%. In the second part, I outline a deep learning based non-iterative and real-time algorithm to reconstruct images from compressively sensed (CS) measurements, which can outperform the traditional iterative CS reconstruction algorithms in terms of reconstruction quality and time complexity, especially at low measurement rates. To overcome the limitations of compressive cameras, which are operated with random measurements and not particularly tuned to any task, in the third part of the dissertation, I propose a method to design spatial-multiplexing measurements, which are tuned to facilitate the easy extraction of features that are useful in computer vision tasks like object tracking. The work presented in the dissertation provides sufficient evidence to high-level inference in computer vision at extremely low measurement rates, and hence allows us to think about the possibility of revamping the current day computer systems.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Leveraging Spatial Patterning for Optical Sensing

The essence of active optical sensors can be distilled into three modules: the illumination, the system under study, and the detection. Just as constraints or requirements on one of these components can impose limitations on what can be sensed, enhancements to any of these modules can facilitate measurements which were previously challenging or impossible. In this thesis, we investigate how spatial patterns can be leveraged at each of the modules of an active optical sensor to simplify experiments and enable new measurements. We present three projects that enhance optical sensing capabilities via spatial patterning at one of the modules. Each project corresponds to a chapter in this thesis. The first project patterns the illumination to develop a velocimeter for the study of fluid mechanics. Drawing from mature technologies which infer velocities from particle transits through linear interference fringes, we extend the capabilities of velocimeters by altering the shapes of the interference fringes and developing a supporting signal processing framework. We demonstrate the approach to perform direct angular velocity measurements. This approach represents an interesting new direction for fluid flow velocimetry which can be extended to sense other flow parameters by selecting different light structures. The second project patterns the system under study. We present a strategy for aligning a beam of light to the axis of rotation of a spinning surface by patterning the surface and monitoring the scattered light as the surface rotates. This technique is simple and inexpensive, and can be implemented by adhering a strip of tape to the surface to achieve alignment accurate to within the uncertainty of the benchmark measurement, &plusmn;2.9% of the beam waist, limited by the size of the optical components used in the experiments. The third project focuses on patterning the detection system. Working at the nexus of dual frequency comb spectroscopy and compressive sensing, we merge the capabilities of these tools to perform the first hyperspectral dual-comb imaging. Programmable transmission masks imprint patterns on the light just before detection. Multiple measurements, each conducted with a different transmission mask, provide a means to reconstruct information encoded across the transverse extent of the beam. For each project, complex alternatives exist to measure similar quantities. For example, the angular velocities of fluids can be deduced from linear velocity fields, alignment to spinning disks can be ensured by mounting diffraction gratings to their surfaces and studying the light diffracted from them, and hyperspectral images can be generated by scanning a dual-comb laser beam. Yet, we find that careful consideration of how information can be encoded in spatial patterns can not only readily yield these quantities of interest, but may do so more effectively than introducing more complicated experimental techniques.</p