Sparse Representation for Paddy Plants Nutrient Deficiency Tracking System

Moving object detection and tracking from consecutive frames of sensing devices (Unmanned Aerial Vehicles-UAV) needs efficient sampling from mass data with sufficient memory saving. Objects with super pixels are tracked by Compressive Sensing (CS) and the generative structural part model is designed to be adaptive to variation of deformable objects. CS can precisely reconstruct sparse signal with a small amount of sampling data. This system creates the sparse representation (SR) dictionary representing the nutrient deficiency tracking system for paddy plants to support the healthily growth of the whole field. This system uses compressed domain features that can be exploited to map the semantic features of consecutive frames. As the CS is a developing signal processing technique, a sparse signal is reconstructed with efficient sampling rate and creates the sparse dictionary. The SR for paddy plant health system can build rich information about paddy plants from signaling devices and can alert the deficiency conditions accurately in real time

Efficient Space-Time Sampling with Pixel-wise Coded Exposure for High Speed Imaging

Cameras face a fundamental tradeoff between spatial and temporal resolution. Digital still cameras can capture images with high spatial resolution, but most high-speed video cameras have relatively low spatial resolution. It is hard to overcome this tradeoff without incurring a significant increase in hardware costs. In this paper, we propose techniques for sampling, representing and reconstructing the space-time volume in order to overcome this tradeoff. Our approach has two important distinctions compared to previous works: (1) we achieve sparse representation of videos by learning an over-complete dictionary on video patches, and (2) we adhere to practical hardware constraints on sampling schemes imposed by architectures of current image sensors, which means that our sampling function can be implemented on CMOS image sensors with modified control units in the future. We evaluate components of our approach - sampling function and sparse representation by comparing them to several existing approaches. We also implement a prototype imaging system with pixel-wise coded exposure control using a Liquid Crystal on Silicon (LCoS) device. System characteristics such as field of view, Modulation Transfer Function (MTF) are evaluated for our imaging system. Both simulations and experiments on a wide range of scenes show that our method can effectively reconstruct a video from a single coded image while maintaining high spatial resolution

Imaging via Three-dimensional Compressive Sampling (3DCS) ∗

Compressive sampling (CS) aims at acquiring a signal at a sampling rate that is significantly below the Nyquist rate. Its main idea is that a signal can be decoded from incomplete linear measurements by seeking its sparsity in some domain. Despite the remarkable progress in the theory of CS, little headway has been made in the compressive imaging (CI) camera. In this paper, a three-dimensional compressive sampling (3DCS) approach is proposed to reduce the required sampling rate of the CI camera to a practical level. In 3DCS, a generic three-dimensional sparsity measure (3DSM) is presented, which decodes a video from incomplete samples by exploiting its 3D piecewise smoothness and temporal low-rank property. In addition, an efficient decoding algorithm is developed for this 3DSM with guaranteed convergence. The experimental results show that our 3DCS requires a much lower sampling rate than the existing CS methods without compromising recovery accuracy. 1