We have embedded Adaptive Compressive Sensing (ACS) algorithm on Charge-Coupled-Device (CCD) camera
based on the simplest concept that each pixel is a charge bucket, and the charges comes from Einstein photoelectric
conversion effect. Applying the manufactory design principle, we only allow altering each working component at a
minimum one step. We then simulated what would be such a camera can do for real world persistent surveillance
taking into account of diurnal, all weather, and seasonal variations. The data storage has saved immensely, and the
order of magnitude of saving is inversely proportional to target angular speed. We did design two new components
of CCD camera. Due to the matured CMOS (Complementary metal–oxide–semiconductor) technology, the on-chip
Sample and Hold (SAH) circuitry can be designed for a dual Photon Detector (PD) analog circuitry for changedetection
that predicts skipping or going forward at a sufficient sampling frame rate. For an admitted frame, there is
a purely random sparse matrix [Φ] which is implemented at each bucket pixel level the charge transport bias voltage
toward its neighborhood buckets or not, and if not, it goes to the ground drainage. Since the snapshot image is not a
video, we could not apply the usual MPEG video compression and Hoffman entropy codec as well as powerful
WaveNet Wrapper on sensor level. We shall compare (i) Pre-Processing FFT and a threshold of significant Fourier
mode components and inverse FFT to check PSNR; (ii) Post-Processing image recovery will be selectively done by
CDT&D adaptive version of linear programming at L1 minimization and L2 similarity. For (ii) we need to
determine in new frames selection by SAH circuitry (i) the degree of information (d.o.i) K(t) dictates the purely
random linear sparse combination of measurement data a la [Φ]M,N M(t) = K(t) Log N(t).
Automatic and real-time face recognition can be applied into many attractive applications. For example, at a checkpoint it is expected that there are no burdens on a passing person and a security guard in addition to low cost. Normally a unique 3D person is projected into 2D images with information loss. It means a person is no longer unique in 2D space. Furthermore the various conditions such as pose variance, illumination variance and different expression make face recognition difficult. In order to separate a person, his or her subspace should have several faces and be redundant. That is why the database naturally becomes large. Under this situation the efficient face recognition is a key to a surveillance system. Face recognition by spars representation classification (SRC) could be one of promising candidates to realize rapid face recognition. This method can be understood in a similar way to compressive sensing (CS). In this paper, we propose the efficient approach of face recognition by SRC for multiple poses from the viewpoint of CS. The part-cropped database (PCD) is suggested to avoid position misalignments by discarding the information of topological linkages among eyes, a nose and a mouth. Although topological linkages are important for face recognition in general, they cause position misalignments among multiple poses which decrease recognition rate. Our approach solves one of trade-off problem between keeping topological linkages and avoiding position misalignments. According to the simulated experiments, PCD works well to avoid position misalignments and acquires correct recognition despite less information on topological linkages.
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