Invariant template matching in systems with spatiotemporal coding: a vote for instability

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition.Comment: 52 pages, 12 figure

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Scanning and Sequential Decision Making for Multidimensional Data -- Part II: The Noisy Case

We consider the problem of sequential decision making for random fields corrupted by noise. In this scenario, the decision maker observes a noisy version of the data, yet judged with respect to the clean data. In particular, we first consider the problem of scanning and sequentially filtering noisy random fields. In this case, the sequential filter is given the freedom to choose the path over which it traverses the random field (e.g., noisy image or video sequence), thus it is natural to ask what is the best achievable performance and how sensitive this performance is to the choice of the scan. We formally define the problem of scanning and filtering, derive a bound on the best achievable performance, and quantify the excess loss occurring when nonoptimal scanners are used, compared to optimal scanning and filtering. We then discuss the problem of scanning and prediction for noisy random fields. This setting is a natural model for applications such as restoration and coding of noisy images. We formally define the problem of scanning and prediction of a noisy multidimensional array and relate the optimal performance to the clean scandictability defined by Merhav and Weissman. Moreover, bounds on the excess loss due to suboptimal scans are derived, and a universal prediction algorithm is suggested. This paper is the second part of a two-part paper. The first paper dealt with scanning and sequential decision making on noiseless data arrays

Multivariate Statistical Methods that Enable Fast Raman Spectroscopy

Raman spectroscopy is a useful tool in investigating inter- and intra-molecular interactions as well as classifying and quantifying chemical species in a sample. Many materials of societal interest, such as proteins and pharmaceuticals, have distinctive Raman spectra with sharp features. However, the adoption of Raman spectra has been hindered by the low rate of Raman scattering, interference from fluorescence, and high spectrometer costs. This work demonstrates multivariate stastical methods that enable fast Raman measurements, despite the low rate of Raman scattering. These methods include a novel type of spectrometer, that uses computer-controlled optical filters to efficiently capture Raman photons and multiplex them onto either one or two photon counting detector(s). This method, referred to as optimal-binary compressive detection (OB-CD), allows for the collection of chemical information in 10’s of microseconds, rather than milliseconds as might be common for Raman spectroscopy performed using a multichannel detector. A method for orthogonalizing moderate amounts of fluorescence from Raman signal in OB-CD is presented. Fast imaging, with speeds as high as 2.5 frames-per-second, is demonstrated and algorithms for image denoising are discussed. Lastly, methods that enables Raman classification using minimal computation time and a technique for accurately processing Raman thermometry data are presented

Vision-based Real-Time Aerial Object Localization and Tracking for UAV Sensing System

The paper focuses on the problem of vision-based obstacle detection and tracking for unmanned aerial vehicle navigation. A real-time object localization and tracking strategy from monocular image sequences is developed by effectively integrating the object detection and tracking into a dynamic Kalman model. At the detection stage, the object of interest is automatically detected and localized from a saliency map computed via the image background connectivity cue at each frame; at the tracking stage, a Kalman filter is employed to provide a coarse prediction of the object state, which is further refined via a local detector incorporating the saliency map and the temporal information between two consecutive frames. Compared to existing methods, the proposed approach does not require any manual initialization for tracking, runs much faster than the state-of-the-art trackers of its kind, and achieves competitive tracking performance on a large number of image sequences. Extensive experiments demonstrate the effectiveness and superior performance of the proposed approach.Comment: 8 pages, 7 figure

Advances in Raman hyperspectral compressive detection instrumentation for fast label free classification, quantitation and imaging

Multiple prototypes of hyperspectral compressive detection (CD) Raman spectrometers have previously been constructed in the Ben-Amotz lab and have proven to be useful for fast, label-free chemical identification, quantitation and imaging. The CD spectrometer consists of a volume holographic grating (VHG) that linearly disperses the Raman photons into its component wavelengths and all wavelengths are focused onto a digital micromirror devise (DMD). The DMD is an optical modulator that consists of an array of programmable 10μm mirrors that can reflect photons in either +12° or -12° to the incoming light. The DMD is tilted such that the +12° photons go back through the focusing lens and the VHG and is focused onto a single 150μm photon counting avalanche photodiode detector(APD). In chapter 1 of the thesis I describe the construction of a new CD Raman spectrometer capable of fast hyperspectral imaging that has better photon collection efficiency and fewer photon losses compared to its predecessors. The new spectrometer consists of a VHG and a DMD, however, the DMD is not tilted but is perpendicular to the incoming Raman photons. All the Raman photons modulated by the DMD are symmetrically detected in the +12° and -12° by two photon counting photomultiplier tube(PMT) detector modules. The new spectrometer avoids a double pass through the optics and hence has fewer losses associated due to reflection transmission of the optics. Full spectral measurements are made by consecutively scanning through columns of the DMD mirrors and measuring the intensity of photons associated with each wavelength. CD measurements are made by multiplexing wavelengths channels onto the detectors and can be done by applying optimal binary(OB) or Hadamard filters. The new optical design has a spectral window from 150cm-1 to 4000 cm-1 and the improvement in the photon collection efficiency allows classification and imaging speeds of 10μs per point with 13mW of laser power on the sample, and is significantly faster than measurements made with the previous prototype. In chapter 2 of the thesis I describe the construction of a new instrument which is equipped with both a hyperspectral CD spectrometer as well as a traditional Czerny Turner spectrometer. A flip mirror after the Raman microscope directs the Raman scattered beam either towards the CD spectrometer (with the mirror down) or towards the Czerny Turner spectrometer. This instrument allows us to perform head to head comparisons of the two spectrometers using the same Raman scattered photons emitted by the sample. The CD spectrometer uses hardware optical filters to perform compressed chemometric measurements to classify chemicals. The traditional spectrometer uses the CCD to measure full spectral data and chemometric analysis is performed to extract lower dimensional chemical information post measurement. Chemical classification results obtained using two sets of chemicals with differing degrees of spectral overlap show that CD classification is comparable to full spectral classification in the high signal regime. However, for signals consisting of less than 1000 total photon counts, CD classification outperforms full spectral classification. In chapter 3 of the thesis, Raman spectroscopy is used to probe changes in vibrational spectra of nucleotide solutions and hanging droplets containing RNA crystals at different pH. Self-modeling curve resolution (SMCR) applied to full Raman is used to extract solute correlated (SC) Raman spectral components that contain solute spectra with minimal interference from the surrounding solvent. The goal of these studies is to show that Raman spectroscopy can be used to study biological molecules in aqueous environments, with minimal sample preparation and without the need of labels

Spectral unmixing of multiply stained fluorescence samples T

The widespread use of fluorescence microscopy along with the vast library of available fluorescent stains and staining methods has been extremely beneficial to researchers in many fields, ranging from material sciences to plant biology. In clinical diagnostics, the ability to combine different markers in a given sample allows the simultaneous detection of the expression of several different molecules, which in turn provides a powerful diagnostic tool for pathologists, allowing a better classification of the sample at hand. The correct detection and separation of multiple stains in a sample is achieved not only by the biochemical and optical properties of the markers, but also by the use of appropriate hardware and software tools. In this chapter, we will review and compare these tools along with their advantages and limitations

Оптическая корреляционная спектроскопия для дистанционного обнаружения загрязнений

Досягнення в галузі спектрально-кореляційної обробки сигналів створюють перспективу підвищення чутливості виявлення сигналів, що мають складний спектр, таких, наприклад, як сигнали раманівського лідара, що використовує комбінаційне розсіяння. При оптичній спектрально-кореляційній обробці результуючий сигнал являє собою міру подібності не окремих ліній спектру, а комбінації багатьох спектральних ліній. Це дає можливість подолати проблему виявлення сигналів від цілей типу хімічних речовин, що мають малий переріз розсіяння, дозволяє використовувати раманівський лідар для багатьох практичних застосувань, таких, наприклад, як виявлення слідів наркотичних речовин в ручній поклажі або в багажі. В роботі розглянуто принцип дії, проаналізовано результати експериментальних досліджень та відмічені перспективи побудови оптичного спектрально-кореляційного раманівського лідара для дистанційного виявлення типових хімічних речовин на фоні матеріалів промислового та природного походження. При цьому оптичний сигнал обробляється до етапу фотодетектування.Recent advances in spectral correlation signal processing can enhance the detection sensitivity of spectrally complex signals, such as those generated by a Raman lidar. By means of optical correlation, the resulting optical signal is a direct measure of the similarity of the received unknown and known reference spectra, being responsive not to the intensity of individual spectral lines, but to the combination of many spectral lines thus creating an increased sensitivity. It overcomes the small cross-sections of target chemicals and makes Raman lidar practical for many important applications. In particular, the short-range, stand-off detection of explosive residue or narcotics on the exterior of packages, luggage and vehicles now appears to be possible. This paper presents the theory of operation, the results of initial proof of principle experiments and the projected performance of an optical correlation spectroscopy Raman lidar when used to remotely detect typical chemical agents in the presence of common industrial materials and natural backgrounds which could interfere with the detection process. These experiments also mark the first known instance where a received optical signal is analyzed and processed prior to photodetection.Достижения в спектрально-корреляционной обработке сигналов дают шанс повышению чувствительности обнаружения сигналов со сложным спектром, таких, например, как сигналы рамановских лидаров, основанных на комбинационном рассеянии. При оптической спектрально-корреляционной обработке результирующий сигнал представляет собой меру подобия неизвестного и референтного спектров, то есть меру подобия не отдельных линий спектра, а комбинации множества спектральных линий. Это дает возможность преодолеть проблему обнаружения сигналов от целей типа химических веществ, имеющих малое сечение рассеяния, и обеспечивает использование рамановского лидара для многих практических применений, таких, например, как обнаружение следов наркотических веществ на ручной клади или в багаже. В работе рассмотрен принцип действия, проанализированы результаты экспериментальных исследований и отмечены перспективы построения оптического спектрально-корреляционного рамановского лидара для дистанционного обнаружения типичных химических веществ на фоне материалов промышленного и естественного происхождения. При этом оптический сигнал обрабатывается до этапа фотодетектирования

Edge-preserving sectional image reconstruction in optical scanning holography

Optical scanning holography (OSH) enables us to capture the three-dimensional information of an object, and a post-processing step known as sectional image reconstruction allows us to view its two-dimensional crosssection. Previous methods often produce reconstructed images that have blurry edges. In this paper, we argue that the hologram's two-dimensional Fourier transform maps into a semi-spherical surface in the threedimensional frequency domain of the object, a relationship akin to the Fourier diffraction theorem used in diffraction tomography. Thus, the sectional image reconstruction task is an ill-posed inverse problem, and here we make use of the total variation regularization with a nonnegative constraint and solve it with a gradient projection algorithm. Both simulated and experimental holograms are used to verify that edge-preserving reconstruction is achieved, and the axial distance between sections is reduced compared with previous regularization methods. © 2010 Optical Society of America.published_or_final_versio