Yuri Owechko

Associative holographic memory with feedback using phase-conjugate mirrors.

We describe an all-optical associative memory system that uses a holographic data base. Phase-conjugate mirrors are used to provide optical feedback, thresholding, and gain. Analysis and preliminary experiments are discussed.

Holographic associative memory with nonlinearities in the correlation domain.

We describe a shift-invariant all-optical holographic associative memory implemented using phase conjugate mirrors and Fourier transform holograms. A key feature of our system is the large storage capacity obtained through the use of nonlinearities in the correlation domain. The use of angularly multiplexed plane wave reference beams allows access to the correlation domain where nonlinearities in the phase conjugate mirrors can be used to reduce greatly crosstalk and correlation noise.

Active learning system for object fingerprinting

Object fingerprinting and identification is a critical part of effective visual surveillance systems. In this paper, we present an approach to actively learn the object models in order to fingerprint the objects. Our approach uses a view-based classifier cascade that actively learns to recognize the generic class of the object. Salient features unique to the specific instance of the selected class of objects are modeled using fuzzy attribute relational graphs. These graphs are also adapted to represent object information gathered from multiple views. Preliminary results are quite promising and extensive studies are underway to ascertain the use of the system in more complicated scenarios.

A Swarm-Based Volition/Attention Framework for Object Recognition

Visual attention helps identify the salient parts of a scene and enables efficient object recognition by allocating visual resources to more relevant regions of the scene. In this paper, we present an object recognition framework that combines top-down volitional recognition with attention processes using a swarm of cooperating intelligent agents. Each agent in the swarm is a selfcontained independent classifier that can, given any location in the image, predict the presence of a particular object of interest. Our framework combines bottom-up attention and top-down object classification using Particle Swarm Optimization (PSO) dynamics in a novel architecture that utilizes spatially-modulated evolutionary search to rapidly detect objects of interest in a scene. We use bottom-up maps that are automatically built from saliency, past swarm experience, and constraints on possible object positions to modify the swarm’s behavior and help guide the swarm in locating objects. We present fast object detection/recognition results for a variety of video sequences. Our results show that our framework allows objects to be quickly and accurately located and classified using very sparse sampling of the scene.

Optoelectronic resonator neural networks

An associative memory is described which utilizes a hologram in a hybrid optoelectronic resonator. The nonlinear end mirrors of the resonator are two sets of video detectors and spatial light modulators which act as pseudoconjugators. The transformation properties of the end mirrors are programmable which allows the real-time selection of various associative pathways. An extension of the system to multilayer neural network models, in particular a backpropagation network, is also described.

Behavior recognition using cognitive swarms and fuzzy graphs

Behavior analysis deals with understanding and parsing a video sequence to generate a high-level description of object actions and inter-object interactions. In this paper, we describe a behavior recognition system that can model and detect spatio-temporal interactions between detected entities in a visual scene by using ideas from swarm optimization, fuzzy graphs, and object recognition. Extensions of Particle Swarm Optimization based approaches for object recognition are first used to detect entities in video scenes. Our hierarchical generic event detection scheme uses fuzzy graphical models for representing the spatial associations as well as the temporal dynamics of the discovered scene entities. The spatial and temporal attributes of associated objects and groups of objects are handled in separate layers in the hierarchy. We also describe a new behavior specification language that helps the user analyst easily describe the event that needs to be detected using either simple linguistic queries or graphical queries. Our experimental results show that the approach is promising for detecting complex behaviors.

Cognitive swarms for rapid detection of objects and associations in visual imagery

We have developed a new optimization-based framework for computer vision that combines ideas from particle swarm optimization (PSO) and statistical pattern recognition to rapidly and accurately detect and classify objects in visual imagery. Swarm intelligence is used to locate objects by optimizing the classification confidence level. We have used our cognitive swarm framework to rapidly detect people, ground vehicles, and boats, and to recognize behaviors based on object associations, such as people exiting and entering vehicles, for applications in security, surveillance, target recognition, and automotive active safety.

All-optical associative memory with shift invariance and multiple-image recall.

We present experimental results from an all-optical associative memory that combines holography and phase conjugation. The device has the capability to recall a complete image when merely a portion of the stored image is input to the system. Multiple superimposed two-dimensional images with gray scale can be stored and recalled. In addition, we have demonstrated the systems invariance to translation of the input images.

On generalized-marginal time-frequency distributions

We introduce a family of time-frequency (TF) distributions with generalized marginals, i.e., beyond the time-domain and the frequency-domain marginals, in the sense that the projections of a TF distribution along one or more angles are equal to the magnitude squared of the fractional Fourier transforms of the signal. We present a necessary and sufficient condition for a TF distribution in Cohens class to satisfy generalized marginals. We then modify the existing well-known TF distributions in Cohens class, such as Choi-Williams (1989) and Page distributions, so that the modified ones have generalized marginals. Numerical examples are presented to show that the proposed TF distributions have the advantages of both Wigner-Ville and other quadratic TF distributions, which only have the conventional marginals. Moreover, they also indicate that the generalized-marginal TF distributions with proper marginals are more robust than the Wigner-Ville and the Choi-Williams distributions when signals contain additive noise.

Driver cognitive workload estimation: a data-driven perspective

Driver workload estimation (DWE) refers to the activities of monitoring a driver and the driving environment in real-time and acquiring the knowledge of the drivers workload continuously. With this knowledge of the drivers workload, the in-vehicle information systems (IVIS) can provide information on when the driver has the spare capacity to receive and comprehend it, which is both effective and efficient. However, after years of study, it is still difficult to build a robust DWE system. In this paper, we analyze the difficulties facing the existing methodology of developing DWE systems and propose a machine-learning-based DWE development process. Some preliminary but promising results are reported using a popular machine-learning method, the decision tree.