USRP Research Projects in ODU Vision Lab - Dr. Asari
Visibility improvement in video sequences captured in non-uniform and extremely low lighting environment
Image enhancement is a preprocessing technique to improve the visibility and highlight the features and details of the image for pattern recognition under varying lighting conditions. Three nonlinear image enhancement algorithms - LDNE (luma-dependent nonlinear enhancement), INDANE (integrated neighborhood dependent algorithm for nonlinear enhancement) and AINDANE (adaptive INDANE, which was implemented based on the statistical features of the image, such as histogram distribution, standard deviation, and frequency domain information.) - based on the principle of sensory attributes of the human visual cortex, are being developed in the ODU Vision Lab. It is observed that LDNE, INDANE and AINDANE algorithms provide extremely optimal results for preprocessing images so that the face regions in an image captured under extremely low or non-uniform lighting conditions are brightened and made more distinct.
Driver's assistant for visibility improvement
Poor visibility on the road has been the cause for many car accidents in the United States. Sometimes a single accident might involve several vehicles. It may be too late to respond after the drivers identify what is ahead in a low visibility environment due to the presence of snow, fog, rain or darkness. Irrespective of the several safety measures available in automobiles, drivers face a great risk of having accidents at night. For instance, an experienced driver generally looks far ahead (100 to 500 meters) to anticipate situations on the road; however, at night the "look ahead" distance is limited to approximately 80 meters. Therefore, it is a necessity that the drivers are assisted with a device that can "see through" the dark, the rain or the fog that is present while driving night. Such a device can be designed with a specific and proven image enhancement technique that can be used to brighten, de-rain or de-fog the images captured in such low visibility environments. The enhanced images can then be used to assist motorists in operating their vehicles under those low visibility conditions. It would be helpful to have enough reaction time if a driver can detect road hazards, pedestrians, and animals on the road well beyond the visible range of vehicle headlights. It is envisaged that this system would be able to provide a clear and complete view and would be able to eliminate even the critical "blind-spot" during driving.
Human face detection, tracking and recognition in video sequences
This project deals with a human surveillance system with integrated face understanding technologies to recognize personal identities in real time. The focus is on developing automated visual identification of face image sequences automatically detected and tracked by a closed-loop mechanism using conventional surveillance cameras. The system performs face detection and tracking robustly in real-time under a wide range of lighting and scale variations by combining motion and facial appearance models. The faces in a scene are detected using a machine learning algorithm and the detected faces are tracked all the time, which helps the detection on next frame. The surveillance system then performs real-time face recognition, which is invariant to large changes in lighting, facial expressions, and pose.
Improvised explosive device (IED) detection
In a biological system, smell is detected by specialized receptor cells of olfactory epithelium called olfactory receptor neurons (ORN). Specialized odor binding proteins (OBP) transports odorants across the mucus layer to the receptors. The role of receptor is taken by a sensor that can convert the detected chemical into an appropriate electrical signal. A chemical sensitive material layer over the sensor acts as the odor binding protein. This layer can be made sensitive to particular chemicals of interests thereby allowing the system to detect the presence of that particular chemical. The target chemicals are of wide variety as an IED can be made using any one or a combination of these chemicals. High explosives can contain any of potassium nitrate, ammonium nitrate, potassium chlorate, red phosphorus, perchlorates, nitroglycerin, TNT, Nitrocellulose, RDX and PETN. A sensor array, containing multiple sensors, is used to distinguish different chemicals. An ideal Improvised Explosive Detection (IED) system would have a sensor placed in a remote controlled vehicle, able to detect the presence of explosive chemicals.
