Newness and distinctiveness is claimed in the features of ornamentation as shown inside the broken line circle in the accompanying representation.

Display screen or portion thereof with graphical user interface

A computer especially suitable for use as a video-based security system includes video inputs, a processor and a network connection. The video inputs are each configured to receive an electronic video signal from a video camera. The processor operates on a digital representation of the electronic video signals from the video inputs. When the computer detects motion in the electronic video signals it generates a compressed representation of the video signal that includes the motion. The compressed representation is transmitted through the network connection.

Video monitoring and security system

Infrared signature studies of aerospace vehicles

The predictions of 13 computational bottom-up saliency models and a newly introduced Multiscale Contrast Conspicuity (MCC) metric are compared with human visual conspicuity measurements. The agreement between human visual conspicuity estimates and model saliency predictions is quantified through their rank order correlation. The maximum of the computational saliency value over the target support area correlates most strongly with visual conspicuity for 12 of the 13 models. A simple multiscale contrast model and the MCC metric both yield the largest correlation with human visual target conspicuity (>;0.84). Local image saliency largely determines human visual inspection and interpretation of static and dynamic scenes. Computational saliency models therefore have a wide range of important applications, like adaptive content delivery, region-of-interest-based image compression, video summarization, progressive image transmission, image segmentation, image quality assessment, object recognition, and content-aware image scaling. However, current bottom-up saliency models do not incorporate important visual effects like crowding and lateral interaction. Additional knowledge about the exact nature of the interactions between the mechanisms mediating human visual saliency is required to develop these models further. The MCC metric and its associated psychophysical saliency measurement procedure are useful tools to systematically investigate the relative contribution of different feature dimensions to overall visual target saliency.

Computational versus Psychophysical Bottom-Up Image Saliency: A Comparative Evaluation Study

An object recognition system including a position sensor, an image sensor and a controller is provided. The position sensor determines the position of an object, and the image sensor captures an image of the object. The controller sets a processing area within the image captured by the image sensor based on the position of the object determined by the position sensor and a predetermined size for the object to be recognized. The controller extracts horizontal edges from the processing area, and identifies horizontal edges belonging to the outline of the object from the extracted edges. Thus, the object can be recognized based on the horizontal edges only. Similarly, the controller can extract vertical edges from the processing area and identify vertical edges belonging to the outline of the object. Preferably, the controller selects upper, lower, left, and right horizontal and vertical candidate ends of the object from the identified horizontal and vertical edges respectively, from which upper, lower, left, and right ends of the object are determined. If either one or both of the left and right horizontal candidate ends cannot be selected, candidate ends can be estimated based on the position of the object recognized in a previous recognition cycle, and the estimated candidate ends can be used in lieu of the candidate ends selected from the horizontal edges.

Object recognition system

Apparatus (10) and a method for visually monitoring a scene and detecting motion of an intruder within the scene. A camera (C) continually views the scene and produces a representative signal. A processor (12) processes the signal and produces an image (f2) represented by the signal. This image is compared with a similar image (f1) of the scene from a previous point in time. Segments of the later image which differ from segments of the earlier image are identified. A discriminator (14) evaluates these segments to determine if the differences result simply from lighting changes, or the movement of an intruder within the scene. If caused by an intruder, an appropriate indication is provided. An algorithm is employed by which differences from one image to another caused by lighting changes, the effects of motion of objects established within the scene, noise, and aliasing effects are identified so as not to produce false alarms. Those remaining segments are further evaluated to determine if there is an intruder present within the scene.

Low false alarm rate detection for a video image processing based security alarm system

A system (10) for friend-or-foe identification (IFF) comprises an interrogator unit (18) carried on a first platform (T1) for generating and transmitting a laser beam. A control unit (22) selectively directs the transmitted laser beam toward a second platform (T2). A coding/decoding unit (28) encodes an IFF interrogator message with which a portion of the transmitted laser beam is modulated. A laser reflector (30) carried on the second platform receives the coded laser beam. The reflector is a normally passive reflector which is activated by receipt of the transmitted laser beam. The reflector responds to the IFF message contained in the received laser beam by modulating the unmodulated portion of the transmitted laser beam. This newly modulated portion of the laser beam is reflected back toward the first platform. The coding/encoding unit decodes on the first platform receives and the response, and determines if it corresponds to a predetermined response which identifies the second platform as a "friend".

Beam steered laser IFF system

A "hardware-in-the-loop" simulator (10) for training people in the use of a missile system to teach target acquisition, missile launch, and missile guidance under simulated battlefield conditions. A battlefield environment (E) including at least one target (T) movable therewithin is created by a simulation module (12). Missile system hardware (H) including the missile acquisition, tracking, and guidance portions is provided. An interface module (20) converts signals produced by the simulating module to an infrared image acceptable by the hardware. The resultant image represents a field-of-view (FOV), including the target, within the battlefield environment. An image module (32) produces a dynamic image representative of the missile's position in the field-of-view. This image is observable by the hardware which utilizes it to determine the position of the missile relative to the target. The hardware also determines if a missile guidance signal is to be sent to the missile to guide it to the target. If so, the interface module is responsive to the guidance signal to simulate, in real-time, the response of the missile to the guidance signal.

Hardware-in-the-loop tow missile system simulator

Apparatus (10) is provided for detecting an object (B) such as a xenon beacon on a missile (M) located within a defined field of view (FOV). The field of view is defined by an array (A) of pixels (P). A sensor (14) includes a detector (16) which repetitively scans the field of view to detect the presence of an object therein. The output of the sensor is converted for use in activating pixels in the array in response to the detection of the object. A processor (22), in response to the presence of the object, defines a pixel matrix within the array. This matrix includes the pixel for the detected object signature. The processor searches all the pixels within the matrix to identify a signature which matches a predetermined characteristic of the object. Identification of such a signature, helps particularly identify the object in the field of view. The processor then focuses on this pixel set, during subsequent scans, to track the object.

Controlled interlace for TOW missiles using medium wave infrared sensor or TV sensor

Qualitative discussion of sensors sources and transfer of radiation targets and backgrounds atmospherics system modeling the human observer end-to-end models.

John R. Wootton

Papers

Low false alarm rate detection for a video image processing based security alarm system

Beam steered laser IFF system

Hardware-in-the-loop tow missile system simulator

Controlled interlace for TOW missiles using medium wave infrared sensor or TV sensor

Electro-Optical Systems Performance Modeling