Seraj Dosenbach Greg Lammers Beau Morrison Ananya Panja

 Vehicle with object detection feature  Scale Invariant Fourier Transform  Object recognition system  Multi-purpose autonomous vehicle

 Autonomous moving apparatus having obstacle avoidance function  Filed Patent #  Oct 11, 2001  Features include object detection followed by object avoidance. A scan type sensor for horizontal plane scanning A non-scan type sensor for detecting an object in a different plane Memory which holds the estimated dimensions of the object A specific-configuration detecting element uses data from the scan type sensor to determine the presence of an object with the preset estimations An Obstacle detecting element calculates the estimated position of the object using data from scan and non-scan type sensors.

 Claims: 1. One scan type sensor-Radar & one non-scan type – Sonar 2. Dimensions of the object is preset in the memory 3. Scan type-Horizontal plane & Non- scan type-Other plans 4. Obstacle detecting element activates if specific- configuration detecting element detects something  Response 1. 3 non-scan type sensors- Sonar 2. Object dimensions are not stored 3. 1 sensor collects the front view data and 2 sensors collect left & right side data respectively 4. Object detection takes place after processing data collected simultaneously, from all the sensors

 Algorithm which identifies scale invariant features in a image and further recognizes an object in an image  Patent #  Filed Mar. 6, 2000  Features include: An initial image is blurred and then the blurred image is subtracted from the initial image to produce difference image(feature points) A processor is used to produce plurality of difference images For each difference image,pixel amplitude extrema is determined and a pixel region is assigned to each extrema. A pixel extrema is further divided into subregions and plurality of component subregion descriptors (CSDs)for each subregion is produced. The CSDs are correlated with the CSDs of the images under consideration An object is recognized when the CSDs of the image exceed the threshold of the CSDs associated with the object

 Atom board used for processing the image data  Our algorithm is an exact implementation of the original code.  The threshold for recognition will be varied as per our results

 Object recognition system comprising of pivotal cameras, frames storing images of moving objects and then identifying similar images using compressors and correlators.  Patent #  Filed Oct 19, 1981

 Claims: 1. Means for deriving image data of a moving object,consists of several framestores of receiving images from at least one camera. 2. Pictures are taken from manually oriented cameras in said directions and from various aspects. 3. Data compression technique is used to determine a representative image of a particular direction. 4. Correlation is used to compare the compressed image with the previous image and identify similarities if any  Response 1. One camera is used and object may be moving. 2. One camera capable is placed on the pan & tilt bracket,to take pictures from different directions 3. SIFT algorithm is used to process the data 4. Correlation is used to analyze the scale invariant features and hence object recognition

 Multi-purpose autonomous vehicle with path plotting  Patent #  Filed Aug 7, 1991  The system includes A multipurpose autonomous vehicle with body member, wheels, means of propelling and steering, sensors, machine vision subsystem, main controller subsystem Navigation subsystem: receive signals from subsystems, plotting a map, plot a path, control movement, continuous monitor sensors to determine obstacles

 Claims: 1. One main control unit with body member, wheels, propelling and steering means, monitoring obstacles. 2. Receives signals from the destinations and plots a path to the destinations and in turn to the beginning point.  Response 1. D.O.G has one main system incorporating similar features 2. Receives the latitude and longitude information at the start of the journey and at the final destination via GPS. It moves from one waypoint to the next with no predetermined path – object avoidance generates a path.