Abstract: A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances.

Abstract: A method for calculating a square root B having N bits of a number X having 2N bits includes iterating on bits bi of square root B starting from the most significant bit until the least significant bit of square root B. For each iteration, the method includes locating a 1 at the squared location of bit bi in a CHECK variable, determining the value of bit bi from the result of a comparison of number X with a function of all previously found bits and a previous comparison outcome, shifting all previously found bits right 1 location in a CHECK variable, and adding the determined value of bit bi into its squared location in the CHECK variable.

Abstract: A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances. The processor includes a Tanimoto to Hamming threshold converter, a Hamming measurer, and a Hamming comparator. The Tanimoto to Hamming threshold converter converts a Tanimoto threshold into a Hamming threshold. The Hamming measurer measures the Hamming distances between the candidate vectors and the query vector. The Hamming comparator selects candidate vectors whose Hamming distance from the query vector is less than or equal to the Hamming threshold.

Abstract: A method for an associative memory device includes the steps of providing a look up table (LUT) with all possible solutions for N first iterations of a CORDIC algorithm, receiving a plurality of input angles, concurrently computing a location index for each angle of the plurality of angles and concurrently storing each index in a column of the associative memory device, copying a solution from the LUT in the location index to a plurality of columns associated with the index and concurrently performing M additional iterations of the CORDIC algorithm on the columns to compute a value of a trigonometric function for each angle.

Abstract: A system for measuring similarity between a binary query vector and a plurality of binary candidate vectors includes a storage unit and a processor. The storage unit stores the binary query vector and the plurality of candidate vectors, and the processor performs Tanimoto calculations in terms of Hamming distances. The processor includes a Tanimoto to Hamming threshold converter, a Hamming measurer, and a Hamming comparator. The Tanimoto to Hamming threshold converter converts a Tanimoto threshold into a Hamming threshold. The Hamming measurer measures the Hamming distances between the candidate vectors and the query vector. The Hamming comparator selects candidate vectors whose Hamming distance from the query vector is less than or equal to the Hamming threshold.

Abstract: An associative processor includes a memory array and a controller. The memory array stores a multiplicity of N bit stochastic numbers in separate rows of a stochastic section of the memory array and each stochastic number has a same probability distribution P. The controller includes a probability calculator which receives a desired probability distribution Pdesired, determines a Boolean function of a set of the N bit stochastic numbers which produces the probability distribution Pdesired and activates associated rows of the stochastic numbers to implement the function on the rows to produce a resultant stochastic number having the probability distribution Pdesired.

Abstract: A method is provided for use in a stereoscopic image generating system, the system including at least two image capturing sensors and at least one aggregation processor. The at least one aggregation processor is configured to: receive data associated with an image captured by the image capturing sensors; calculate aggregation results for a pre-defined number of disparity levels based on data received from one of the at least two image capturing sensors; estimate aggregation results for data received from another image capturing sensor; and combine the calculated results with the estimated results.

Abstract: An associative processor includes a memory array and a controller. The memory array stores a multiplicity of N bit stochastic numbers in separate rows of a stochastic section of the memory array and each stochastic number has a same probability distribution P. The controller includes a probability calculator which receives a desired probability distribution Pdesired, determines a Boolean function of a set of the N bit stochastic numbers which produces the probability distribution Pdesired and activates associated rows of the stochastic numbers to implement the function on the rows to produce a resultant stochastic number having the probability distribution Pdesired.

Abstract: A method is provided for reconstructing a three dimensional image, comprising: providing a pair of stereoscopic images captured by two image capturing devices; retrieving disparity data from the pair of stereoscopic images; using retrieved disparity data to determine contours of an object present in the pair of the stereoscopic images captured; selecting a pixel belonging to that object; establishing a search area at the vicinity of the selected pixel; determining values for various disparity candidates, wherein the determined values correspond to one or more chosen hypothesis associated with the search area; testing the one or more chosen hypothesis and selecting an hypothesis which best fits the selected pixel and its neighboring pixels; executing a stereo matching algorithm using data retrieved from the captured images while using the values of disparity candidates that correspond to the chosen hypothesis; and reconstructing the three dimensional image based on the results obtained.

Abstract: A method is provided for obtaining a disparity map for reconstructing a three dimensional image. The map is based upon a large range of disparities and is obtained by using a hardware provided with a buffer capable of storing data that relates to substantially less disparities than a data associated with the large disparities' range. The method comprises the steps of: providing a pair of stereoscopic images captured by two image capturing devices; dividing the large disparities' range into N disparity ranges; executing a stereo matching algorithm for a plurality of times, using data retrieved from a pair of captured images, wherein the algorithm is executed each time while using a different disparity range out of the N disparity ranges, thereby obtaining a plurality of individual disparity maps, each corresponding to a different disparity range; and merging the individual disparity maps to generate a map of the large disparities' range.

Abstract: A method is provided for reconstructing a three dimensional image, comprising: providing a pair of stereoscopic images captured by two image capturing devices; retrieving disparity data from the pair of stereoscopic images; using retrieved disparity data to determine contours of an object present in the pair of the stereoscopic images captured; selecting a pixel belonging to that object; establishing a search area at the vicinity of the selected pixel; determining values for various disparity candidates, wherein the determined values correspond to one or more chosen hypothesis associated with the search area; testing the one or more chosen hypothesis and selecting an hypothesis which best fits the selected pixel and its neighboring pixels; executing a stereo matching algorithm using data retrieved from the captured images while using the values of disparity candidates that correspond to the chosen hypothesis; and reconstructing the three dimensional image based on the results obtained.

Abstract: A method is provided for use in a stereoscopic image generating system comprising at least two image capturing sensors and at least one aggregation processor configured to: receive data associated with an image captured by the image capturing sensors; calculate aggregation results for a pre-defined number of disparity levels based on data received from one of the at least two image capturing sensors; estimate aggregation results for data received from another image capturing sensors; and combine the calculated results with the estimated results.

Abstract: A method is provided for obtaining a disparity map for reconstructing a three dimensional image. The map is based upon a large range of disparities and is obtained by using a hardware provided with a buffer capable of storing data that relates to substantially less disparities than a data associated with the large disparities' range. The method comprises the steps of: providing a pair of stereoscopic images captured by two image capturing devices; dividing the large disparities' range into N disparity ranges; executing a stereo matching algorithm for a plurality of times, using data retrieved from a pair of captured images, wherein the algorithm is executed each time while using a different disparity range out of the N disparity ranges, thereby obtaining a plurality of individual disparity maps, each corresponding to a different disparity range; and merging the individual disparity maps to generate a map of the large disparities' range.