Abstract: A system and method of determining a neural network configuration may include receiving at least one neural network configuration, altering the received configuration for at least two iterations, calculating a first parameter of an altered configuration, calculating a second parameter of a consecutive altered configuration of the at least two iterations, comparing values of the calculated first parameter and second parameter, and determining a configuration having largest value of the calculated parameters.

Abstract: A computer-implemented method for detecting a characteristic in a sample of a set of samples is described. The method may include receiving from a user an indication for each sample of said set of samples that the user determines to include the characteristic. The method may also include defining samples of said set of samples that were not indicated by the user to include the characteristic as not including the characteristic. The method may further include iteratively applying by a processing unit, a detection algorithm on a first subset of the set of samples, said detection algorithm using a set of detection criteria that includes one or a plurality of detection criteria, evaluating a detection performance of the detection algorithm and modifying the detection algorithm by making changes in the set of detection criteria to enhance detection performance of the learning algorithm.

Abstract: A method processes an input image securely. An input image I is acquired in a client. A set of m random images, H1, . . . , Hm, and a coefficient vector, a=[a1, . . . , am], are generated such that the input image I is I=?i=1m?iHj. The set of the random images is transferred to a server including a weak classifier. In the server, a set of m convolved random images H? are determined, such that {H1?=?1(H1*y}i,1m, where * is a convolution operator and ?1 is a first random pixel permutation. The set of convolved images is transferred to the client. In the client, a set of m permuted images I? is determined, such that I?=?2(?i=1m?iH1?), where ?2 is a second random pixel permutation. The set of permuted image is transferred to the server.

Abstract: A method processes an input image securely. An input image is acquired in a client and partitioned into a set of overlapping tiles. The set of overlapping tiles is transferred to a server. In the server, motion pixels in each tile that are immediately adjacent to other motions pixels in the tile are labeled locally to generate a set of locally labeled tiles. The set of locally labeled tiles is transferred to the client. In the client, the set of locally labeled tiles is labeled globally to generate a list of pairs of unique global labels. The list of pairs of unique global labels is transferred to the server. In the server, the pairs of unique global labels are classified into equivalence classes. The equivalence classes are transferred to the client and the motion pixels are relabeled in the client according to the equivalence classes to form connected components in the input image.

Abstract: A method processes a sequence of input images securely. A sequence of input images are acquired in a client. Pixels in each input image are permuted randomly according to a permutation ? to generate a permuted image for each input image. Each permuted image is transferred to a server, which maintains a background image from the permuted images. In the server, each permuted image is combined with the background image to generate a corresponding permuted motion image for each permuted image. Each permuted motion image is transferred to the client and the pixels in each permuted motion image are reordered according to an inverse permutation ??1 to recover a corresponding motion image for each input image.

Abstract: A user trainable detecting apparatus for on site configuration comprises: one or more sensors; a detector for detecting events within the data arriving from the sensor, and a user interface that has labeling functionality, and which enables the user to label data from the sensor through the interface. A learning unit uses the labeled data for in-situ learning for use in the detector.

Abstract: A method processes an input image securely. An input image I is acquired in a client. A set of m random images, H1, . . . , Hm, and a coefficient vector, a=[a1, . . . , am], are generated such that the input image I is I=?i=1m?i Hj. The set of the random images is transferred to a server including a weak classifier. In the server, a set of m convolved random images H? are determined, such that {HI?=?1(H1*y}i.1m, where * is a convolution operator and ?1 is a first random pixel permutation. The set of convolved images is transferred to the client. In the client, a set of m permuted images I? is determined, such that I?=?2(?i=1m?i H1?), where ?2 is a second random pixel permutation. The set of permuted image is transferred to the server.

Abstract: A method processes a sequence of input images securely. A sequence of input images are acquired in a client. Pixels in each input image are permuted randomly according to a permutation ? to generate a permuted image for each input image. Each permuted image is transferred to a server, which maintains a background image from the permuted images. In the server, each permuted image is combined with the background image to generate a corresponding permuted motion image for each permuted image. Each permuted motion image is transferred to the client and the pixels in each permuted motion image are reordered according to an inverse permutation ??1 to recover a corresponding motion image for each input image.

Abstract: A method processes an input image securely. An input image is acquired in a client and partitioned into a set of overlapping tiles. The set of overlapping tiles is transferred to a server. In the server, motion pixels in each tile that are immediately adjacent to other motions pixels in the tile are labeled locally to generate a set of locally labeled tiles. The set of locally labeled tiles is transferred to the client. In the client, the set of locally labeled tiles is labeled globally to generate a list of pairs of unique global labels. The list of pairs of unique global labels is transferred to the server. In the server, the pairs of unique global labels are classified into equivalence classes. The equivalence classes are transferred to the client and the motion pixels are relabeled in the client according to the equivalence classes to form connected components in the input image.