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 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 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.