Abstract: Apparatus and methods are described including a microscope system (11) configured to acquire one or more microscope images of a bodily sample, an output device (34), and at least one computer processor (28). The computer processor identifies, in the one or more images, at least one element as being a pathogen candidate, and extracts, from the one or more images, at least one candidate-informative feature associated with the pathogen candidate. The compute processor extracts, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample. The computer processor classifies a likelihood of the bodily sample being infected with a pathogenic infection, by processing the candidate-informative feature in combination with the sample-informative feature, and generates an output upon the output device, in response thereto. Other applications are also described.

Abstract: Methods, devices and computer-readable mediums for detecting occlusions which occur due to foreground object movement with respect to a background between first and second successive frames. Occlusion detection may use motion estimation with respect to at least a third frame temporally preceding the first frame. Occlusion detection may be based on one or more assumptions such as: occlusion motion vectors are different than other background motion vectors; occlusion motions are likely to be similar to foreground occluding motion; and/or motion estimation will match an occlusion block with a block belonging to a common background object. Occlusion detection may be combined with motion error detection based on a motion field divergence using a motion vector assigned to the occlusion, e.g., for generating an intermediate frame in frame up rate conversion (FRUC).

Abstract: Apparatus and methods are described for use with a blood sample (48, 50), including measuring hemoglobin concentration within at least a portion (48) of the blood sample, by performing a first measurement on the blood sample. Mean corpuscular hemoglobin in the blood sample (48, 50) is measured, by performing a second measurement on the blood sample (48, 50). A parameter of the blood sample is determined, based on a relationship between the concentration of hemoglobin and the mean corpuscular hemoglobin. Other applications are also described.

Abstract: Methods, devices and computer-readable mediums for detecting occlusions which occur due to foreground object movement with respect to a background between first and second successive frames. Occlusion detection may use motion estimation with respect to at least a third frame temporally preceding the first frame. Occlusion detection may be based on one or more assumptions such as: occlusion motion vectors are different than other background motion vectors; occlusion motions are likely to be similar to foreground occluding motion; and/or motion estimation will match an occlusion block with a block belonging to a common background object. Occlusion detection may be combined with motion error detection based on a motion field divergence using a motion vector assigned to the occlusion, e.g., for generating an intermediate frame in frame up rate conversion (FRUC).

Abstract: Apparatus and methods are described including a microscope system (11) configured to acquire one or more micro scope images of a bodily sample, an output device (34), and at least one computer processor (28). The computer processor identifies, in the one or more images, at least one element as being a pathogen candidate, and extracts, from the one or more images, at least one candidate-informative feature associated with the pathogen candidate. The compute processor extracts, from the one or more images, at least one sample-informative feature that is indicative of contextual information related to the bodily sample. The computer processor classifies a likelihood of the bodily sample being infected with a pathogenic infection, by processing the candidate-informative feature in combination with the sample-informative feature, and generates an output upon the output device, in response thereto. Other applications are also described.

Abstract: A method for selecting a motion vector includes selecting a candidate block included in a second image that corresponds to a processing block of a first image, computing a first probability for the processing block and the candidate block, selecting a random block for the candidate block, computing a second probability for the processing block and the random block, saving a greater of the first probability and the second probability as a first comparison result, computing a third probability for the processing block, a first neighboring block of the processing block, a matching block in the second image that is matched to the first neighboring block, and a second neighboring block of the matching block, and saving a greater of the first comparison result and the third probability as a second comparison result.

Abstract: Provided are a method and an apparatus for correcting an erroneous displacement vector in an image.

Abstract: A method for selecting a motion vector includes selecting a candidate block included in a second image that corresponds to a processing block of a first image, computing a first probability for the processing block and the candidate block, selecting a random block for the candidate block, computing a second probability for the processing block and the random block, saving a greater of the first probability and the second probability as a first comparison result, computing a third probability for the processing block, a first neighboring block of the processing block, a matching block in the second image that is matched to the first neighboring block, and a second neighboring block of the matching block, and saving a greater of the first comparison result and the third probability as a second comparison result.