Systems and methods for classifying products are disclosed. A first data sample having a first portion and a second portion is identified from a training dataset. A first mask is generated based on the first data sample, where the first mask is associated with the first portion of the first data sample. A second data sample is generated based on a noise input. The first mask is applied to the second data sample for outputting a third portion of the second data sample. The third portion of the second data sample is combined with the second portion of the first data sample for generating a first combined data sample. Confidence and classification of the first combined data sample are predicted. The first combined data sample is added to the training dataset in response to predicting the confidence and the classification.

An apparatus for detecting a specular surface in a scene is provided. The apparatus includes an illumination device configured to emit polarized light towards the scene. The apparatus further includes an imaging system configured to capture a first image of the scene based on light emanating from the scene. The light emanating from the scene includes one or more reflection of the emitted polarized light. The imaging system is further configured to capture a second image of the scene based on filtered light. The apparatus further includes a polarization filter configured to generate the filtered light by filtering the light emanating from the scene. The apparatus further includes processing circuitry configured to determine presence of the specular surface in the scene based on a comparison of the first image and the second image.

A method for recovering missing spectral amplitude and phase information of an input real, positive valued image or signal possibly carried by incoherent or coherent waves.

A method and apparatus for recognizing target objects. The method comprises identifying a group of target objects in an image. Further, the method comprises forming a group of chips encompassing the group of target objects identified in the image. Yet further, the method comprises recognizing the group of target objects in the group of chips using a group of filters, wherein the group of filters was created using a group of models for reference objects and environmental information for a location where the group of target objects was located when the image was generated, wherein a filter in the group of filters comprises a group of reference images for a reference object in the reference objects.

Systems and methods for classifying products are disclosed. A first data sample having a first portion and a second portion is identified from a training dataset. A first mask is generated based on the first data sample, where the first mask is associated with the first portion of the first data sample. A second data sample is generated based on a noise input. The first mask is applied to the second data sample for outputting a third portion of the second data sample. The third portion of the second data sample is combined with the second portion of the first data sample for generating a first combined data sample. Confidence and classification of the first combined data sample are predicted. The first combined data sample is added to the training dataset in response to predicting the confidence and the classification.

A method for recovering missing spectral amplitude and phase information of an input real, positive valued image or signal possibly carried by incoherent or coherent waves.