Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: In one embodiment, a method includes accessing first-resolution images corresponding to frames of a video, computing a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video, generating a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame, generating a second-resolution intermediate image associated with the second frame based on the first-resolution image associated with the second frame, computing adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model, and adjusting pixels of the second-resolution intermediate image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second fra

Abstract: In one embodiment, a method includes accessing first-resolution images corresponding to frames of a video, computing a motion vector based on a first-resolution image of a first frame in the video and a first-resolution image of a second frame in the video, generating a second-resolution warped image associated with the second frame by using the motion vector to warp a second-resolution reconstructed image associated with the first frame, generating a second-resolution intermediate image associated with the second frame based on the first-resolution image associated with the second frame, computing adjustment parameters by processing the first-resolution image associated with the second frame and the second-resolution warped image associated with the second frame using a machine-learning model, and adjusting pixels of the second-resolution intermediate image associated with the second frame based on the adjustment parameters to reconstruct a second-resolution reconstructed image associated with the second fra

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: In one embodiment, a computing system configured to generate a current frame may access a current sample dataset having incomplete pixel information of a current frame in a sequence of frames. The system may access a previous frame in the sequence of frames with complete pixel information. The system may further access a motion representation indicating pixel relationships between the current frame and the previous frame. The previous frame may then be transformed according to the motion representation. The system may generate the current frame having complete pixel information by processing the current sample dataset and the transformed previous frame using a first machine-learning model.

Abstract: In one embodiment, a computing system configured to generate a current frame may access a current sample dataset having incomplete pixel information of a current frame in a sequence of frames. The system may access a previous frame in the sequence of frames with complete pixel information. The system may further access a motion representation indicating pixel relationships between the current frame and the previous frame. The previous frame may then be transformed according to the motion representation. The system may generate the current frame having complete pixel information by processing the current sample dataset and the transformed previous frame using a first machine-learning model.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a set of inspection images of a set of assembly units recorded by an optical inspection station; for each inspection image in the set of inspection images, detecting a set of features in the inspection image and generating a vector representing the set of features in a multi-dimensional feature space; grouping neighboring vectors in the multi-dimensional feature space into a set of vector groups; and, in response to receipt of a first inspection result indicting a defect in a first assembly unit, in the set of assembly units, associated with a first vector in a first vector group, in the set of vector groups, labeling the first vector group with the defect and flagging a second assembly unit associated with a second vector, in the first vector group, as exhibiting characteristics of the defect.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a first set of inspection images of a first set of assembly units recorded by an optical inspection station over a first period of time; generating a first set of vectors representing features extracted from the first set of inspection images; grouping neighboring vectors in a multi-dimensional feature space into a set of vector groups; accessing a second inspection image of a second assembly recorded by the optical inspection station at a second time succeeding the first period of time; detecting a second set of features in the second inspection image; generating a second vector representing the second set of features in the multi-dimensional feature space; and, in response to the second vector deviating from the set of vector groups by more than a threshold difference, flagging the second assembly unit.

Abstract: One variation of a method for predicting manufacturing defects includes: accessing a set of inspection images of a set of assembly units recorded by an optical inspection station; for each inspection image in the set of inspection images, detecting a set of features in the inspection image and generating a vector representing the set of features in a multi-dimensional feature space; grouping neighboring vectors in the multi-dimensional feature space into a set of vector groups; and, in response to receipt of a first inspection result indicting a defect in a first assembly unit, in the set of assembly units, associated with a first vector in a first vector group, in the set of vector groups, labeling the first vector group with the defect and flagging a second assembly unit associated with a second vector, in the first vector group, as exhibiting characteristics of the defect.