公开(公告)号：US20240406580A1

公开(公告)日：2024-12-05

申请号：US18325420

申请日：2023-05-30

Applicant: Synaptics Incorporated

Inventor： Omar OREIFEJ ,  Karthikeyan SHANMUGA VADIVEL ,  Patrick A. WORFOLK

IPC: H04N23/81 ,  G06T5/50 ,  H04N23/53 ,  H04N23/695

Abstract: This disclosure provides methods, devices, and systems for machine learning. The present implementations more specifically relate to automatons that can acquire input images and ground truth images for training neural network models. In some aspects, a system for acquiring training data may include a camera, an electronic display, and an apparatus configured to maintain the camera in a stationary position while moving the electronic display in and out of the camera's field-of-view (FOV). In some aspects, the system may further include a controller configured to acquire training data via the camera based on the positioning of the electronic display. In some implementations, the controller may acquire ground truth images of a scene while the electronic display is covering the camera's FOV. In some other implementations, the controller may acquire input images of the scene while the electronic display is outside the camera's FOV.

公开(公告)号：US20220366189A1

公开(公告)日：2022-11-17

申请号：US17317480

申请日：2021-05-11

Applicant: Synaptics Incorporated

Inventor： Omar OREIFEJ ,  Karthikeyan SHANMUGA VADIVEL ,  Patrick A. WORFOLK ,  Kirk HARGREAVES

IPC: G06K9/62 ,  G06T7/00 ,  G06K9/46

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. The present implementations more specifically relate to selecting images that can be used for training a neural network to infer denoised representations of images captured in low light conditions. In some aspects, a machine learning system may obtain a series of images of a given scene, where each of the images is associated with a different SNR (representing a unique combination of exposure and gain settings). The machine learning system may identify a number of saturated pixels in each image and classify each of the images as a saturated image or a non-saturated image based on the number of saturated pixels. The machine learning system may then select the non-saturated image with the highest SNR as the ground truth image, and the non-saturated images with lower SNRs as the input images, to be used for training the neural network.

公开(公告)号：US20250005906A1

公开(公告)日：2025-01-02

申请号：US18344064

申请日：2023-06-29

Applicant: Synaptics Incorporated

Inventor： Karthikeyan SHANMUGA VADIVEL ,  Patrick A. WORFOLK ,  Zacchaeus SCHEFFER ,  Dmitri LVOV

IPC: G06V10/77 ,  G06V10/22 ,  G06V10/26 ,  G06V10/764 ,  G06V10/766 ,  G06V10/82

Abstract: This disclosure provides methods, devices, and systems for object detection. The present implementations more specifically relate to techniques for improving distant object detection in memory-constrained computer vision systems. In some aspects, a computer vision system may include an ROI extraction component, a feature pyramid network (FPN) having a number (N) of pyramid levels, and N network heads associated with the N pyramid levels, respectively. The FPN extracts N feature maps from an input image, where the N feature maps are associated with the N pyramid levels, respectively, and each of the N network heads performs an object detection operation on a respective feature map of the N feature maps. The ROI extraction component selects a region of the feature map associated with the lowest pyramid level for distant object detection so that the object detection operation performed on that feature map is confined to the selected region.

公开(公告)号：US20240257303A1

公开(公告)日：2024-08-01

申请号：US18608582

申请日：2024-03-18

Applicant: Synaptics Incorporated

Inventor： Karthikeyan SHANMUGA VADIVEL ,  Omar OREIFEJ ,  Patrick A. WORFOLK

IPC: G06T3/4046 ,  G06T5/70 ,  G06T7/90

CPC classification number: G06T3/4046 ,  G06T5/70 ,  G06T7/90

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. In some implementations, an image processor may be configured to reduce or remove noise associated with an image based, at least in part, on a neural network. For example, the neural network may be trained to infer a denoised representation of the image. In some aspects, the image processor may scale the brightness level of the image to fall within a normalized range of values associated with the neural network. In some other aspects, a machine learning system may scale the brightness levels of input images to match the brightness levels of ground truth images used to train the neural network. Still further, in some aspects, the machine learning system may scale the brightness levels of the input images and the brightness levels of the ground truth images to fall within the normalized range of values during training.

公开(公告)号：US20220366532A1

公开(公告)日：2022-11-17

申请号：US17317227

申请日：2021-05-11

Applicant: Synaptics Incorporated

Inventor： Karthikeyan SHANMUGA VADIVEL ,  Omar OREIFEJ ,  Patrick A. WORFOLK

IPC: G06T3/40 ,  G06T5/00 ,  G06T7/90

Abstract: This disclosure provides methods, devices, and systems for low-light imaging. In some implementations, an image processor may be configured to reduce or remove noise associated with an image based, at least in part, on a neural network. For example, the neural network may be trained to infer a denoised representation of the image. In some aspects, the image processor may scale the brightness level of the image to fall within a normalized range of values associated with the neural network. In some other aspects, a machine learning system may scale the brightness levels of input images to match the brightness levels of ground truth images used to train the neural network. Still further, in some aspects, the machine learning system may scale the brightness levels of the input images and the brightness levels of the ground truth images to fall within the normalized range of values during training.