9 results about "Temporal filter" patented technology

In various embodiments, when reference picture resampling is applied to achieve better coding efficiency, methods and devices adapt temporal filtering of a noise-reduced input video prior to encoding according to a resolution ratio. According to one embodiment, temporal filtering strength values are fine-tuned at a pre-processing module according to the resolution ratio of the RPR mode. According to another embodiment, temporal filtering is deferred after rescaling when the RPR mode is enabled, and filtering strength values are fine-tuned according to the resolution ratio of the RPR mode. A third embodiment proposes to refine motion estimation in the encoder by setting the block size of motion estimation in temporal filtering according to the resolution ratio of the RPR mode.

A system and method for identifying container numbers on front-mounted cranes are disclosed, relating to the field of container number identification on front-mounted cranes. To address the shortcomings of existing methods, such as inability to adapt to rainy or snowy weather and low-light conditions, poor container number identification performance, and poor anti-interference capabilities, this invention acquires video or images of containers on front-mounted cranes; extracts continuous frames from the video at a fixed frequency and performs grayscale processing on each frame to reduce computational complexity; applies temporal filtering to the preprocessed video / image; applies spatial filtering to the filtered video / image to remove isolated noise points; reduces high-frequency noise and smooths the image through weighted averaging; enhances image contrast by adjusting the grayscale distribution of the image, making the container number area clearer; and performs container number area detection and character recognition on the processed image to achieve automatic container number identification. This invention is mainly used for identifying container numbers on front-mounted cranes.

This paper presents a method for fusing and reconstructing trajectory data from multiple sources in hybrid traffic flow, relating to the fields of multi-source sensor fusion localization and target tracking. The method acquires the 3D acceleration sequence and RGB video stream of the target object, extracting the geometric center coordinate sequence. Based on the frequency domain energy distribution of acceleration, the target's fundamental frequency parameters are obtained. Temporal filtering coefficients are constructed using the dynamic sampling rate of the video, and the geometric center coordinates are filtered to obtain a corrected center coordinate sequence, which is then mapped to a measurement vector. Kinematic state prediction is performed using acceleration to obtain prior inferred state variables, and then posterior updates are performed using the measurement vectors to obtain the optimal spatial position. The reconstructed trajectory sequence is generated by stitching the data together, and the sliding time window is updated based on feedback-optimized parameters. This method effectively eliminates spurious displacements caused by gait, fundamentally eliminating jagged oscillations in the trajectory, resulting in a smooth and stable fused trajectory.

The application discloses a training method and a decoding method of a single-time spatial-temporal filter based on double-frequency intermodulation, and the training method comprises the following steps: displaying a plurality of training targets in a row-column distribution on a visual stimulation interface, wherein each training target is jointly coded by a first stimulation signal and a second stimulation signal; collecting single-time training data of a subject; pre-processing the single-time training data to obtain ; performing time-domain displacement processing on the pre-processed mean template of the training target to obtain a time-shifted template; integrating the pre-processed mean template and the time-shifted template of the plurality of training targets to obtain a training target matrix; obtaining a standard reference signal of the training target, wherein the standard reference signal comprises a double-frequency intermodulation component; obtaining a training target reference signal matrix according to the standard reference signal; and obtaining a first spatial-temporal filter according to the training target reference signal matrix. The application achieves the technical effect that a spatial-temporal filter with good performance is obtained by using single-time training data, and provides a new method for efficient encoding and decoding of a large-instruction-set visual evoked brain-computer interface.

Provided is a method for encoding a video, an electronic device and a storage medium, relating to the field of video encoding. The method includes: obtaining a reference frame that is adjacent to a current frame and has established hierarchical division of an initial ROI; reusing a motion vector generated by the reference frame in a motion compensated temporal filter process to determine an ROI position prediction value of the current frame; extracting facial feature points from the current frame, and correcting the ROI position prediction value based on a spatial topological relationship of feature points in the initial ROI; configuring differentiated quantization parameters for a main face region, a secondary face region and a background region after correction respectively; dynamically allocating a three-region bitrate according to a real-time network bandwidth; and outputting an encoded frame of the current frame and associated ROI-level metadata.

A method for a multi-antenna receiver arrangement, the multi-antenna receiver arrangement being comprisable in a wireless device, WD, the method comprising: receiving a first plurality of analog radio signals; converting the first plurality of analog radio signals into a first plurality of digital signals; extracting reference signals from each of the first plurality of digital signals; determining characteristics for each of the first plurality of digital signals based on the extracted reference signals; processing the first plurality of digital signals with a second plurality of spatial-temporal filters to obtain a second plurality of combined signals, wherein the first plurality is larger than the second plurality. Corresponding computer program product, multi-antenna receiver arrangement, and wireless device are also disclosed.

Embodiments of the present application provide an image denoising method and an image processing device. The method comprises: obtaining a tth input image and a tth reference image; determining a first weight map based on the tth input image and the tth reference image; performing a guided filtering operation on the first weight map with the tth input image as a guide image to generate a second weight map; and performing a temporal filtering operation on the tth input image and the tth reference image based on the second weight map to generate a tth output image.

The application discloses a training method of a single-time spatial-temporal filter based on a periodic characteristic and a discriminant analysis method, and comprises the following steps: collecting original steady-state visual evoked potentials of a subject when a video frequency is f n ; pre-processing the original steady-state visual evoked potentials to obtain a sub-band; dividing the sub-band according to a length to obtain a number of data segments with the length, and the value of the length increases with the increase of the frequency f n ; averaging the data segments to obtain, and connecting multiple to obtain ; expanding and enhancing to obtain ; projecting onto a subspace spanned by an ideal reference signal to obtain ; re-enhancing to obtain ; calculating an inter-class difference matrix and an intra-class difference matrix; calculating a scatter matrix and ; obtaining a projection subspace capable of effectively classifying all classes by using generalized eigenvalue decomposition, and obtaining the spatial-temporal filter according to the projection subspace. The application achieves the technical effect of reducing the training cost of the SSVEP spatial-temporal filter.

This invention provides a spatiotemporal video denoising method based on PID control, comprising: S1. Spatial filtering of the current frame; S2. Spatial filtering of the reference frame; S3. Calculating the spatial filtering weights of the reference frame: calculating the optimal spatial filtering weights of the reference frame using a PID adjustment method; S4. Calculating the temporal filtering weights: calculating the optimal temporal fusion weights of the reference frame using a PID adjustment method; S5. Spatiotemporal fusion to obtain the final processing result. In this method, the PID adjustment method smoothly adjusts the weighted weights of the reference frames in moving and stationary regions, making the transition between the imaging effects in moving and stationary regions smoother and the visual effect more natural. The integral control part of the PID adjustment method can effectively eliminate the problem of inaccurate motion judgment caused by noise, and can effectively avoid the phenomenon of motion blur.