Abstract: The present disclosure provides an adaptive stereo matching optimization method, apparatus, and device, and a storage medium. The method includes: acquiring images of at least two perspectives of the same target scene, accordingly obtaining, through calculation, disparity value ranges corresponding to pixels in the target scene; and obtaining optimized depth value ranges by adjusting the disparity value ranges of the pixels in the target scene in real time through an adaptive stereo matching model; adjusting an execution cycle in the adaptive stereo matching model in real time through a DVFS algorithm according to a resource constraint condition of the processing system; and/or training on a plurality of scene image data sets through a convolutional neural network, so that the specific function parameters in the adaptive stereo matching model are correspondingly adjusted in real time according to the acquired different scene images.

Abstract: Example embodiments relate to methods for disseminating scaling information and applications thereof in very large scale integration (VLSI) implementations of fixed-point fast Fourier transforms (FFTs). One embodiment includes a method for disseminating scaling information in a system. The system includes a linear decomposable transformation process and an inverse process of the linear decomposable transformation process. The inverse process of the linear decomposable transformation process is defined, in time or space, as an inverse linear decomposable transformation process. The linear decomposable transformation process is separated from the inverse linear decomposable transformation process. The linear decomposable transformation process or the inverse linear decomposable transformation process is able to be performed first and is defined as a linear decomposable transformation I. The other remaining process is performed subsequently and is defined as a linear decomposable transformation II.

Abstract: The present disclosure provides an adaptive stereo matching optimization method, apparatus, and device, and a storage medium. The method includes: acquiring images of at least two perspectives of the same target scene, accordingly obtaining, through calculation, disparity value ranges corresponding to pixels in the target scene; and obtaining optimized depth value ranges by adjusting the disparity value ranges of the pixels in the target scene in real time through an adaptive stereo matching model; adjusting an execution cycle in the adaptive stereo matching model in real time through a DVFS algorithm according to a resource constraint condition of the processing system; and/or training on a plurality of scene image data sets through a convolutional neural network, so that the specific function parameters in the adaptive stereo matching model are correspondingly adjusted in real time according to the acquired different scene images.

Abstract: An efficient parallel computing method for a box filter, includes: step 1, with respect to a given degree of parallelism N and a radius r of the filter kernel, establishing a first architecture provided without an extra register and a second architecture provided with the extra register; step 2, building a first adder tree for the first architecture and a second adder tree for the second architecture, respectively; step 3, searching the first adder tree and the second adder tree from top to bottom, calculating the pixel average corresponding to each filter kernel by using the first adder tree and the second adder tree, respectively, and counting resources required to be consumed by the first architecture and the second architecture, respectively; and, step 4, selecting one architecture consuming a relatively small resources from the first architecture and the second architecture for computing the box filter.

Abstract: An efficient parallel computing method for a box filter, includes: step 1, with respect to a given degree of parallelism N and a radius r of the filter kernel, establishing a first architecture provided without an extra register and a second architecture provided with the extra register; step 2, building a first adder tree for the first architecture and a second adder tree for the second architecture, respectively; step 3, searching the first adder tree and the second adder tree from top to bottom, calculating the pixel average corresponding to each filter kernel by using the first adder tree and the second adder tree, respectively, and counting resources required to be consumed by the first architecture and the second architecture, respectively; and, step 4, selecting one architecture consuming a relatively small resources from the first architecture and the second architecture for computing the box filter.