Abstract: This application describes systems and methods for facilitating memory access on flash drives. An example method may start with receiving a read command on a flash memory from a host specifying a logic block address (LBA). The flash memory may include a plurality of blocks grouped into a plurality of super blocks, and each of the plurality of blocks may include a plurality of pages. The method may further include determining a zone identification and an LBA offset based on the LBA; determining a flash physical address (FPA) corresponding to the LBA by accessing a mapping table stored in a random access memory (RAM) according to the zone identification and the LBA offset (e.g., the mapping table includes a plurality of FPAs arranged in a plurality of zones corresponding to the plurality of super blocks); and determining a page number and a block identification corresponding to the FPA.

Abstract: A solid state drive (SSD) includes an NAND memory and an SSD controller. The SSD controller includes an interface coupled to a host machine, a nonvolatile memory controller coupled to the interface, and a processor coupled to the nonvolatile memory controller. The SSD controller is configured to: receive, via the interface, a write command from the host machine; process, by the nonvolatile memory controller, the write command; transmit, from the nonvolatile memory controller to the processor, a system message; process, by the processor according to Zoned Namespaces (ZNS) protocol, the system message; obtain, by the nonvolatile memory controller via the interface, host data for storage from the host machine; and write the host data to the NAND memory based on a result of processing the system message. Processing the system message by the processor and obtaining the host data by the nonvolatile memory controller are executed in parallel.

Abstract: An SSD includes an MRAM, an NAND memory, and an SSD controller. The SSD controller is configured to receive first data from a host machine, save the first data to an SSD data buffer, fetch the first data from the SSD data buffer and write the first data to the MRAM via the MRAM controller, determine, by the data allocation circuit based on a characteristic of the first data, whether to save the first data to the MRAM or the NAND memory, and in response to determining saving the first data to the NAND memory, read the first data from the MRAM, write the first data to the NAND memory, and erase the first data from the MRAM.

Abstract: An image processing device according to one embodiment includes an acquisition unit, a generation unit, a calculation unit, and an estimation unit. The acquisition unit acquires an input image. The generation unit generates a plurality of comparison images by superimposing each of a plurality of noises with different densities from each other on a target region being at least part of the input image. The calculation unit calculates, for each of the plurality of comparison images, the degradation level of the comparison image with respect to the input image. The estimation unit estimates the noise level of the input image based on a plurality of calculated degradation levels.

Abstract: To increase accuracy in extracting a foreground area while saving a user time and effort. Image obtaining means of an image processing device obtains an image including a background and an object. Element area setting means sets, with respect to the image, a plurality of element areas respectively corresponding to a plurality of elements in the image. Overlapped area specifying means specifies an overlapped area in which a degree of overlap of the element areas is greater than or equal to a predetermined value in the image. Foreground area extracting means extracts a foreground area corresponding to the object from the image based on the overlapped area.

Abstract: An image processing device according to one embodiment includes a processor. The processor executes a step of acquiring an input image, a step of calculating a feature residual by processing the input image in a convolutional layer, a step of performing at least one convolution on the input image, a step of generating an output feature by applying the feature residual to the convolved input image, and a step of generating an image residual based on the output feature. The image residual is applied to the input image, and thereby a high-resolution image with higher resolution than the input image is generated.

Abstract: A computer implemented method using artificial intelligence for matching images with locations and directions by acquiring a plurality of panoramic images, detecting objects and their locations in each of the panoramic images, acquiring a floorplan image, detecting objects and their locations in the floorplan image, comparing the objects and locations detect in each of the panoramic image to the objects and locations detected in the floorplan image, and determining a location in the floorplan image where each panoramic image was taken.

Abstract: An image processing device according to one embodiment includes a processor. The processor executes a step of acquiring an input image, a step of calculating a feature residual by processing the input image in a convolutional layer, a step of performing at least one convolution on the input image, a step of generating an output feature by applying the feature residual to the convolved input image, and a step of generating an image residual based on the output feature. The image residual is applied to the input image, and thereby a high-resolution image with higher resolution than the input image is generated.

Abstract: A 3D model generating system for accurately estimating a shape of an entire space surrounded by a plurality of surfaces is provided. Image acquiring means of the 3D model generating system acquires a plurality of images respectively capturing different directions in a space surrounded by a plurality of surfaces. Partial model generating means generates a plurality of partial models respectively corresponding to the images, each of the partial models representing a portion of the surfaces captured in the images. Overall model generating means generates an overall model representing an entire surface based on the partial models generated by the partial model generating means.

Abstract: Processing load on a computer can be reduced while saving the labor of photographing an object to estimate a size of the object. Photographed image acquiring means of a 3D model generating system acquires a photographed image in which a first object and a second object are disposed in a real space. Virtual image generating means generates a virtual image in which a first model representing the first object that is a standard size and a second model representing the second object that is not the standard size are disposed in a virtual space. Parameter determining means determines a size parameter indicating a size of the second object based on a result of comparing the photographed image with the virtual image.

Abstract: A 3D model generating system for accurately estimating a shape of an entire space surrounded by a plurality of surfaces is provided. Image acquiring means of the 3D model generating system acquires a plurality of images respectively capturing different directions in a space surrounded by a plurality of surfaces. Partial model generating means generates a plurality of partial models respectively corresponding to the images, each of the partial models representing a portion of the surfaces captured in the images. Overall model generating means generates an overall model representing an entire surface based on the partial models generated by the partial model generating means.

Abstract: A computer implemented method using artificial intelligence for matching images with locations and directions by acquiring a plurality of panoramic images, detecting objects and their locations in each of the panoramic images, acquiring a floorplan image, detecting objects and their locations in the floorplan image, comparing the objects and locations detect in each of the panoramic, image to the objects and locations detected in the floorplan image, and determining a location in the floorplan image where each panoramic image was taken.

Abstract: A 3D model generating system for accurately estimating a shape of an entire space surrounded by a plurality of surfaces is provided. Image acquiring means of the 3D model generating system acquires a plurality of images respectively capturing different directions in a space surrounded by a plurality of surfaces. Partial model generating means generates a plurality of partial models respectively corresponding to the images, each of the partial models representing a portion of the surfaces captured in the images. Overall model generating means generates an overall model representing an entire surface based on the partial models generated by the partial model generating means.

Abstract: A 3D model generating system for accurately estimating a shape of an entire space surrounded by a plurality of surfaces is provided. Image acquiring means of the 3D model generating system acquires a plurality of images respectively capturing different directions in a space surrounded by a plurality of surfaces. Partial model generating means generates a plurality of partial models respectively corresponding to the images, each of the partial models representing a portion of the surfaces captured in the images. Overall model generating means generates an overall model representing an entire surface based on the partial models generated by the partial model generating means.

Abstract: An image processing device according to one embodiment includes an acquisition unit, a generation unit, a calculation unit, and an estimation unit. The acquisition unit acquires an input image. The generation unit generates a plurality of comparison images by superimposing each of a plurality of noises with different densities from each other on a target region being at least part of the input image. The calculation unit calculates, for each of the plurality of comparison images, the degradation level of the comparison image with respect to the input image. The estimation unit estimates the noise level of the input image based on a plurality of calculated degradation levels.

Abstract: A method of using a computer to semantically segment an image using a convolutional neural network system where a processor configured to convolve an input image with a plurality of filters and outputting a first output volume, pool the first output volume and creating a first activation map, determine the level of influence of the first activation map on the semantic segmentation, up-pool the first activation map to form an output image having a same number of pixels as the input image, output a probabilistic segmentation result, labeling each pixel's probability that it is a particular label, and the determination of the level of influence of the first activation map on the semantic segmentation is done using a gate layer that is positioned between a pooling layer and an up-pooling layer.

Abstract: A system for and method for improved computer vision image processing and image segmentation and recognition is disclosed. The system and method utilizes a cost function approach for improved image segmentation and recognition. In particular, a global cost function is defined and then the global cost function is minimized. This global cost function takes into account two processing pipelines of data determined by the operation of two different processing technologies upon the target input image. Constraints are utilized to ensure data consistency across the model and between the data pipelines. The system and method are useful for processing low quality images containing alphanumeric data such as floorplan images.

Abstract: To increase accuracy in extracting a foreground area while saving a user time and effort. Image obtaining means of an image processing device obtains an image including a background and an object. Element area setting means sets, with respect to the image, a plurality of element areas respectively corresponding to a plurality of elements in the image. Overlapped area specifying means specifies an overlapped area in which a degree of overlap of the element areas is greater than or equal to a predetermined value in the image. Foreground area extracting means extracts a foreground area corresponding to the object from the image based on the overlapped area.

Abstract: A method of using a computer to semantically segment an image using a convolutional neural network system where a processor configured to convolve an input image with a plurality of filters and outputting a first output volume, pool the first output volume and creating a first activation map, determine the level of influence of the first activation map on the semantic segmentation, up-pool the first activation map to form an output image having a same number of pixels as the input image, output a probabilistic segmentation result, labeling each pixel's probability that it is a particular label, and the determination of the level of influence of the first activation map on the semantic segmentation is done using a gate layer that is positioned between a pooling layer and an up-pooling layer.

Abstract: A system for and method for improved computer vision image processing and image segmentation and recognition is disclosed. The system and method utilizes a cost function approach for improved image segmentation and recognition. In particular, a global cost function is defined and then the global cost function is minimized. This global cost function takes into account two processing pipelines of data determined by the operation of two different processing technologies upon the target input image. Constraints are utilized to ensure data consistency across the model and between the data pipelines. The system and method are useful for processing low quality images containing alphanumeric data such as floorplan images.