The present disclosure relates to an information processing apparatus, an information processing method, and a program capable of performing a process for a desired depth value range of depth data.

A maximum value and a minimum value of depth values of depth data corresponding to image data are set to thereby set a depth value range from the minimum value to the maximum value. A mask for the depth data is generated according to the depth value range thus set. For example, the present disclosure is applicable to an information processing apparatus, electronic equipment, an information processing method, a program, or the like.

A method of evaluating one or more kernels of an ear of maize using digital imagery that includes acquiring a digital image of the one or more kernels of the ear of maize without the use of spatial reference points, processing the digital image to estimate at least one physical property of the one or more kernels of the ear of maize from the digital image, and evaluating the at least one kernel of maize using the estimate of the at least one physical property of the at least one kernel of maize. The method includes using one or more such digital images to estimate yield on a plant, management zone, field, county and country level.

A method of evaluating one or more kernels of an ear of maize using digital imagery that includes acquiring a digital image of the one or more kernels of the ear of maize without the use of spatial reference points, processing the digital image to estimate at least one physical property of the one or more kernels of the ear of maize from the digital image, and evaluating the at least one kernel of maize using the estimate of the at least one physical property of the at least one kernel of maize. The method includes using one or more such digital images to estimate yield on a plant, management zone, field, county and country level.

An image object labeling method and system are disclosed. The method is executed by a processor coupled to a memory and includes providing an image file; detecting at least one first object on the image file and generating at least one graphic block and attribute thereof; performing a binarization process to present a first graphic feature on a region containing the graphic block in the image file and a second graphic feature on the rest region; combining the results of detecting and the binarization process, and filtering the image file through several gradually reduced masks to show several separated graphic components until number of the separated graphic components and the at least one graphic block are the same; and assigning a label to the separated graphic components according to the attribute, alternatively, outputting a message, receiving a command for adjusting the graphic block and attribute thereof, as the labels.

An image object labeling method and system are disclosed. The method is executed by a processor coupled to a memory and includes providing an image file; detecting at least one first object on the image file and generating at least one graphic block and attribute thereof; performing a binarization process to present a first graphic feature on a region containing the graphic block in the image file and a second graphic feature on the rest region; combining the results of detecting and the binarization process, and filtering the image file through several gradually reduced masks to show several separated graphic components until number of the separated graphic components and the at least one graphic block are the same; and assigning a label to the separated graphic components according to the attribute, alternatively, outputting a message, receiving a command for adjusting the graphic block and attribute thereof, as the labels.

Provided is an image restoration apparatus and method. The image restoration apparatus may store an image restoration model including a convolutional layer corresponding to kernels having various dilation gaps, and may restore an output image from a target image obtained by rearranging a compound eye vision (CEV) image by the image restoration model.

Provided is an image restoration apparatus and method. The image restoration apparatus may store an image restoration model including a convolutional layer corresponding to kernels having various dilation gaps, and may restore an output image from a target image obtained by rearranging a compound eye vision (CEV) image by the image restoration model.

A testing device and a testing method for detecting a stitching defect of a panoramic camera are provided. The testing method includes: accessing the panoramic camera to obtain a stitched image, wherein the stitched image includes a chart image corresponding to a chart, wherein the chart includes multiple black stripes and multiple white stripes; generating a defect image marked with the stitching defect according to the chart image; and outputting the defect image.

A testing device and a testing method for detecting a stitching defect of a panoramic camera are provided. The testing method includes: accessing the panoramic camera to obtain a stitched image, wherein the stitched image includes a chart image corresponding to a chart, wherein the chart includes multiple black stripes and multiple white stripes; generating a defect image marked with the stitching defect according to the chart image; and outputting the defect image.

A data processing method for a biochip comprises: acquiring a biochip image to be detected; performing binarization processing on the biochip image to obtain a binary image; performing a morphological dilation operation on the binary image in a row direction to obtain a first image, and performing a morphological dilation operation on the binary image in a column direction to obtain a second image; performing connected domain detection on the first image in the row direction, and performing connected domain detection on the second image in the column direction, to determine the number of rows and the number of columns of a sample point array and center position information of each sample point.