An image processing method, comprising: generating a first image (G11, G21) having a first resolution, wherein generating the first image (G11, G21) comprises selecting, from each of a plurality of pixel blocks that include at least two pixels , a pixel to include in the first image (G11, G21); searching, by processing circuitry, the first image (G11, G21) to determine whether a target object can be identified in the first image; specifying, when it is determined that the target object can be identified in the first image (Gil, G21), a region-of-interest in the first image (G11, G21); generating, when it is determined that the target object cannot be identified in the first image (G11, G21), a second image (G22) having a second resolution higher than the first resolution, wherein generating the second image (G22) comprises complementing the pixels included in the first image (G11,21) by selecting, from each of the pixel blocks , at least one pixel among the pixels not included in the first image; searching, by the processing circuitry, the second image (G22) to determine whether the target object can be identified in the second image (G22); specifying, when it is determined that the target object can be identified in the second image (G22), a region-of-interest in the second image (G22); generating a third image (G23) corresponding to the specified region-of-interest in the second image (G22), wherein the third image (G23) has a third resolution higher than the second resolution; and recognizing, by the processing circuitry, an item in the third image (G23), wherein recognizing the item in the third image (G23) comprises processing at least a portion of the third image (G23) using a machine learning classifier. generating a first image (G11, G21) having a first resolution, wherein generating the first image (G11, G21) comprises selecting, from each of a plurality of pixel blocks that include at least two pixels , a pixel to include in the first image (G11, G21); searching, by processing circuitry, the first image (G11, G21) to determine whether a target object can be identified in the first image; specifying, when it is determined that the target object can be identified in the first image (Gil, G21), a region-of-interest in the first image (G11, G21); generating, when it is determined that the target object cannot be identified in the first image (G11, G21), a second image (G22) having a second resolution higher than the first resolution, wherein generating the second image (G22) comprises complementing the pixels included in the first image (G11,21) by selecting, from each of the pixel blocks , at least one pixel among the pixels not included in the first image; searching, by the processing circuitry, the second image (G22) to determine whether the target object can be identified in the second image (G22); specifying, when it is determined that the target object can be identified in the second image (G22), a region-of-interest in the second image (G22); generating a third image (G23) corresponding to the specified region-of-interest in the second image (G22), wherein the third image (G23) has a third resolution higher than the second resolution; and recognizing, by the processing circuitry, an item in the third image (G23), wherein recognizing the item in the third image (G23) comprises processing at least a portion of the third image (G23) using a machine learning classifier. <EOS>