Computer-implemented method of detecting foreign object on background object in an image, apparatus for detecting foreign object on background object in an image, and computer-program product

An apparatus for detecting a foreign object on a background object in an image, comprising:

• a memory;
• and
• one or more processors;
• wherein the memory and the one or more processors are connected with each other;
• and
• the memory stores computer-executable instructions for controlling the one or more processors to:
• extract image features of the image based on image characteristics of the background object and a suspected foreign object;
• detect a salient region in the image based on a human visual attention model;
• generate a salient region mask based on detection of the salient region;
• obtain the image features in a region surrounded by the salient region mask;
• perform feature combination and feature vector length normalization on the image features of the region surrounded by the salient region mask to generate normalized feature vectors;
• identify the suspected foreign object using an image classifier, wherein the image classifier uses the normalized feature vectors as input for identifying the suspected foreign object;
• and
• determine a color consistency of the region surrounded by the salient region mask;
• calculate variance of hue features of the region surrounded by the salient region mask;
• and
• determine whether the variance of the hue features is greater than a first threshold value;
• wherein the variance of the hue features being greater than the first threshold value indicates the color consistency of the region surrounded by the salient region mask being below a second threshold value.

A computer-program product comprising a non-transitory tangible computer-readable medium having computer-readable instructions thereon, the computer-readable instructions being executable by a processor to cause the processor to perform:

• extracting image features of an image based on image characteristics of a background object and a suspected foreign object in the image;
• detecting a salient region in the image based on a human visual attention model;
• generating a salient region mask based on detection of the salient region;
• obtaining the image features in a region surrounded by the salient region mask;
• performing feature combination and feature vector length normalization on the image features of the region surrounded by the salient region mask to generate normalized feature vectors;
• identifying the suspected foreign object using an image classifier, wherein the image classifier uses the normalized feature vectors as input for identifying the suspected foreign object;
• and
• determining a color consistency of the region surrounded by the salient region mask;
• wherein determining the color consistency comprises:
• calculating variance of hue features of the region surrounded by the salient region mask;
• and
• determining whether the variance of the hue features is greater than a first threshold value;
• wherein the variance of the hue features being greater than the first threshold value indicates the color consistency of the region surrounded by the salient region mask being below a second threshold value.

A computer-implemented method of detecting a foreign object on a background object in an image, comprising:

• extracting image features of the image based on image characteristics of the background object and a suspected foreign object;
• detecting a salient region in the image based on a human visual attention model;
• generating a salient region mask based on detection of the salient region;
• obtaining the image features in a region surrounded by the salient region mask;
• performing feature combination and feature vector length normalization on the image features of the region surrounded by the salient region mask to generate normalized feature vectors;
• identifying the suspected foreign object using an image classifier, wherein the image classifier uses the normalized feature vectors as input for identifying the suspected foreign object;
• and
• pre-training an initial image classifier to obtain the image classifier;
• wherein pre-training the initial image classifier comprises:
• providing a training sample, a training classification label corresponding to the training sample, training image features corresponding to the training sample, and a training salient region mask corresponding to the training sample, the training image features comprising training features of a first type and training features of a second type;
• performing feature combination and feature vector length normalization on the training image features of a region surrounded by the training salient region mask to generate training normalized feature vectors;
• classifying the training sample based on the training normalized feature vectors to generate an initial classification label;
• determining a degree of match between the initial classification label and the training classification label;
• tuning the initial image classifier based on the degree of match;
• providing the training image having a training foreign object on a training background object;
• extracting training image features of the training image based on image characteristics of the training background object and the training foreign object;
• detecting a training salient region in the training image based on the human visual attention model;
• generating the training salient region mask based on detection of the training salient region;
• and
• obtaining the training image features in a region surrounded by the training salient region mask.