Method and apparatus for processing image based on partial images

A method of processing an image, the method comprising:

• extracting a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN);
• updating a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region of the input image frame by inputting the extracted feature into the RNN;
• and
• generating an image processing result for the input image frame based on the updated hidden state,
• wherein the generating comprises:
• generating intermediate context data based on the updated hidden state in response to the current partial processing region not corresponding to a final partial processing region of the input image frame;
• and
• generating an intermediate detection result based on the intermediate context data,
• wherein in response to the intermediate detection result indicating that there is a target object in the input image frame, an image processing process on the input image frame is terminated without performing an additional image processing process on at least one subsequent partial processing region.

An apparatus for processing an image, the apparatus comprising:

• a processor configured to execute instructions;
• and
• a memory to store the instructions,
• wherein the execution of the instructions by the processor configures the processor to:
• extract a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN) being a multi-head CNN (MCNN);
• update a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region of the input image frame by inputting the extracted feature into the RNN;
• and
• generate an image processing result for the input image frame based on the updated hidden state, and
• wherein the processor is further configured to extract a plurality of intermediate features of the current partial processing region by inputting the pixel data of the current partial processing region into each of plural heads of the MCNN, and generate an encoded feature of the current partial processing region by fusing the plurality of extracted intermediate features.

An electronic device, comprising:

• a camera configured to generate an input image frame;
• a processor configured to execute instructions;
• a memory to store the instructions,
• wherein the execution of the instructions by the processor configures the processor to:
• extract a feature of a current partial processing region of an input image frame by inputting pixel data of the current partial processing region into a convolutional neural network (CNN) being a multi-head CNN (MCNN);
• update a hidden state of a recurrent neural network (RNN) for a context between the current partial processing region and at least one previous partial processing region of the input image frame by inputting the extracted feature into the RNN;
• and
• generate an image processing result for the input image frame based on the updated hidden state, and
• wherein the processor is further configured to extract a plurality of intermediate features of the current partial processing region by inputting the pixel data of the current partial processing region into each of plural heads of the MCNN, and generates an encoded feature of the current partial processing region by fusing the plurality of extracted intermediate features.