Methods and apparatus for tracking and discerning objects using their saliency. In one embodiment of the present disclosure, the tracking of objects is based on a combination of object saliency and additional sources of signal about object identity. Under certain simplifying assumptions, the present disclosure allows for robust tracking of simple objects with limited processing resources. In one or more variants, efficient implementation of the methods described allow sensors (e.g., cameras) to be used on board a robot (or autonomous vehicle) on a mobile determining platform, such as to capture images to determine the presence and/or identity of salient objects. Such determination of salient objects allow for e.g., adjustments to vehicle or other moving object trajectory.

There is provided an image processing apparatus. An obtaining unit obtains a first image to which tone conversion processing conforming to a first input/output characteristic having a first maximum output luminance value has been applied. A generation unit generates first correction information for correcting a luminance value of the first image based on a difference regarding an output luminance value between the first input/output characteristic and a second input/output characteristic having a second maximum output luminance value, and on second correction information for correcting a luminance value of a second image to which tone conversion processing conforming to the second input/output characteristic has been applied. A correction unit corrects a luminance value of the first image in conformity to the first correction information.

Methods and apparatus for tracking and discerning objects using their saliency. In one embodiment of the present disclosure, the tracking of objects is based on a combination of object saliency and additional sources of signal about object identity. Under certain simplifying assumptions, the present disclosure allows for robust tracking of simple objects with limited processing resources. In one or more variants, efficient implementation of the methods described allow sensors (e.g., cameras) to be used on board a robot (or autonomous vehicle) on a mobile determining platform, such as to capture images to determine the presence and/or identity of salient objects. Such determination of salient objects allow for e.g., adjustments to vehicle or other moving object trajectory.

Methods and apparatus for tracking and discerning objects using their saliency. In one embodiment of the present disclosure, the tracking of objects is based on a combination of object saliency and additional sources of signal about object identity. Under certain simplifying assumptions, the present disclosure allows for robust tracking of simple objects with limited processing resources. In one or more variants, efficient implementation of the methods described allow sensors (e.g., cameras) to be used on board a robot (or autonomous vehicle) on a mobile determining platform, such as to capture images to determine the presence and/or identity of salient objects. Such determination of salient objects allow for e.g., adjustments to vehicle or other moving object trajectory.

Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

An information processing apparatus includes an acquisition unit configured to acquire image data, a generation unit configured to generate information about time as generated additional data, and a replacement unit configured to replace data at a plurality of pixel positions of the acquired image data with the generated additional data.

Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.

Apparatus and methods for detecting and utilizing saliency in digital images. In one implementation, salient objects may be detected based on analysis of pixel characteristics. Least frequently occurring pixel values may be deemed as salient. Pixel values in an image may be compared to a reference. Color distance may be determined based on a difference between reference color and pixel color. Individual image channels may be scaled when determining saliency in a multi-channel image. Areas of high saliency may be analyzed to determine object position, shape, and/or color. Multiple saliency maps may be additively or multiplicative combined in order to improve detection performance (e.g., reduce number of false positives). Methodologies described herein may enable robust tracking of objects utilizing fewer determination resources. Efficient implementation of the methods described below may allow them to be used for example on board a robot (or autonomous vehicle) or a mobile determining platform.