Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

Abstract: A system and method of automated classification of rock types includes: partitioning, by a processing device, an image into partitions; extracting, by the processing device, sub-images from each of the partitions; first-level classifying, by an automated classifier, the sub-images into corresponding first classes; and second-level classifying, by the processing device, the partitions into corresponding second classes by, for each partition of the partitions, selecting a most numerous one of the corresponding first classes of the sub-images extracted from the partition. A method of displaying automated classification results on a display device is provided. The method includes: receiving, by a processing device, an image partitioned into partitions and classified into corresponding classes; and manipulating, by the processing device, the display device to display the image together with visual identification of the partitions and their corresponding classes.

Abstract: A system and method of automated classification of rock types includes: partitioning, by a processing device, an image into partitions; extracting, by the processing device, sub-images from each of the partitions; first-level classifying, by an automated classifier, the sub-images into corresponding first classes; and second-level classifying, by the processing device, the partitions into corresponding second classes by, for each partition of the partitions, selecting a most numerous one of the corresponding first classes of the sub-images extracted from the partition. A method of displaying automated classification results on a display device is provided. The method includes: receiving, by a processing device, an image partitioned into partitions and classified into corresponding classes; and manipulating, by the processing device, the display device to display the image together with visual identification of the partitions and their corresponding classes.

Abstract: A method for training an automated classifier of input images includes: receiving, by a processing device, a convolution neural network (CNN) model; receiving, by the processing device, training images and corresponding classes, each of the corresponding classes being associated with several ones of the training images; preparing, by the processing device, the training images, including separating the training images into a training set of the training images and a testing set of the training images; and training, by the processing device, the CNN model utilizing the training set, the testing set, and the corresponding classes to generate the automated classifier.

Abstract: Described is a system for collision detection and avoidance estimation using sub-region based optical flow. During operation, the system estimates time-to-contact (TTC) values for an obstacle in multiple regions-of-interest (ROI) in successive image frames as obtained from a monocular camera. Based on the TTC values, the system detects if there is an imminent obstacle. If there is an imminent obstacle, a path for avoiding the obstacle is determined based on the TTC values in the multiple ROI. Finally, a mobile platform is caused to move in the path as determined to avoid the obstacle.

Abstract: Described is a system and method for accurate image and/or video scene classification. More specifically, described is a system that makes use of a specialized convolutional-neural network (hereafter CNN) based technique for the fusion of bottom-up whole-image features and top-down entity classification. When the two parallel and independent processing paths are fused, the system provides an accurate classification of the scene as depicted in the image or video.

Abstract: Described is a system for frontal and side doorway detection. Salient line segments are extracted from an image frame captured of an indoor environment. Existence of a vanishing point in the image frame is determined. If a vanishing point is detected with a confidence score that meets or exceeds a predetermined confidence score, then the system performs side doorway detection via a side doorway detection module. If a vanishing point is detected with a confidence score below the predetermined confidence score, then the system performs frontal doorway detection via a frontal doorway detection module. A description of detected doorways is output and used by a mobile robot (Unmanned Aerial Vehicle) to autonomously navigate the indoor environment.

Abstract: Described is a robotic system for detecting obstacles reliably with their ranges by a combination of two-dimensional and three-dimensional sensing. In operation, the system receives an image from a monocular video and range depth data from a range sensor of a scene proximate a mobile platform. The image is segmented into multiple object regions of interest and time-to-contact (TTC) value are calculated by estimating motion field and operating on image intensities. A two-dimensional (2D) TTC map is then generated by estimating average TTC values over the multiple object regions of interest. A three-dimensional TTC map is then generated by fusing the range depth data with image. Finally, a range-fused TTC map is generated by averaging the 2D TTC map and the 3D TTC map.

Abstract: Described is a robotic system for detecting obstacles reliably with their ranges by a combination of two-dimensional and three-dimensional sensing. In operation, the system receives an image from a monocular video and range depth data from a range sensor of a scene proximate a mobile platform. The image is segmented into multiple object regions of interest and time-to-contact (TTC) value are calculated by estimating motion field and operating on image intensities. A two-dimensional (2D) TTC map is then generated by estimating average TTC values over the multiple object regions of interest. A three-dimensional TTC map is then generated by fusing the range depth data with image. Finally, a range-fused TTC map is generated by averaging the 2D TTC map and the 3D TTC map.