Example systems and methods to transform events and/or mood associated with playing media into lighting effects are disclosed herein. An example apparatus includes a content identifier to identify a first event occurring during presentation of media content at a first time. The example apparatus includes a content driven analyzer to determine a first lighting effect to be produced by a light-producing device based on the first event and instruct the light-producing device to produce the first lighting effect based on the first event during presentation of the media content. The content identifier is to identify a second media event occurring during presentation of the media content at a second time after the first time. The content driven analyzer is to instruct the light-producing device to one of maintain the first lighting effect based on the second event or produce a second lighting effect based on the second event during presentation of the media content.

A method of determining a summation of pixel characteristics for a rectangular region of a digital image includes determining if a base address for a data element in an integral image buffer is aligned for an SIMD operation by a processor embedded in an electronic assembly configured to perform Haar-like feature calculations. The data element represents a corner of the rectangular region of an integral image. The integral image is a representation of the digital image. The integral image is formed by data elements stored in the integral image buffer. The data element is loaded from the integral image buffer to the processor when the base address is aligned for the SIMD operation. An offset data element of an offset integral image is loaded from an offset integral buffer when the base address is non-aligned for the SIMD operation. The offset data element represents the corner of the rectangular region.

Techniques are provided herein for generating an integral image of an input image in parallel across the cores of a multi-core processor. The input image is split into a plurality of tiles, each of which is stored in a scratchpad memory associated with a distinct core. At each tile, a partial integral image of the tile is first computed over the tile, using a Single-Pass Algorithm. This is followed by aggregating partial sums belonging to subsets of tiles using a 2D Inclusive Parallel Prefix Algorithm. A summation is finally performed over the aggregated partial sums to generate the integral image over the entire input image.

Aspects of the present disclosure provide an image-based face detection and recognition system that processes and/or analyzes portions of an image using image strips and cascading classifiers to detect faces and/or various facial features, such an eye, nose, mouth, cheekbone, jaw line, etc.

The present disclosure provides systems and methods that measure soil roughness in a field from imagery of the field. In particular, the present subject matter is directed to systems and methods that include or otherwise leverage a machine-learned clod detection model to determine a soil roughness value for a portion of a field based at least in part on imagery of such portion of the field captured by an imaging device. For example, the imaging device can be a camera positioned in a downward-facing direction and physically coupled to a work vehicle or an implement towed by the work vehicle through the field.

An image processing device includes a data processing unit that processes data of a face image which is captured to include a face. The data processing unit generates an edge image by filtering the face image to detect an edge in a scanning direction, extracts a sampling value as the information regarding a gradient magnitude and whether the gradient is positive or negative from each of positions in the edge image corresponding to a plurality of points constituting the sampling curve, calculates a likelihood with respect to the sampling curve by setting points having a positive gradient and a negative gradient as likelihood evaluation targets in a first point group and a second point group, and detects a sampling curve having a maximum likelihood as a pupil or an iris among a plurality of sampling curves.

Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

Described is a novel method for feature extraction for automatic gait recognition. This method uses Multi-kernel Fuzzy-based Local Gabor Binary Pattern. From a captured gait video sequence, the gait period is determined then a gait energy image is constructed to represent the spatial-temporal variations during one motion cycle of the gait sequence. Then, each gait sequence is represented with a feature vector. The computation of this vector is conducted by first applying the 2D Gabor filter bank then encoding the variations in the Gabor magnitude using a multi-kernel fuzzy local binary pattern operator. Finally, gait classification is performed using a support vector machine.

In order to eliminate erroneous detection in a case where a plurality of facial regions are detected in a captured image, facial detection device (2) of the present disclosure is a facial detection device that detects a facial region of a person from captured images which are continuous in time series, including a processor (15) that performs facial detection processing of detecting the facial region from the captured images and error determination processing of calculating a moving direction of each facial region between the captured images that are sequential in time series, and determining whether or not the detection as a facial region is correct with respect to a plurality of facial regions in which a correlation degree in the moving directions of the facial regions is equal to or larger than a predetermined threshold value, in a case where a plurality of facial regions are detected.

A device and a computer implemented method of anomaly detection, including processing a digital representation of a signal or image with a wavelet decomposition to generate a first, second, and third plurality of decomposed representations, processing a first decomposed representation of the third plurality of decomposed representations with a first generative model to determine a first likelihood, processing a second decomposed representation of the third plurality of decomposed representations with a second generative model to determine a second likelihood, processing a second decomposed representation of the second plurality of decomposed representations with a third generative model to determine a third likelihood, processing a second decomposed representation of the first plurality of decomposed representations with a fourth generative model to determine a fourth likelihood, detecting an anomaly when at least one of the first likelihood, the second likelihood, the third likelihood and the fourth likelihood meets a criterium for anomaly detection.