Abstract: In one implementation, a method for determining accurate store traffic levels includes receiving, at a computer system, historical store data for a physical store that was generated by internal data sources located within the physical store; generating, by the computer system, a historical store model that correlates values from store data with guest traffic levels within the physical store; receiving, by the computer system, current store data from one or more of the internal data sources; determining, by the computer system, current guest traffic level in the physical store based on the current store data and the historical store model; and outputting, by the computer system, the current guest traffic level in the physical store.

Abstract: In one implementation, a method for determining accurate store traffic levels includes receiving, at a computer system, historical store data for a physical store that was generated by internal data sources located within the physical store; generating, by the computer system, a historical store model that correlates values from store data with guest traffic levels within the physical store; receiving, by the computer system, current store data from one or more of the internal data sources; determining, by the computer system, current guest traffic level in the physical store based on the current store data and the historical store model; and outputting, by the computer system, the current guest traffic level in the physical store.

Abstract: A smart electric toothbrush showing the results of brushing by determining the brushing posture, which includes a brush handle and a brush head mounted on the brush handle, a motor in the brush head driving the brush head to work, and a controlling component electrically connected to the motor. A posture detection unit is located in the brush head electrically connected to the controlling component, and the posture detection unit includes a three-axis gyroscope and a three-axis accelerometer. Roll and pitch angles of the smart electric toothbrush are detected by the posture detection unit, and a brushing section is determined by the controlling component by detecting a posture of brushing after complementary filtering the roll angle and the pitch angle. By matching the determined position to the 16 sections, the user can be alarmed that which section is missing or over-brushing.

Abstract: Automatic red-eye object classification in digital photographic images. A method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, RGB pixels of the candidate red-eye object are converted into YUV pixels. Then, the YUV pixels satisfying a constraint that is a function of the YUV pixels are summed. Next, the sum is determined to be greater than or equal to a scaled version of the total number of YUV pixels in the candidate red-eye object. Finally, the candidate red-eye object is transformed into a true red-eye object.

Abstract: An input image (e.g. a digital RGB color image) is subjected to an eye classifier that is targeted at discriminating a complete eye pattern from any non-eye patterns. The red-eye candidate list with associated bounding boxes that are generated by the red-eye classifier are received. The bounding rectangles are subjected to object segmentation. A connected component labeling procedure is then applied to obtain one or more red regions. The largest red region is then chosen for feature extraction. A number of features are then extracted from this region. Then these features are used to determine if the particular candidate red-eye object is a mouth.

Abstract: An input image (e.g. a digital RGB color image) is subjected to an eye classifier that is targeted at discriminating a complete eye pattern from any non-eye patterns. The red-eye candidate list with associated bounding boxes that are generated by the red-eye classifier are received. The bounding rectangles are subjected to object segmentation. A connected component labeling procedure is then applied to obtain one or more red regions. The largest red region is then chosen for feature extraction. A number of features are then extracted from this region. Then these features are used to determine if the particular candidate red-eye object is a mouth.

Abstract: Automatic red-eye object classification in digital photographic images. In a first example embodiment, a method for classifying a candidate red-eye object in a digital photographic image includes several acts. First, a candidate red-eye object in a digital photographic image is selected. Next, RGB pixels of the candidate red-eye object are converted into YUV pixels. Then, the YUV pixels satisfying a constraint that is a function of the YUV pixels are summed. Next, the sum is determined to be greater than or equal to a scaled version of the total number of YUV pixels in the candidate red-eye object. Finally, the candidate red-eye object is transformed into a true red-eye object.