Abstract: An automated communication design analysis and construction system that includes one or more intelligent communication design servers, comprising: a normalization module that converts communication content files for different recipients to normalized intermediate format files; an objects identification and quantification module that identifies text objects and image objects in the normalized intermediate format files; a cross-recipient group analysis module configured to identify static global objects that are invariant between recipients, data variables, and variable global objects that vary between recipients in the normalized intermediate format files; and an intelligent communication content learning and constructing engine that can construct standard communication design files based on the static global objects, the data variables, and the variable global objects. A data storage stores the communication content files and the standard communication design files.

Abstract: A method for proactively creating a photo product includes storing a library of points of interest in a computer storage, detecting one or more images from a user, automatically extracting image capture locations from the images, automatically comparing the image capture locations with the points of interest in the library to obtain one or more identified points of interest, automatically identifying an occasion based on the identified points of interest in the library, automatically selecting, by the computer processor, a photo product type in part based on the occasion, automatically creating a design of a photo product of the photo product type using one or more images from the user, and manufacturing a physical photo product based on the photo product design.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: A computer-implemented method for creating a photo product design with minimal user inputs includes generating a first portion of a photo product design by a product design module in response to user input, determining one or more layout parameters from the user input to the first portion of the photo product design by a user design learning module, automatically creating a second portion of the photo product design by an intelligent product creation engine at least in part in response to the one or more layout parameters, and automatically making a physical photo product based on at least the first portion and the second portion of the photo product design.

Abstract: A system for assembling a group composite image from individual subject images is described. Often subjects of a group vary in height. Additionally, as each subject is photographed individually, different zoom factors can be applied by a camera that affects a pixel density of the image captured. The system includes a fiducial marking device that emits collimated light to form one or more fiducial markers on a subject while an image is captured by the camera. Based on a location of the fiducial markers in the image, a pixel density of the image and a reference height of the subject can be determined. The individual subject image can be scaled based on the pixel density and reference height to account for the varying subject heights and zoom factors to generate a group composite image that accurately represents the subjects of the group relative to one another.

Abstract: A method and device for generating a composite group image from subgroup images is provided. Subgroup images, each having a common background, are accessed. The boundaries of a subgroup area within each of the subgroup images is determined. At least one horizontal and at least one vertical shift factor is determined using the determined boundaries. An arrangement for the subgroup images based on the at least one horizontal and the at least one vertical shift factor is generated. The composite group image is generated by blending the subgroup images arranged in the arrangement.

Abstract: A photo product design customization system for customizing a photo product design with minimal and intuitive user inputs includes a product design module that displays a photo product design in a main design panel, a user input detection unit that can detect a first directional movement from a first dynamic object in a product type selection panel to the main design panel, or a second directional movement from a second dynamic object in a product style selection panel to the main design panel, or a third directional movement from a third dynamic object in a product style selection panel to the main design panel, and an intelligent product design creation engine that can automatically change the photo product design in the main design panel in response to detection of the first, the second, or the third directional movement.

Abstract: A repositionable display panel includes a rigid image plate that includes a front surface configured to receive a colorant to form an image thereon and a back surface opposite to the first surface. The rigid image plate is defined by an image plate thickness and image plate widths. A spacer block is defined by a spacer block thickness and spacer block widths smaller than the image plate widths. A pressure-sensitive adhesive layer between the back surface of the rigid image plate and the spacer block bonds the rigid image plate and the spacer block together.

Abstract: A method of tracking digital images includes inputting data identifying a subject of an image into a camera, acquiring an image with the camera, and storing the image and the inputted data, as metadata, in an image file when the image is acquired. The method can be implemented using a scanner, a digital camera, and a data processor. The scanner obtains the identifying data and transmits the data to the camera. The camera obtains digital images and embeds the data into digital image files encoding the digital images. The identifying data has a format different from any of the formats processable by the digital camera. The data processor converts the format of the identifying data to one of the plurality of formats processable by the digital camera loads the converted information into the digital camera as metadata.

Abstract: Devices and methods for conducting a remote photography session are described. In some instances, a computing device at a photography station receives messages from a photography station controller which is remote from the photography station. In some examples, the computing device receives one or more messages instructing the computing device to capture an image from an image capture device. The computing device can also receive one or more messages instructing the computing device to adjust the image capture device. Additionally, the messages may instruct the computing device to present instructions to a subject of the photography session. These instructions can prompt the subject to make adjustments to meet a criteria for a photograph in the photography session.

Abstract: A method of identifying a background type in a photograph includes extracting a background image from a photograph, feeding the background image into a first convolution neural network to obtain a first decision, extracting color features in the background image, transforming the color features into a two-dimensional color feature matrix, feeding the two-dimensional color feature matrix into a second convolution neural network to obtain a second decision by the one or more computer processors, extracting texture features in the background image, transforming the texture features into a two-dimensional texture feature matrix image by the one or more computer processors, feeding the two-dimensional texture feature matrix into a third convolution neural network to obtain a third decision, computing a hybrid decision based on the first decision, the second decision, and the third decision, and identifying a background type in the background image based on the hybrid decision.

Abstract: A system for assembling a group composite image from individual subject images is described. Often subjects of a group vary in height. Additionally, as each subject is photographed individually, different zoom factors can be applied by a camera that affects a pixel density of the image captured. The system includes a fiducial marking device that emits collimated light to form one or more fiducial markers on a subject while an image is captured by the camera. Based on a location of the fiducial markers in the image, a pixel density of the image and a reference height of the subject can be determined. The individual subject image can be scaled based on the pixel density and reference height to account for the varying subject heights and zoom factors to generate a group composite image that accurately represents the subjects of the group relative to one another.

Abstract: A photography system includes a lighting system configured to provide a lighting scene that is diffused and is artistically desirable. The system includes an array of light panels configured to simulate a wall of windows that receive natural, diffused, and consistent sunlight, such as a large window facing the north. The light panels can operate in various modes of operation, such as a continuous light mode a strobe light mode.

Abstract: A photography system includes a digital camera and a depth and position detection device. The digital camera is configured to capture a digital image of a subject. The depth and position detection device scans the subject and determines three-dimensional locations of the subject and any other objects within the field of view. The depth and position detection device also determines locations of body points of the subject. The depth and position information can then be mapped to the digital image.

Abstract: A computer implemented method for gender classification by applying feature learning and feature engineering to face images. The method includes conducting feature learning on a face image comprising feeding the face image into a first convolution neural network to obtain a first decision, conducting feature engineering on a face image, comprising the steps of automatically detecting facial landmarks in the face image, transforming the facial features into a two-dimensional matrix, and feeding the two-dimensional matrix into a second convolution neural network to obtain a second decision, computing a hybrid decision based on the first decision and the second decision, and classifying gender of the face image in accordance with the hybrid decision.

Abstract: A method and device for generating a composite group image from subgroup images is provided. Subgroup images, each having a common background, are accessed. The boundaries of a subgroup area within each of the subgroup images is determined. At least one horizontal and at least one vertical shift factor is determined using the determined boundaries. An arrangement for the subgroup images based on the at least one horizontal and the at least one vertical shift factor is generated. The composite group image is generated by blending the subgroup images arranged in the arrangement.

Abstract: A method of photographing a subject includes storing a library of photographic scene designs in a computer memory, training a photographic scene detection model by a computer processing device using machine learning from sample portrait images comprising known photographic scenes defined in the library of photographic scene designs, capturing a production portrait photograph, using a digital camera, of a subject in a photographic scene that is defined by a photographic scene design in the library of photographic scene designs, automatically detecting the photographic scene in the production portrait photograph using the photographic scene detection model operating on one or more computer processors, and processing the production portrait photograph by an image processing system to personalize the photographic scene detected in the production portrait photograph.

Abstract: A method for automatically analyzing and constructing communications to a plurality of recipients includes automatically separating communication content files into page groups in a system comprising one or more intelligent communication design servers, wherein each of the page groups is associated a recipient of the communications, inputting the communication content files into an intra-page machine prediction model to produce intra-page parameters, inputting the communication content files and the intra-page parameters into an intra-page machine prediction model to produce intra-group parameters and inter-group parameters, automatically constructing standard communication design files by an intelligent communication content learning and constructing engine based on the communication content files and the intra-page parameters, intra-group parameters, and inter-group parameters, and printing and finishing physical mailing pieces to be mailed to the recipients based on the standard communication design files.

Abstract: A computer-implemented method includes selecting a kernel and kernel parameters for a first Support Vector Machine (SVM) model, testing the first SVM model on a feature matrix T of n feature vectors of length m to produce false positive (FP) data set and false negative (FN) data set by a computer processor, wherein n and m are integer numbers, automatically removing feature vectors corresponding to the FP data set from the feature matrix T by the computer processor to produce a feature matrix T_best, retraining the first SVM model on the feature matrix T_best to produce a second SVM model, and checking if a ratio (T_best sample number)/(SVM support vector number) is above a threshold for the second SVM model on T_best. If the ratio is above the threshold, SVM predictions are performed using the second SVM model on the feature matrix T_best.

Abstract: Devices and methods for evaluating at least one photograph during a photography session are described. In some instances, a camera may be used to capture images during a photography session and a computing device may be used to identify a portrait order specification for the photography session including a set of required photographs, each having associated required criteria. The computing device may evaluate the images and determine whether the images can be associated with any of the required photographs based on features included in the images being associated with the required criteria of the required photographs. The images associated with required photographs may be displayed, and a determination of whether the images qualify as a required photograph may be made using a graphical user interface, or may be made automatically.