Abstract: A method and apparatus for recognizing an image, and a storage medium are provided. The method includes: obtaining labeling results of training data to be labeled in a target training data set; determining a weight coefficient of each labeling result in response to that the training data includes a plurality of labeling results; determining a label of the training data based on labeling result and the weight coefficient corresponding to the labeling result, wherein the training data with the label is the target training data for training a recognition model; and recognizing the image based on the recognition model trained based on the target training data.

Abstract: A apparatus for classifying a seed as inbred or hybrid comprises a terahertz signal source for emitting a terahertz signal towards the seed, a detector for detecting at least part of the terahertz signal having interacted with the seed, a scanner for moving the support relative to the terahertz signal to provide a scan of the seed, a data processing device for forming an image data from the detected terahertz signal as obtained for a plurality of positions during the scan of the seed, and a decision support system for providing a classification from the image data. In an embodiment, the terahertz signal source is arranged for emitting a continuous or pulse wave signal, and wherein the detector is arranged for detecting an amplitude and a phase of the terahertz signal having interacted with the seed. A signal representing an outcome of the classification may control a separator to separate seeds according to their classification.

Abstract: A scent visualization system may comprise a display apparatus for generating a target image including a target object; an olfactory sensor for detecting a scent; and a scent visualization apparatus for generating target associative visualization information that reminds the scent from the target image received from the display apparatus and sensing information received from the olfactory sensor, and generating an associative image by combining the target associative visualization information and the target image. Therefore, a low-cost, high-efficiency, high-utilization, and high-convenience scent visualization system can be provided.

Abstract: A computer-implemented detection method includes, in response to inputting a first image including a region of one or more objects to a learned model, identifying a first entire image corresponding to entirety of a first object as a detection candidate, the learned model being generated by learning training data including an image corresponding to a part of an object and an entire image corresponding to entirety of the object, detecting an existing region of the first target object in the first image in accordance with a comparison between the identified first entire image and the region of the one or more target objects, and determining, based on a specific image obtained by invalidating the existing region in the first image, whether another target object is included in the first image.

Abstract: A method for rendering a non-photorealistic (NPR) content from a set (SI) of at least one image of a same scene is provided. The set of images (SI) is associated with a depth image comprising a set of regions. Each region corresponds to a region of a given depth. The method for rendering a non-photorealistic content includes generation of a segmented image having at least one segmented region generated with a given segmentation scale. The at least one segmented region corresponds to at least one region of the set of regions. A binary edge image is generated in which at least one binary edge region is generated with a given edge extraction scale, the at least one binary edge region corresponding to at least one region of the set of regions. The non-photorealistic content is rendered by combining the segmented image and the binary edge image.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a plurality of remote sources is searched to identify candidate transformation tools relevant for performing data transformations. The candidate transformation tools are analyzed to identify tool examples corresponding with each of the candidate transformation tools. For each of the candidate transformation tools, the tool examples are stored in association with the corresponding candidate transformation tool. Based on a comparison of tool examples with example values, a transformation tool is identified as relevant to facilitate transforming example input values to the desired form in which to transform data.

Abstract: Methods, computer systems, computer-storage media, and graphical user interfaces are provided for facilitating data transformations, according to embodiments of the present invention. In one embodiment, a set of example values is received. An index to identify a plurality of data transformation tools that are relevant to the set of example values is referenced, wherein each of the data transformation tools correspond with one or more tool examples. The data transformation tools are ranked based on an extent of similarity between the set of example values and the tool examples. For data transformation tools associated with the extent of similarity that exceeds a similarity threshold, a transformation program is generated that uses the data transformation tool and a supplemental transformation tool to transform the one or more example input values to the desired form in which to transform data.

Abstract: Methods for detecting anomalies in a geographic area include receiving, from an electronic computing device, expected relationship data indicating expected relationships between a plurality of entities within the geographic area; detecting the plurality of entities within the geographic area; generating observed relationship data indicating observed relationships between the plurality of entities; identifying the expected relationships between the plurality of entities based on the expected relationship data; determining that a given observed relationship between two entities of the plurality of entities is likely to represent an anomaly based on the expected relationship data; and providing an electronic notification to a safety officer, the electronic notification indicating that the given observed relationship is likely to represent the anomaly.

Abstract: An image processing apparatus configured to divide an image into frequency bands and reduce noise, in which a first image includes a high-band frequency component, and a second image includes a low-band frequency component, includes: a first detecting unit configured to detect an edge in at least one of the first image and the second image; a second detecting unit configured to detect a low-contrast edge that is of a lower contrast than a contrast of the edge detected by the first detecting unit in the at least one of the first image and the second image; a compositing unit configured to composite the first image and the second image using a weighting corresponding to the edge and the low-contrast edge.

Abstract: A system and method for determining an optimal solution to an optimization problem in a swarm of candidate solutions is provided. The invention comprises generating a population of random particles, where each particle is representative of a candidate solution. Further, a best particle is identified from the generated population of particles. The best particle is representative of an optimal solution. The population of particles is categorised into similar and non-similar particle groups by applying one or more multivariate measurement techniques, and similarity between the particles of the non-similar particle group with best particle is updated by applying an imitation technique. The generated population is updated with updated particles and a new best particle is evaluated from said population. Furthermore, final best particle is determined by further updating the population of particles until one or more target conditions are achieved.

Abstract: A computer transforms high-dimensional data into low-dimensional data. (A) A distance matrix is computed from observation vectors. (B) A kernel matrix is computed from the distance matrix using a bandwidth value. (C) The kernel matrix is decomposed using an eigen decomposition to define eigenvalues. (D) A predefined number of largest eigenvalues are selected from the eigenvalues. (E) The selected largest eigenvalues are summed. (F) A next bandwidth value is computed based on the summed eigenvalues. (A) through (F) are repeated with the next bandwidth value until a stop criterion is satisfied. Each observation vector of the observation vectors is transformed into a second space using a kernel principal component analysis with the next bandwidth value and the kernel matrix. The second space has a dimension defined by the predefined number of first eigenvalues. Each transformed observation vector is output.

Abstract: The present disclosure relates to training a recommendation model to generate trait recommendations using one permutation hashing and populated-value-slot-based densification. In particular, the disclosed systems can train the recommendation model by computing sketch vectors corresponding to traits using one permutation hashing. The disclosed systems can then fill in unpopulated value slots of the sketch vectors using populated-value-slot-based densification. The disclosed systems can combine the resulting densified sketches to generate the trained recommendation model. For example, in some embodiments, the disclosed systems can combine the sketches by generating a plurality of locality sensitive hashing tables based on the sketches. In some embodiments, the disclosed systems generate a count sketch matrix based on the sketches and generate trait embeddings based on the count sketch matrix using spectral embedding.

Abstract: Methods and systems for digital image segmentation and annotation, including: receiving a digital image depicting, in part, an object of interest from an input file; one or more of manually and automatically adding a polygon around the object of interest to generate a segmented digital image; one or more of manually and automatically appending a label to the polygon around the object of interest to generate a segmented and annotated digital image, wherein the label indicates one or more of an identity and a characteristic of the object of interest; and outputting information related to the segmented and annotated digital image to an output file. Optionally, the polygon is one of a bounding box and a 4-point polygon. Optionally, the object of interest is a parking spot.

Abstract: A computing system accesses an image-based document and a text document having text extracted from the image-based document and provides a user interface displaying at least a portion of the image-based document. In response to selection of a text portion of the image-based document, the system determines an occurrence of the text portion within at least a portion of the image-based document and then applies a search model on the text document to identify the same occurrence of the text portion. Once matched, alignment data indicating a relationship between a selected tag and both the text portion of the image-based document and the text portion of the text document is stored.

Abstract: A system and method for detecting, identifying, and displaying handwriting-based entry is provided. The system and method include features for detecting entry of at least one first letter based on handwriting, identifying a style of the at least one first letter, and displaying at least one second letter associated with the at least one first letter based on, and in the form of, the identified style of the at least one first letter.

Abstract: The presently-disclosed technology improves the resolution of an x-ray microscope so as to obtain super-resolution x-ray images having resolutions beyond the maximum normal resolution of the x-ray microscope. Furthermore, the disclosed technology provides for the rapid generation of the super-resolution x-ray images and so enables real-time super-resolution x-ray imaging for purposes of defect detection, for example. A method of super-resolution x-ray imaging using a super-resolving patch classifier is provided. In addition, a method of training the super-resolving patch classifier is disclosed. Other embodiments, aspects and features are also disclosed.

Abstract: The disclosure relates to a method and an apparatus for analyzing an image using a deep neural net pre-trained for multiple classes. The image is processed by a forward pass through an adapted neural net to generate a processing result. The adapted neural net is adapted from the pre-trained neural net to focus on exactly one selected class. The processing result is then analyzed focused on features corresponding to the selected class using an image processing algorithm. A modified image is generated by removing a manifestation of these features from the image.

Abstract: A recognition apparatus based on a deep neural network, a training apparatus and methods thereof. The deep neural network is obtained by inputting training samples comprising positive samples and negative samples into an input layer of the deep neural network and training. The apparatus includes: a judging unit configured to judge that a sample to be recognized is a suspected abnormal sample when confidences of positive sample classes in a classification result outputted by an output layer of the deep neural network are all less than a predefined threshold value. Hence, reliability of a confidence of a classification result outputted by the deep neural network may be efficiently improved.

Abstract: In an aspect of the present disclosure relates to a network involving humans and AI based systems working in conjunction to perform tasks such as traffic violation detection, infrastructure monitoring, traffic flow management, crop monitoring etc. from visual data acquired from numerous data acquisition sources. The system includes a network of electronic mobile devices with AI capabilities, connected to a decentralized network working towards capturing high quality data for finding events or objects of interest in the real world, retraining AI models, for processing the high volumes of data on decentralized or centralized processing units and also being used for the verification of the outputs of the AI models. The system talks about many annotation techniques on smartphones for crowdsourced AI Data Labeling for AI Training.

Abstract: There is disclosed a method and system to operate a software application entirely based on a unitary lexicon data structure (LDS) comprising a plurality of data field definition blocks stored in memory, with one LDS record for each lexicon term. The LDS is used to develop computerized lexicons and deploy them for use to operate a lexical application with all data displayed for viewing and input by the user on a single screen to which all desired data items come, rather than the user navigating to fields statically located on a multitude of screens. Each LDS record contains a whole set of data in memory, with data duplicated across LDS records in order to bypass the need for the application to interoperate with a database to input and display related data. There is a graphical icon also of a unitary format into which all data is input and displayed. Input data items are related to one another in the icon, regardless of whether a relational database is configured to interoperate with the system.

Abstract: The present disclosure relates to an image analysis method, system, and computer program. The image analysis method of the present disclosure includes: receiving a query image; extracting one or more regions of interest from the query image; calculating a first feature for each of the regions of interest by respectively applying the regions of interest to one or more ROI (region of interest) feature extraction models independently learned in order to extract features of the regions of interest; and calculating analysis values of the query image by applying the first features of the regions of interest to a pre-learned integration analysis model. According to the present disclosure, it is possible to reduce the influence on an analysis model by an error that training data created for map learning of an entire image may have, and it is also possible to increase learning accuracy and objectivity of a deep neural network.

Abstract: Disclosed is a system and method to automatically identify property-related risks through the use of computer vision, sensors, and/or building information models (BIMs). The ability to automatically identify a variety of hazards helps mitigate the associated risks, and thus reduces the number of accidents or fatalities that would otherwise occur. In some embodiments, a “risk map” can be generated by mapping the identified risks for a given property.

Abstract: Techniques for determining image similar are described. For example, a computer-implemented method comprising: receiving a request to determine similarity between a first image and at least one other image; determining similarity between the first image and at least one other image based upon one or more Gram matrix-based style values and one or more vector distance calculation-based content values as determined from one or more outputs of layers of a convolutional neural network; and providing an indication of the similarity of between the first image and the at least one other image is described.

Abstract: Techniques for grammar-based automated generation of annotated synthetic form training data for machine learning are described. A training data generation engine utilizes a defined grammar to construct a layout for a form, select key-value units to place within the layout, and select attribute variants for the key-value units. The form is rendered and stored at a storage location, where it can be provided along with other similarly-generated forms to be used as training data for a machine learning model.

Abstract: A method and device for identifying a main picture in a web page. The method comprises: picking out candidate main pictures based on a page attributes of each picture in a web page (210); cropping an original picture of each candidate main picture to obtain a corresponding picture composition (220); determining a candidate main picture having an information topic matching a topic of the web page (230); and identifying a picture composition corresponding to the matched candidate main picture as the main picture of the web page (240).

Abstract: The invention relates to a control device (1) for a vehicle for determining the perceptual load of a visual and dynamic driving scene. The control device is configured to: receive a sensor output (101) of a sensor (3), the sensor (3) sensing the visual driving scene, extract a set of scene features (102) from the sensor output (101), the set of scene features (102) representing static and/or dynamic information of the visual driving scene, and determine the perceptual load (104) of the set of extracted scene features (102) based on a predetermined load model (103), wherein the load model (103) is predetermined based on reference video scenes each being labelled with a load value The invention further relates to a system and a method.

Abstract: A method for improving the performance of a computer vision system includes obtaining input specifying a task to be performed by the computer vision system; obtaining a first digital image; and comparing the first digital image to at least one training image used to train the computer vision system to solve the task. Further steps include, based on the comparing indicating that the first digital image is insufficiently similar to the at least one training image, recommending at least one adjustment to the digital image; obtaining a second digital image in accordance with the adjustment; and performing the task with the computer vision system based on the second digital image obtained in accordance with the adjustment. Adjustments can be based, for example, on image composition and/or weather conditions.

Abstract: Disclosed are systems and methods for improving interactions with and between computers in content generating, searching, hosting and/or providing systems supported by or configured with personal computing devices, servers and/or platforms. The systems interact to identify and retrieve data within or across platforms, which can be used to improve the quality of data used in processing interactions between or among processors in such systems. The disclosed systems and methods provide systems and methods for automatically detecting and rendering highlights from streaming videos in real-time. As a streaming video is being broadcast over the Internet, the disclosed systems and methods determine each type of scene from the streaming video, and automatically score highlight scenes. The scored highlight scenes are then communicated to users as compiled video segments, which can be over any type of channel or platform accessible to a user's device and network that enables content rendering and user interaction.

Abstract: An image processing apparatus includes a memory and a processor coupled to the memory. The processor is configured to classify each of a plurality of images into one of a plurality of groups based on a feature of each of the plurality of images. The processor is configured to store first information in the memory. A first recognition method is associated with a first group in the first information and images classified into the first group are correctly recognized by the first recognition method. The processor is configured to store second information in the memory. One of at least one second recognition method different from the first recognition method is associated with a second group in the second information and images classified into the second group are incorrectly recognized by the first recognition method.

Abstract: Disclosed are systems and methods for automatic selection of canonical digital images from a large corpus of digital images, such as the corpus of digital images available on the web, for an entity, such as and without limitation a person, a point of interest, object, etc. The automated, unsupervised approach for selecting a diverse set of high quality, canonical digital images, is well suited for processing a large corpus of digital images. A set of canonical digital images identified for an entity can be retrieved in response to a digital image request for digital images depicting the entity.

Abstract: Described is a system for visual activity recognition. In operation, the system detects a set of objects of interest (OI) in video data and determines an object classification for each object in the set of OI, the set including at least one OI. A corresponding activity track is formed for each object in the set of OI by tracking each object across frames. Using a feature extractor, the system determines a corresponding feature in the video data for each OI, which is then used to determine a corresponding initial activity classification for each OI. One or more OI are then detected in each activity track via foveation, with the initial object detection and foveated object detection thereafter being appended into a new detected-objects list. Finally, a final classification is provided for each activity track using the new detected-objects list and filtering the initial activity classification results using contextual logic.

Abstract: Relevance decay techniques are provided for time-based evaluation of machine learning applications and other classifiers. An exemplary method comprises obtaining time series measurement data; generating an input dataset comprising a plurality of records, wherein each record comprises features extracted from the time series measurement data, a target class corresponding to an event to be identified, and a time lag indicating a difference in time between a given extraction and the event to be identified; evaluating a plurality of classifiers during an evaluation phase using a portion of the input dataset and one or more predefined evaluation metrics weighted using a time-based relevance decay function based on the time lag; and selecting one or more of the classifiers to perform classification of the time series measurement data based on the predefined weighted evaluation metrics during a classification phase.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a final classification output for an image of eye tissue. The image is provided as input to each of one or more segmentation neural networks to obtain one or more segmentation maps of the eye tissue in the image. A respective classification input is generated from each of the segmentation maps. For each of the segmentation maps, the classification input for the segmentation map is provided as input to each of one or more classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network. A final classification output for the image is generated from the respective classification outputs for each of the segmentation maps.

Abstract: Methods and systems may provide for facial recognition of at least one input image utilizing hierarchical feature learning and pair-wise classification. Receptive field theory may be used on the input image to generate a pre-processed multi-channel image. Channels in the pre-processed image may be activated based on the amount of feature rich details within the channels. Similarly, local patches may be activated based on the discriminant features within the local patches. Features may be extracted from the local patches and the most discriminant features may be selected in order to perform feature matching on pair sets. The system may utilize patch feature pooling, pair-wise matching, and large-scale training in order to quickly and accurately perform facial recognition at a low cost for both system memory and computation.

Abstract: A method of assisting in selection of a particular mask from among a plurality of masks, the method comprising: generating a plurality of augmented faces, each augmented face corresponding to a respective mask of the plurality of masks; and associating each augmented face with a respective mask of the plurality of masks.

Abstract: A method includes processing an input image using convolution layers to define image features and processing the image features to define feature statistics. Processing the image features includes applying an activation function in a feature dimension of the image features. The method also includes processing the feature statistics using fully connected layers to produce a binary output regarding a characteristic of the input image.

Abstract: In various embodiments, a quality trainer trains a model that computes a value for a perceptual video quality metric for encoded video content. During a pre-training phase, the quality trainer partitions baseline values for metrics that describe baseline encoded video content into partitions based on genre. The quality trainer then performs cross-validation operations on the partitions to optimize hyperparameters associated with the model. Subsequently, during a training phase, the quality trainer performs training operations on the model that includes the optimized hyperparameters based on the baseline values for the metrics to generate a trained model. The trained model accurately tracks the video quality for the baseline encoded video content. Further, because the cross-validation operations minimize any potential overfitting, the trained model accurately and consistently predicts perceived video quality for non-baseline encoded video content across a wide range of genres.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining routing. An exemplary method comprises: inputting a plurality of to-be-optimized routing solution candidates to a Siamese neural network comprising a plurality of value prediction networks, each of the value prediction networks being trained to predict a cost associated with a to-be-optimized routing solution candidate; identifying one or more to-be-optimized routing solution candidates from the plurality of to-be-optimized routing solution candidates based on outputs of the Siamese neural network; inputting the one or more identified to-be-optimized routing solution candidates to a routing optimizer to obtain one or more optimized routing solution candidates; and determining an optimized routing solution with a lowest cost from the one or more optimized routing solution candidates.

Abstract: Implementations are directed to training a machine learning model that, once trained, is used in performance of robotic grasping and/or other manipulation task(s) by a robot. The model can be trained using simulated training examples that are based on simulated data that is based on simulated robot(s) attempting simulated manipulations of various simulated objects. At least portions of the model can also be trained based on real training examples that are based on data from real-world physical robots attempting manipulations of various objects. The simulated training examples can be utilized to train the model to predict an output that can be utilized in a particular task—and the real training examples used to adapt at least a portion of the model to the real-world domain can be tailored to a distinct task. In some implementations, domain-adversarial similarity losses are determined during training, and utilized to regularize at least portion(s) of the model.

Abstract: A system and method for training a computer-implemented object classifier includes detecting a foreground visual object within a sub-region of a scene, determining a background model of the sub-region of the scene, the background model representing the sub-region when any foreground visual object is absent from that sub-region, and training the object classifier by computer-implemented machine learning using the background model of the sub-region as a negative training example.

Abstract: A computerized automated parameterization image pattern detection and classification method performs (1) morphological metrics learning using labeled region data to generate morphological metrics; (2) intensity metrics learning using learning image and labeled region data to generate intensity metrics; and (3) population learning using the morphological metrics and the intensity metrics to generate learned pattern detection parameter. The method may further update the learned pattern detection parameter using additional labeled region data and learning image, and apply pattern detection with optional user parameter adjustment to image data to generate detected pattern. The method may alternatively perform pixel parameter learning and pixel classification to generate pixel class confidence, and uses the pixel class confidence and the labeled region data to perform pattern parameter learning to generate the learned pattern detection parameter.

Abstract: A method and apparatus for removing black eyepits and sunglasses in first actual scenario data having an image containing a face acquired from an actual scenario, to obtain second actual scenario data; counting a proportion of wearing glasses in the second actual scenario data; dividing original training data composed of an image containing a face into wearing-glasses and not-wearing-glasses first and second training data, where a proportion of wearing glasses in the original training data is lower than a proportion in the second actual scenario data; generating wearing-glasses third training data based on glasses data and the second training data; generating fourth training data in which a proportion of wearing glasses is equal to the proportion of wearing glasses in the second actual scenario data, based on the third training data and the original training data; and training a face recognition model based on the fourth training data.

Abstract: A moving object detection apparatus has a generation unit that generates a background model based on a feature of a background region of a captured image that is captured by image capturing unit, a detection unit that detects a moving object region from an image input using an input unit, based on the background model, and a determination unit that determines whether to cause the generation unit to newly generate a background model, based on an amount of change in the moving object region detected by the detection unit for a first image and a second image captured at different times and input using the input unit.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using an image processing neural network system that includes a spatial transformer module. One of the methods includes receiving an input feature map derived from the one or more input images, and applying a spatial transformation to the input feature map to generate a transformed feature map, comprising: processing the input feature map to generate spatial transformation parameters for the spatial transformation, and sampling from the input feature map in accordance with the spatial transformation parameters to generate the transformed feature map.

Abstract: Provided are a method for pushing a picture, a terminal and a storage medium. The method includes the follows. An image containing a facial expression of a user is acquired, when a screen is lit; the image is analyzed to determine an emotion type of the user; and a target picture is pushed according to the emotion type of the user.

Abstract: Techniques are disclosed for improving classification performance in supervised learning. In accordance with some embodiments, a multiclass support vector machine (SVM) having three or more classes may be converted to a plurality of binary problems that then may be reduced via one or more reduced-set methods. The resultant reduced-set (RS) vectors may be combined together in one or more joint lists, along with the original support vectors (SVs) of the different binary classes. Each binary problem may be re-trained using the joint list(s) by applying a reduction factor (RF) parameter to reduce the total quantity of RS vectors. In re-training, different kernel methods can be combined, in accordance with some embodiments. Reduction may be performed until desired classification performance is achieved. The disclosed techniques can be used, for example, to improve classification speed, accuracy, class prioritization, or a combination thereof, in the SVM training phase, in accordance with some embodiments.

Abstract: A system for generating graphical user interfaces. The system may include processors and storage devices storing instructions. The instructions may configure the one or more processors to perform operations including identifying a plurality of attributes from an image captured with a client device, identifying a plurality of first results based on the attributes, generating a first graphical user interface for display in the client device. The first graphical user interface may include a plurality of result icons corresponding to a subset of the first results having confidence scores above a threshold, a plurality of filter icons displaying options, and a search button. The operations may also include receiving a selection of at least one of the result icons or at least one of the filter icons, performing a search, based on the selection, and generating a second graphical user interface.

Abstract: Various arrangements for generating a high definition map are presented. An image of a roadway environment may be captured. A laser imaging detection and ranging point cloud of the roadway environment may be generated. An object recognition process may be performed on the image of the roadway environment to detect one or more objects present in the roadway environment. A fusion process may be performed using the lidar point cloud to identify a location of the one or more detected objects from the image in a vehicle frame of reference. The one or more objects may be mapped from the vehicle frame of reference to a global frame of reference An autonomous driving high definition map may be created that includes the mapped one or more objects in the global frame of reference.

Abstract: Hybrid transcription of audio relies on having one or more layers of transcribers who review transcriptions generated by automatic speech recognition (ASR) systems in order to correct errors that are found in the transcriptions. When it comes to determining how much human reviewing is needed, such as determining how many layers of review to use, there is a cost/benefit tradeoff to consider. Some embodiments described herein utilize a machine learning-based approach for estimating quality of hybrid transcription of audio. In one embodiment, a computer generates a transcription of a segment of audio using an ASR system, which is subsequently reviewed by a transcriber. The computer then calculates, based on properties of the review by the transcriber, a value indicative of an expected accuracy of the reviewed transcription. The computer may suggest a second transcriber review the reviewed transcription if the value indicative of the expected accuracy is below a threshold.

Abstract: A lighting unit for use in low lighting environments such as underground mines is provided. The lighting unit includes a main housing having a front opening and a cavity, a first visual lighting assembly positioned within the cavity of the main housing that emits light in the visible spectrum, and a second ultraviolet lighting assembly positioned a fixed distance from the first visual lighting assembly within the cavity of the main housing that emits light in the ultraviolet spectrum. The lighting unit is part of a system for detecting patterns on objects having reflective or high visibility colored material thereon. In addition to the lighting unit, the system includes a camera having a field of view within which the system is configured to detect patterns of reflective or high visibility colored material on one or more objects, a warning device to alert the operator when a marked object enters the camera's field of view.