Fuji Xerox Co., Ltd.

Fujifilm Corporation

Fujifilm Business Innovation Corp.

Hideto Oda

Ryota Ozaki

Noriji Kato

Patrick Chiu

Yukio Kumazawa

Chelhwon Kim

Chidansh Amitkumar Bhatt

Qiong Liu

Matthew L Lee

Jian Zhao

Yanxia Zhang

Sumeet Jain

A system that infers and detects how focused a worker is on work tasks, based on physiological parameters collected using wearable and environmental sensors. The system can identify whether a worker is in a focused state of flow, the affective state in which the worker's mind is fully immersed and actively engaged in their task. The system can also identify whether the worker is anxious (where the task demands/difficulty is too high relative the worker's skills) or bored (where the task is too easy). Once the focus state is detected, the information can be used in various applications for optimizing the worker's tasks and work quality such as monitoring task mastery, interventions to improve worker focus (e.g., recommending breaks, reassigning tasks, clarifying goals), identifying potentially lower quality production lots in a manufacturing environment, measuring the effects of skills training, and personal informatics for improving a worker's own productivity.

Systems and methods for physiological sensing for purposes of detecting persons affective focus state for optimizing productivity and work quality

Example implementations described herein are directed to an interface for a worker dispatch system configured to dispatch routes and schedules for workers on a manufacturing floor. Example implementations can involve a system configured to execute a lot ordering and route optimization for each of a plurality of workers; determine rack placement of the plurality of racks and total travel distance for each worker; generate a deployment plan for each worker from the lot ordering, the route optimization, the rack placement, and the total travel distance, the deployment plan involving a pick-up route and a schedule for each worker; provide the deployment plan to an interface configured to receive modifications to the deployment plan; and upon receiving instructions to deploy the deployment plan through the interface, provide the deployment plan to a device of each worker.

Optimized parts pickup list and routes for efficient manufacturing using frequent pattern mining and visualization

Example implementations described herein are directed to systems and methods for skill assessment, such as hand washing compliance in hospitals, or assembling products in factories. Example implementations involve body part tracking (e.g., hands), skeleton tracking and deep neural networks to detect and recognize sub-tasks and to assess the skill on each sub-task. Furthermore, the order of the sub-tasks is checked for correctness. Beyond monitoring individual users, example implementations can be used for analyzing and improving workflow designs with multiple sub-tasks.

Methods for real-time skill assessment of multi-step tasks performed by hand movements using a video camera

A real-time end-to-end system for capturing ink strokes written with ordinary pen and paper using a commodity video camera is described. Compare to traditional camera-based approaches, which typically separate out the pen tip localization and pen up/down motion detection, described is a unified approach that integrates these two steps using a deep neural network. Furthermore, the described system does not require manual initialization to locate the pen tip. A preliminary evaluation demonstrates the effectiveness of the described system on handwriting recognition for English and Japanese phrases.

Systems and methods for real-time end-to-end capturing of ink strokes from video

An image-feature-value calculating unit extracts an image feature value of an image of a cell candidate area. An NRBC discriminating unit uses a pre-trained discriminator to identify whether or not a target cell is shown in the cell candidate area, on the basis of the image feature value of the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discrimination result display unit displays the image of the cell candidate area. When the cell candidate area is identified as an area in which a target cell is shown, a discriminator training unit trains the discriminator by using the image feature value of the image of the cell candidate area as a training sample on the basis of a user input about whether or not a target cell is shown in the cell candidate area.

Image processing apparatus, image processing method, and storage medium identifying cell candidate area

An image processing apparatus includes a number-of-target-cells estimating unit for estimating, on the basis of a feature of a target sample, the number of target cells included in the target sample, and a detection parameter setting unit for setting, on the basis of the estimated number of target cells, a detection parameter regarding a process of detecting target cells in a captured image of the target sample.

Image processing apparatus, non-transitory computer readable medium, and image processing method

An image processing apparatus includes a detection-difficulty determination unit and a detection-parameter setting unit. The detection-difficulty determination unit calculates a degree of detection difficulty indicating a degree of difficulty in detection of a target cell contained in a specimen, based on a test object condition of the specimen. The detection-parameter setting unit sets a detection parameter for the detection of the target cell from a captured image of the specimen, based on the degree of detection difficulty.

Image processing apparatus, storage medium, and image processing method

A detection control device includes a detection target image acquiring unit that acquires an imaged image resulting from imaging a specimen that is to be subjected to the detection of the target cell, a processing target region setting unit that sets a plurality of processing target regions in the imaged image, a processing target region distributing unit that distributes, in accordance with a distribution amount of data to each of a plurality of processing devices that detect the target cell in an image region, the plurality of processing target regions to the plurality of processing devices, and a detection result acquiring unit that acquires, from each of the plurality of processing devices, a result of a process of detecting the target cell in the processing target regions distributed to the respective plurality of processing devices.

Detection control device, detection system, non-transitory storage medium, and detection control method

A device comprises: a nucleus-candidate-region extracting section that extracts, from a captured image obtained by image-capturing a sample piece including a target cell having a nucleus, a nucleus candidate region corresponding to the nucleus; a basic-probability-information acquiring section that acquires, for each of a plurality of determination subject regions determined on the basis of the nucleus candidate region extracted by the nucleus-candidate-region extracting section, basic probability information indicating probability that an image in the determination subject region is an image of the target cell, on the basis of a feature amount of the image of the determination subject region; and a probability-information calculating section that calculates probability information indicating probability that an image in a display subject region corresponding to the nucleus candidate region is the image of the target cell, on the basis of the basic probability information acquired for each of the plurality of determination subject regions.

Image processing device, computer storage medium, and method for detecting and displaying nucleated target cells

A character recognition apparatus includes a stroke extracting unit, a noise-candidate extracting unit, a generating unit, a unit, and a specifying unit. The stroke extracting unit extracts multiple strokes from a recognition target. The noise-candidate extracting unit extracts noise candidates from the strokes. The generating unit generates multiple recognition result candidates obtained by removing at least one of the noise candidates from the recognition target. The unit performs character recognition on the recognition result candidates and obtains recognition scores. The specifying unit uses the recognition scores to specify a most likely recognition result candidate from the recognition result candidates as a recognition result.

Character recognition apparatus, character recognition processing system, and non-transitory computer readable medium

A character recognition apparatus includes an evaluation-value output unit, a generation unit, a learning unit, and a determination unit. The evaluation-value output unit outputs evaluation values for each of different character recognition programs. Each evaluation value indicates a degree to which an inputted character pattern corresponds to each of character codes to be recognized using the character recognition program. The generation unit generates feature information for the character pattern. The feature information includes, as elements, the evaluation values. The learning unit learns classifications for feature information on a character-code-by-character-code basis based on feature information generated for a character pattern for which a character code is specified in advance. The determination unit determines a character code of an unknown character pattern whose character code is unknown, based on which classification among the learned classifications includes feature information generated for the unknown character pattern.

Character recognition apparatus, character recognition method, and computer-readable medium

A handwritten-information processing apparatus includes a handwritten-information acquisition unit and a handwritten-information insertion unit. The handwritten-information acquisition unit acquires a handwritten information item which has been handwritten by a user on a medium having a region inside a writing frame and a region outside the writing frame. The region inside the writing frame includes division regions. The region outside the writing frame is located outside the region inside the writing frame. When the handwritten-information acquisition unit has acquired an insertion mark which is a handwritten information item extending across a boundary between the region inside the writing frame and the region outside the writing frame, the handwritten-information insertion unit inserts, on the basis of the insertion mark, into the region inside the writing frame, a handwritten information item which has been handwritten in the region outside the writing frame.

Handwritten-information processing apparatus, handwritten-information processing method, and non-transitory computer-readable medium

An image processing apparatus includes an obtaining unit, a first extraction unit, a second extraction unit, a setting unit, a filter setting unit, a generating unit, and a determination unit. The obtaining unit obtains a captured image of a test piece including a target cell having a nucleus. The first extraction unit extracts pixels which are candidates for the nucleus from pixels included in the captured image. The second extraction unit extracts a connected-pixel group which is a candidate for the target cell. The setting unit sets rectangular regions in the captured image. The filter setting unit sets a filter including regions. When the filter is disposed in each of the rectangular regions, the generating unit generates an image feature value. The determination unit determines, on the basis of whether the image feature value satisfies a condition, whether the target cell is included in the rectangular region.

Image processing apparatus, image processing method, non-transitory computer-readable medium, and image processing system for detection of target cells using image feature determination

A character recognition apparatus includes an extracting unit extracting a feature point for a line in a handwritten character, first and second generation units, a learning unit, and a determination unit. The first generation unit generates first feature data from feature points for lines including an in-same-character line (first line) and being selected from lines in character-code-specified handwritten characters (known lines). The second generation unit generates second feature data from feature points for lines including an after-character-transition line (second line) and being selected from known lines. The learning unit causes a discriminator to learn classifications for first and second lines based on the first and second feature data. The determination unit determines whether each line in character-code-unknown handwritten characters is a first or second line, based on which classification is determined by the discriminator for feature data for the line.

A character recognition apparatus includes an acquisition unit, a specification unit, a movement unit, and a recognition unit. The acquisition unit acquires data representing a character string. The specification unit specifies an element of a compound character satisfying a predetermined condition for determining the compound character from the character string. The movement unit moves the element of the compound character close to an adjacent character. The recognition unit recognizes a changed character string in which the movement unit has moved the element of the compound character, based on a shape of characters and relevance between adjacent characters.

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Kanagawa, Japan

Hideto Oda