A mobile terminal and a control method thereof, which can obtain an image, are provided. A mobile terminal (100) includes a camera unit (121), a pictogram extraction unit (182) and a controller (180). The camera unit (121) obtains image information corresponding to at least one of a still image and a moving image. The pictogram extraction unit (182) extracts at least one pictogram from the obtained image information. The controller (180) detects information related to the extracted pictogram, and displays the detected information to be overlapped with the obtained image information. In the mobile terminal, the controller (180) includes, as the detected information, at least one of previously recorded information and currently searched information related to the extracted pictogram.

Mobile terminal and control method thereof

A system for collecting data comprising a mobile terminal for capturing a plurality of frames of image data, the mobile terminal having a first imaging assembly and a second imaging assembly, the first imaging assembly for capturing a first frame of image data representing a first object and the second imaging assembly for capturing a second frame of image data representing a second object, wherein the system for use in collecting data is operative for associating first frame information and second frame information, the first frame information including one or more of image data of the first frame of image data and information derived utilizing the image data of the first frame of image data, the second frame information including one or more of image data of the second frame of image data and information derived utilizing the image data of the second frame of image data.

Apparatus, system, and method of use of imaging assembly on mobile terminal

Multi-scale exposure fusion is an effective image enhancement technique for a high dynamic range (HDR) scene. In this paper, a new multi-scale exposure fusion algorithm is proposed to merge differently exposed low dynamic range (LDR) images by using the weighted guided image filter to smooth the Gaussian pyramids of weight maps for all the LDR images. Details in the brightest and darkest regions of the HDR scene are preserved better by the proposed algorithm without relative brightness change in the fused image. In addition, a new weighted structure tensor is introduced to the differently exposed images and it is adopted to design a detail extraction component for the proposed fusion algorithm, such that users are allowed to manipulate fine details in the enhanced image according to their preference. The proposed multi-scale exposure fusion algorithm is also applied to design a simple single image brightening algorithm for both low-light imaging and back-light imaging.

Detail-Enhanced Multi-Scale Exposure Fusion

Retinal blood vessels segmentation by using Gumbel probability distribution function based matched filter

A combined optical and electrical transmission assembly includes a combined optical, electrical and power cable having an optical fiber, electrical wiring and a power line combined therein or a combined optical and electrical cable having an optical fiber and electrical wiring combined therein, and a combined optical and electrical transmission module that includes an electrical-to-optical conversion unit having a laser for converting electrical signals to optical signals and a driving IC for driving the laser, and/or an optical-to-electrical conversion unit having a photodiode for converting optical signals to electrical signals and an amplification IC for amplifying electrical signals from the photodiode. The combined optical and electrical transmission module is connected to both ends of the combined optical, electrical and power cable or the combined optical and electrical cable. The combined optical and electrical transmission modules are each connected with an electrical cable for transmitting electrical signals with an external device and a power cable for transmitting power. The length of the power cable is shorter than that of the electrical cable.

Combined optical and electrical transmission assembly and module

The inventory inspection using autonomous unmanned aerial vehicles (UAVs) is beneficial in terms of cost, time, and safety of human workers. However, in typical warehouses, it is very challenging for the autonomous UAVs to do inventory task motions safely because aisles are narrow and long, and the illumination is poor. Prior autonomous UAVs are not suitable for such environments, since they suffer from either localization methods prone to disturbance, drift and outliers, or expensive sensors. We present a low-cost sensing system with an extended Kalman filter (EKF)-based multi-sensor fusion framework to achieve practical autonomous navigation of UAVs in warehouse environments. To overcome the inherent drift, outliers, and disturbance problems of naive UAV localization methods, we suggest 1) exploiting component test of Mahalanobis norm to reject outliers efficiently, 2) introducing pseudo-covariance to incorporate a visual SLAM algorithm, and 3) recognizing floor lanes to get absolute information–as robust data fusion methods. Exemplar results are provided to demonstrate the effectiveness of the methods. The proposed system has been successfully implemented for diverse cyclic inventory inspection tasks in a materials warehouse.

Robust Autonomous Navigation of Unmanned Aerial Vehicles (UAVs) for Warehouses’ Inventory Application

In this paper, we present a novel color-to-gray image conversion method which preserves both color and texture discriminabilities effectively. Unlike previous approaches, the proposed method does not require any user-specific parameters for conversion. Moreover, the computational complexity is low enough to be applied to real-time applications. These breakthroughs are achieved by applying the ELSSP (Eigenvalue-weighted Linear Sum of Subspace Projections) method, which is proposed in this paper for the color-to-gray image conversion. Experimental results demonstrate that the proposed method is superior to the state-of-the-art methods in terms of both conversion speed and image quality.

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Novel PCA-based color-to-gray image conversion

A fastening apparatus of a pluggable optical transceiver module, which is coupled to an optical connector and electrically connected to a cage assembly, is disclosed. The fastening apparatus includes a module case extending along a longitudinal direction and at least one rotational latch apparatus positioned on both longitudinal lateral surfaces of the module and adapted to be latched for retaining the module. When the module is mounted along the longitudinal direction of the case, it can be released through a seesawing motion about a rotation axis, which is provided in the interior thereof, while pressing in a direction perpendicular to the longitudinal direction.

Fastening apparatus for a pluggable optical transceiver module

In this paper, we propose a novel background subtraction method which enables reliable detection of foreground objects in a long surveillance video stream. Recently, although much progress has been made in the field of background subtraction, there are still challenging scenarios (e.g., high frequency motion of dynamic texture, non-stationary motion of camera, abrupt changes of illumination, etc.) in the long surveillance videos in which even state-of-the-art methods are often prone to fail. To cope with these challenging scenarios effectively, in the proposed method, a background model is initialized in a low-dimensional subspace and then updated periodically based on a novel recursive on-line ${(2{\rm D})}^2{\rm PCA}$ algorithm developed in this paper. Moreover, a threshold map is also updated in a scene-adaptive manner for labeling each pixel in a scene either foreground or background independently. Based on this on-line framework, the background of a surveillance video stream is reconstructed over time, thereby facilitating the detection of foreground objects reliably. In extensive experiments, we demonstrate that the proposed background subtraction method can cope with the aforementioned challenging scenarios more favorably than the state-of-the-art methods.

Recursive On-Line ${(2{\rm D})}^2{\rm PCA}$ and Its Application to Long-Term Background Subtraction

In this paper, we deal with the problem of background subtraction especially for the scenes containing dynamic textures. In the scenes, unlike static textures, dynamic textures show a wide range of per-pixel color variations over time. For successful dynamic background subtraction, therefore, it is an essential task to represent the dynamics of these variations effectively. For this task, in the proposed method, i) a training set of dynamic background scenes is modeled in a low-dimensional subspace and then ii) the background of a test scene is represented as a linear combination of a few coefficient matrices resulting from the projection of the training scenes onto the low-dimensional subspace. More specifically, the proposed dynamic background subtraction method is based on the sparse representation of dynamic textures in the low-dimensional subspace. In the experiments, the proposed method shows promising performance in comparison with other competitive methods in the literature.

Ja-Won Seo

Papers

Robust Autonomous Navigation of Unmanned Aerial Vehicles (UAVs) for Warehouses’ Inventory Application

Novel PCA-based color-to-gray image conversion

Fastening apparatus for a pluggable optical transceiver module

Recursive On-Line ${(2{\rm D})}^2{\rm PCA}$ and Its Application to Long-Term Background Subtraction

Dynamic background subtraction via sparse representation of dynamic textures in a low-dimensional subspace