A smart phone senses audio, imagery, and/or other stimulus from a user's environment, and acts autonomously to fulfill inferred or anticipated user desires. In one aspect, the detailed technology concerns phone-based cognition of a scene viewed by the phone's camera. The image processing tasks applied to the scene can be selected from among various alternatives by reference to resource costs, resource constraints, other stimulus information (e.g., audio), task substitutability, etc. The phone can apply more or less resources to an image processing task depending on how successfully the task is proceeding, or based on the user's apparent interest in the task. In some arrangements, data may be referred to the cloud for analysis, or for gleaning. Cognition, and identification of appropriate device response(s), can be aided by collateral information, such as context. A great number of other features and arrangements are also detailed.

Intuitive computing methods and systems

A mobile computing device can comprise a microprocessor, a display, at least one motion sensor, and an imaging device including a two-dimensional image sensor and an imaging lens configured to focus an image of a target object on the image sensor. The mobile computing device can be configured to periodically display a preview image frame of the target object. The mobile computing device can be further configured to compensate for a movement of the imaging device relatively to the target object during a time period elapsed between taking and displaying the preview image frame, by transforming the preview image frame based on the device movement detected by the motion sensor.

Mobile computing device with improved image preview functionality

The present invention relates to a mobile terminal, a display device, and a control method therefor, wherein, when the mobile terminal and the display device are connected to each other, data communication can be performed therebetween while taking into consideration the convenience of a user. According to at least one embodiment of the present invention, when the mobile terminal and the display device are connected to each other so that a monitor window of the screen of the mobile terminal is presented on the display device, the user of the terminal can easily shift the orientation direction of the monitor window by shifting the orientation direction of the housing of the mobile terminal as needed.

Mobile terminal, display device and controlling method thereof

P2012 is an area- and power-efficient many-core computing accelerator based on multiple globally asynchronous, locally synchronous processor clusters. Each cluster features up to 16 processors with independent instruction streams sharing a multi-banked one-cycle access L1 data memory, a multi-channel DMA engine and specialized hardware for synchronization and aggressive power management. P2012 is 3D stacking ready and can be customized to achieve extreme area and energy efficiency by adding domain-specific HW IPs to the cluster. The first P2012 SoC prototype in 28nm CMOS will sample in Q3, featuring four 16-processor clusters, a 1MB L2 memory and delivering 80GOPS (with 32 bit single precision floating point support) in 18mm2 with 2W power consumption (worst-case). P2012 can run standard OpenCL™ and proprietary Native Programming Model SW components to achieve the highest level of control on application-to-resource mapping. A dedicated version of the OpenCV vision library is provided in the P2012 SW Development Kit to enable visual analytics acceleration. This paper will discuss preliminary performance measurements of common feature extraction and tracking algorithms, parallelized on P2012, versus sequential execution on ARM CPUs.

Platform 2012, a many-core computing accelerator for embedded SoCs: performance evaluation of visual analytics applications

The Canny edge detector is one of the most widely used edge detection algorithms due to its superior performance. Unfortunately, not only is it computationally more intensive as compared with other edge detection algorithms, but it also has a higher latency because it is based on frame-level statistics. In this paper, we propose a mechanism to implement the Canny algorithm at the block level without any loss in edge detection performance compared with the original frame-level Canny algorithm. Directly applying the original Canny algorithm at the block-level leads to excessive edges in smooth regions and to loss of significant edges in high-detailed regions since the original Canny computes the high and low thresholds based on the frame-level statistics. To solve this problem, we present a distributed Canny edge detection algorithm that adaptively computes the edge detection thresholds based on the block type and the local distribution of the gradients in the image block. In addition, the new algorithm uses a nonuniform gradient magnitude histogram to compute block-based hysteresis thresholds. The resulting block-based algorithm has a significantly reduced latency and can be easily integrated with other block-based image codecs. It is capable of supporting fast edge detection of images and videos with high resolutions, including full-HD since the latency is now a function of the block size instead of the frame size. In addition, quantitative conformance evaluations and subjective tests show that the edge detection performance of the proposed algorithm is better than the original frame-based algorithm, especially when noise is present in the images. Finally, this algorithm is implemented using a 32 computing engine architecture and is synthesized on the Xilinx Virtex-5 FPGA. The synthesized architecture takes only 0.721 ms (including the SRAM read/write time and the computation time) to detect edges of \(512\times 512\) images in the USC SIPI database when clocked at 100 MHz and is faster than existing FPGA and GPU implementations.

A Distributed Canny Edge Detector: Algorithm and FPGA Implementation

A panoramic picture photographing method permits more accurate photographing of an image, particularly a panoramic image of a subject, without additional equipment, or separate devices and hardware. The method includes projecting an image, which is successively inputted in every predetermined unit of time, to an imaginary surface having a same focal distance in photographing each image constituting a panoramic picture to obtain a projected image; confirming movement of a corresponding photographing apparatus so that the projected image is compared with a previous image in real time by using a motion estimation technique, to which exposure correction has been applied; determining a photographing time point of each image by confirming the confirmed movement of the corresponding photographing apparatus according to a photographing direction has reached a predetermined critical value; and photographing each image at the photographing time point of each image according to a passive input or automatically.

Method for photographing panoramic picture

Disclosed is a method of photographing a panoramic image, the method including the steps of: checking if a configuration of each scene constituting the panoramic image is arbitrarily set whenever each scene is photographed; outputting a photographic guide screen on a preview screen, the photographic guide screen overlappingly displaying a guide image of a previously photographed scene and displaying an indicator indicative of a position of a current scene; storing a corresponding photographed scene as a correspondingly numbered scene when a shutter key is input during the outputting of the photographic guide screen; and checking if a predetermined direction key or a completion key is input, determining that all scenes are photographed, when the completion key is input, and considering the frame to extend in an up-and-down direction or a right-and-left direction and proceeding to the step of outputting the photographic guide screen, when the direction key is input.

Method for photographing panoramic image

The advent of GPUs with programmable shaders on handheld devices has motivated embedded application developers to utilize GPU to offload computationally intensive tasks and relieve the burden from embedded CPU. In this work, we propose an image processing toolkit on handheld GPU with programmable shaders using OpenGL ES 2.0 API. By using the image processing toolkit, we show that a range of image processing algorithms map readily to handheld GPU. We employ real-time video scaling, cartoon-style non-photorealistic rendering, and Harris corner detector as our example applications. In addition, we propose techniques to achieve increased performance with optimized shader design and efficient sharing of GPU workload between vertex and fragment shaders. Performance is evaluated in terms of frames per second at varying video stream resolution.

https://image.inha.ac.kr/paper2/IEICE12Park.pdf

Implementation and optimization of image processing algorithms on handheld GPU

Provided is an apparatus and a method for generating an image including multiple people, in which images are photographed, face images are detected from each of the photographed images, a face score for each of the face images detected from the photographed images is calculated, it is determined whether each of the face scores calculated for each of the face images is at least equal to a predetermined threshold, and each of face images having a highest face score for each of the face images is synthesized, to output the synthesized image as a final image, in order to generate a most satisfactory photographed image for a user.

Apparatus and method for generating image including multiple people

A method for photographing a panoramic picture, and for identifying motion of a photographing apparatus to automatically photograph respective images of a panoramic picture. The method steps include: (a) obtaining a first image in response to a request for a panoramic picture photography; (b) identifying motion of a photographing apparatus having obtained the first image, by applying a motion estimation technique to currently input images; (c) automatically determining a photographing direction based on the identified motion of the photographing apparatus; (d) selectively applying a motion estimation technique for estimating motion in a horizontal direction or a vertical direction, according to the determined photographing direction, and identifying motion of the photographing apparatus; (e) judging photographing time points of respective images by identifying that the identified motion of the photographing apparatus has reached a preset threshold value for decision of the photographing time point; and (f) photographing images at the photographing time point of the respective images.

Method for automatically photographing panoramic picture

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