Current Issue : January - March Volume : 2017 Issue Number : 1 Articles : 5 Articles
An image signal processor (ISP) for a camera image sensor consists of many complicated functions; in this paper,\na full chain of the ISP functions for smart devices is presented. Each function in the proposed ISP full chain is\ndesigned to handle high-quality images. Every function in the chain is fully converted to a fixed-point arithmetic,\nand a special function is not used for easy porting to a Samsung Reconfigurable Processor (SRP). Several parallelizing\noptimization techniques are applied to the proposed ISP full chain for real-time operation on a given 600-MHz\nreconfigurable processor. To verify the performance of the proposed ISP full chain, a series of tests was performed,\nand all of the measured values satisfy the quality and performance requirements....
Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital\nimages may get degraded. In spite of much advancement in imaging science, captured images do not always fulfill\nusers� expectations of clear and soothing views. Most of the existing methods mainly focus on either global or local\nenhancement that might not be suitable for all types of images. These methods do not consider the nature of the\nimage, whereas different types of degraded images may demand different types of treatments. Hence, we classify\nimages into several classes based on the statistical information of the respective images. Afterwards, an adaptive\ngamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of\nAGC are set dynamically based on the image information. Extensive experiments along with qualitative and\nquantitative evaluations show that the performance of AGC is better than other state-of-the-art techniques....
Assistive technologies aim at improving personal mobility of individuals with disabilities, increasing their\nindependence and their access to social life. They include mechanical mobility aids that are increasingly employed\namongst the older people who rely on them. However, these devices might fail to prevent falls due to the\nunder-estimation of approaching hazards. Stairs and curbs are among these potential dangers present in urban\nenvironments and living accommodations, which increase the risk of an accident. We present and evaluate a\nlow-complexity algorithm to detect descending stairs and curbs of any shape, specifically designed for low-power\nreal-time embedded platforms. Based on a passive stereo camera, as opposed to a 3D active sensor, we assessed the\ndetection accuracy, processing time and power consumption. Our goal being to decide on three possible situations\n(safe, dangerous and potentially unsafe), we achieve to distinguish more than 94 % dangers from safe scenes within a\n91 % overall recognition rate at very low resolution. This is accomplished in real-time with robustness to\nindoor/outdoor lighting conditions. We show that our method can run for a day on a smartphone battery....
We propose a novel and general framework called the multithreading cascade of Speeded Up Robust Features\n(McSURF), which is capable of processing multiple classifications simultaneously and accurately. The proposed\nframework adopts SURF features, but the framework is a multi-class and simultaneous cascade, i.e., a multithreading\ncascade. McSURF is implemented by configuring an area under the receiver operating characteristic (ROC) curve\n(AUC) of the weak SURF classifier for each data category into a real-value lookup list. These non-interfering lists are\nbuilt into thread channels to train the boosting cascade for each data category. This boosting cascade-based\napproach can be trained to fit complex distributions and can simultaneously and robustly process multi-class events.\nThe proposed method takes facial expression recognition as a test case and validates its use on three popular and\nrepresentative public databases: the Extended Cohn-Kanade, MMI Facial Expression Database, and Annotated Facial\nLandmarks in the Wild database. Overall results show that this framework outperforms other state-of-the-art methods....
In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the\nproblem of single-image super-resolution. Dictionaries are designed for a set of clustered data. Data is classified into\ndirectional clusters by correlation criterion. The training data is structured into nine clusters based on correlation\nbetween the data patches and already developed directional templates. The invariance of the sparse representations\nis assumed for the task of super-resolution. For each cluster, a pair of high-resolution and low-resolution dictionaries\nare designed along with their mapping functions. This coupled dictionary learning with a mapping function helps in\nstrengthening the invariance of sparse representation coefficients for different resolution levels. During the\nreconstruction phase, for a given low-resolution patch a set of directional clustered dictionaries are used, and the\ncluster is selected which gives the least sparse representation error. Then, a pair of dictionaries with mapping\nfunctions of that cluster are used for the high-resolution patch approximation. The proposed algorithm is compared\nwith earlier work including the currently top-ranked super-resolution algorithm. By the proposed mechanism, the\nrecovery of directional fine features becomes prominent....
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