Tensor FISTA-Net for Real-Time Snapshot Compressive Imaging

Snapshot compressive imaging (SCI) cameras capture high-speed videos by compressing multiple video frames into a measurement frame. However, reconstructing video frames from the compressed measurement frame is challenging. The existing state-of-the-art reconstruction algorithms suffer from low reconstruction quality or heavy time consumption, making them not suitable for real-time applications. In this paper, exploiting the powerful learning ability of deep neural networks (DNN), we propose a novel Tensor Fast Iterative Shrinkage-Thresholding Algorithm Net (Tensor FISTA-Net) as a decoder for SCI video cameras. Tensor FISTA-Net not only learns the sparsest representation of the video frames through convolution layers, but also reduces the reconstruction time significantly through tensor calculations. Experimental results on synthetic datasets show that the proposed Tensor FISTA-Net achieves average PSNR improvement of 1.63∼3.89dB over the state-of-the-art algorithms. Moreover, Tensor FISTA-Net takes less than 2 seconds running time and 12MB memory footprint, making it practical for real-time IoT applications.

A machine learning joint lidar and radar classification system in urban automotive scenarios

This work presents an approach to classify road users as pedestrians, cyclists or cars using a lidar sensor and a radar sensor. The lidar is used to detect moving road users in the surroundings of the car. A 2-dimensional range-Doppler window, a so called region of interest, of the radar power spectrum centered at the object's position is cut out and fed into a convolutional neural network to be classified. With this approach it is possible to classify multiple moving objects within a single radar measurement frame. The convolutional neural network is trained using data gathered with a test vehicle in real urban scenarios. An overall classification accuracy as high as 0.91 is achieved with this approach. The accuracy can be improved to 0.94 after applying a discrete Bayes filter on top of the classifier.

Digital Aerial Photo Orientation within the Different Algorithm Analysis

Scanning digital aerial images as data source, using PhotoShop software and MapGis software measurement pixel coordinates of the 8frame point on photo , according to the camera parameters file provided fiducial mark on the image plane coordinate system coordinates the theory, using affine transformation method and bilinear transformation method, calculated the interior orientation parameters. Basing on this parameter calculates residual error of frame point coordinate, analyse and compare residual error which obtained by two kinds of measurement frame point pixel coordinates .Research shows that, the directional accuracy by using the MapGis measurement frame point pixel coordinates is higher than that by using PhotoShop measurement;

Establishment and Instrumentation of a Full Scale Laboratory for Thermal and Hygroscopic Investigations of Soil Behavior in Cold Climates

This study presents the establishment and instrumentation of a laboratory for investigating how different soils behave under controlled conditions in cold climates. Ground conditions are extremely important in regards to the building sector. Establishing new infrastructure and buildings require high competence about the ground/soils in order to build robust and long lasting foundations and constructions. In cold climates, soils are frequently exposed to freezing and thawing cycles, and building projects often require additional resources compared to similar projects further south. During 2009-2010, a new laboratory was established in Narvik, Norway. The laboratory consists of 4 different 6x6m bins containing different homogenous soils down to a depth of 3m. A special designed measurement frame has been placed inside each bin, which facilitates instrumentation for thermal and hygroscopic measurements. The laboratory has many applications which may lead to advances within knowledge about thermal response of soils, artificial thawing for more efficient building in cold climates, faster dehydration and curing of concrete during winter, improved road foundations and preventing frost heaves and so on. This study describes the laboratory setup and presents test measurements on thermal responses of sand, silty sand and gravel during artificial thawing using a hydronic thawing system.

DETECTION OF FRAME DUPLICATION FORGERY IN VIDEOS BASED ON SPATIAL AND TEMPORAL ANALYSIS

In this paper, we present a passive-blind scheme for detection of frame duplication forgery in videos. The scheme is a coarse-to-fine approach that is implemented in four stages: candidate segment selection, spatial similarity measurement, frame duplication classification, and post-processing. To screen and select duplicated candidates in the temporal domain, the histogram difference of two adjacent frames in the RGB color space is adopted as a feature. Then, to evaluate the similarity of two images, we use a block-based algorithm to measure the spatial correlation between the candidate segment and the corresponding frame in the query template. Based on the results of spatial and temporal analysis, we construct a classifier to detect duplicated clips. In addition, to deal with the partial detection problem, we develop a post-processing technique that examines and merges two adjacent detected candidates into a complete duplicated video clip. Our experiment results demonstrate that the proposed scheme can not only achieve detection of frame duplication forgery but also accurately detect and localize duplicated clips in different kinds of videos. The results also show that the scheme outperforms an existing method in terms of precision, recall, accuracy, and computation time.